Stata Press Publication A Visual Guide To Graphics

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Dedication
Acknowledgments
Preface
Introduction
Twoway graphs
Scatterplot matrix graphs
Bar graphs
Box plots
Dot plots
Pie graphs
Options available for most graphs
Standard options available for all graphs
Styles for changing the look of graphs
Appendix
Subject index

A Visual Guide to Stata Graphics

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published form of the book may be distributed or reproduced, either electronically or in printed form.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

A Visual Guide to Stata Graphics

MICHAEL N. MITCHELL

University of California, Los Angeles

A Stata Press Publication

StataCorp LP

College Station, Texas

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published form of the book may be distributed or reproduced, either electronically or in printed form.

Stata Press, 4905 Lakeway Drive, College Station, Texas 77845

2004 by StataCorp LP

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Printed in the United States of America

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εis a trademark of the American Mathe-

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Dedication

I would like to dedicate this book to Paul Hoﬀman. Although he was my supervisor for

the last nine years, it always felt much more like he was a trusted friend always there to

help me do the best work that I could. I am so sorry he had so leave us so soon. In my own

way, I hope that I can give to others the same kinds of things he gave to me. I am really

going to miss you, Paul.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

Acknowledgments

Although there is a single name on the cover of this book, many people have helped to

make this book possible. Without them, this book would have remained a dream, and I

could have never shared it with you. I want to thank those people who helped that dream

become the book you are now holding.

I want to thank the warm people at Stata, who were very generous in their assistance

and who always ﬁnd a way to be friendly and helpful. In particular, I wish to thank Vince

Wiggins for his generosity of time, insightful advice, boundless enthusiasm, and commitment

to help make this book the best that it could be. I am very grateful to Jeﬀ Pitblado, who

created the L

EX tools that made the layout of this book possible. Without the beneﬁt of

his time and talent, I would still be learning L

EX instead of writing these acknowledgments.

Also, I would like to thank the Stata technical support team, especially Derek Wagner, for

patiently working with me on my numerous questions. I am also very grateful to John

Williams for his thoroughness and alacrity in editing the book and to Chinh Nguyen for his

creative and clever cover design.

I also want to thank, in alphabetical order, Xiao Chen, Phil Ender, Frauke Kreuter, and

Christine Wells for their support and suggestions.

Last, and certainly not least, I would like to thank the teachers who have added to my

life in very special ways. I have been very fortunate to have been touched by many special

teachers, and I will always be grateful for what they kindly gave to me. I want to thank

(in order of appearance) Larry Grossman, Fred Perske, Rosemary Sheridan, Donald Butler,

Jim Torcivia, Richard O’Connell, Linda Fidell, and Jim Sidanius. These teachers all left me

gifts of knowledge and life lessons that help me every day. Even if they do not all remember

me, I will always remember them.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

Contents

Dedication v

Acknowledgments vii

Preface xiii

1 Introduction 1

1.1 Usingthisbook ................................. 1

1.2 Types of Stata graphs .............................. 4

1.3 Schemes ..................................... 14

1.4 Options ...................................... 20

1.5 Buildinggraphs ................................. 29

2 Twoway graphs 35

2.1 Scatterplots ................................... 35

2.2 Regressionﬁtsandsplines ........................... 49

2.3 Regression conﬁdence interval (CI) ﬁts . ................... 50

2.4 Line plots .................................... 54

2.5 Area plots .................................... 61

2.6 Bar plots ..................................... 62

2.7 Range plots ................................... 64

2.8 Distribution plots ................................ 74

2.9 Options ...................................... 82

2.10 Overlaying plots ................................. 87

3 Scatterplot matrix graphs 95

3.1 Marker options ................................. 95

3.2 Controlling axes ................................. 98

3.3 Matrix options .................................. 102

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published form of the book may be distributed or reproduced, either electronically or in printed form.

xContents

3.4 Graphingbygroups............................... 103

4 Bar graphs 107

4.1 Y-variables.................................... 107

4.2 Graphingbarsovergroups........................... 111

4.3 Options for groups, over options ........................ 117

4.4 Controlling the categorical axis ........................ 123

4.5 Controlling legends ............................... 130

4.6 Controlling the y-axis . . . ........................... 143

4.7 Changing the look of bars, lookofbar options ................. 147

4.8 Graphingbygroups............................... 151

5 Box plots 157

5.1 Specifyingvariablesandgroups,yvarsandover ............... 157

5.2 Options for groups, over options ........................ 163

5.3 Controlling the categorical axis ........................ 168

5.4 Controlling legends ............................... 174

5.5 Controlling the y-axis . . . ........................... 179

5.6 Changing the look of boxes, boxlook options ................. 183

5.7 Graphingbygroups............................... 189

6 Dot plots 193

6.1 Specifyingvariablesandgroups,yvarsandover ............... 193

6.2 Options for groups, over options ........................ 198

6.3 Controlling the categorical axis ........................ 202

6.4 Controlling legends ............................... 205

6.5 Controlling the y-axis . . . ........................... 207

6.6 Changing the look of dot rulers, dotlook options ............... 210

6.7 Graphingbygroups............................... 214

7 Pie graphs 217

7.1 Typesofpiegraphs............................... 217

7.2 Sortingpieslices................................. 219

7.3 Changing the look of pie slices, colors, and exploding ............ 221

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published form of the book may be distributed or reproduced, either electronically or in printed form.

Contents xi

7.4 Slicelabels.................................... 224

7.5 Controlling legends ............................... 228

7.6 Graphingbygroups............................... 232

8 Options available for most graphs 235

8.1 Changingthelookofmarkers ......................... 235

8.2 Creating and controlling marker labels . ................... 247

8.3 Connecting points and markers ........................ 250

8.4 Setting and controlling axis titles ....................... 254

8.5 Setting and controlling axis labels ....................... 256

8.6 Controlling axis scales ............................. 265

8.7 Selectinganaxis................................. 269

8.8 Graphingbygroups............................... 272

8.9 Controlling legends ............................... 287

8.10 Adding text to markers and positions . . ................... 299

8.11 More options for text and textboxes . . . ................... 303

9 Standard options available for all graphs 313

9.1 Creating and controlling titles ......................... 313

9.2 Usingschemestocontrolthelookofgraphs ................. 318

9.3 Sizinggraphsandtheirelements........................ 322

9.4 Changing the look of graph regions . . . ................... 324

10 Styles for changing the look of graphs 327

10.1 Angles ...................................... 327

10.2 Colors ...................................... 328

10.3 Clock position .................................. 330

10.4 Compass direction ................................ 331

10.5 Connecting points ................................ 332

10.6 Line patterns .................................. 336

10.7 Line width .................................... 337

10.8 Margins ..................................... 338

10.9 Marker size ................................... 340

10.10 Orientation .................................... 341

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xii Contents

10.11 Marker symbols ................................. 342

10.12 Text size ..................................... 344

11 Appendix 345

11.1 Overview of statistical graph commands, stat graphs ............ 345

11.2 Common options for statistical graphs, stat graph options ......... 352

11.3 Saving and combining graphs, save/redisplay/combine ........... 358

11.4 Putting it all together, more examples .................... 366

11.5 Common mistakes ................................ 376

11.6 Customizing schemes . . . ........................... 379

11.7 Online supplements ............................... 382

Subject index 383

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Preface

It is obvious to say that graphics are a visual medium for communication. This book

takes a visual approach to help you learn about how to use Stata graphics. While you can

read this book in a linear fashion or use the table of contents to ﬁnd what you are seeking, it

is designed to be “thumbed through” and visually scanned. For example, the right margin

of each right page has what I call a Visual Table of Contents to guide you through the

chapters and sections of the book. Generally, each page has three graphs on it, allowing

you to see and compare as many as six graphs at a time on facing pages. For a given graph,

you can see the command that produced it, and next to each graph is some commentary.

But don’t feel compelled to read the commentary; often, it may be suﬃcient just to see the

graph and the command that made it.

This is an informal book and is written in an informal style. As I write this, I picture

myself sitting at the computer with you, and I am showing you examples that illustrate

how to use Stata graphics. The comments are written very much as if we were sitting down

together and I had a couple of points to make about the graph that I thought you might

ﬁnd useful. Sometimes, the comments might seem obvious, but since I am not there to hear

your questions, I hope it is comforting to have the obvious stated just in case there was a

bit of doubt.

While this book does not spend much time discussing the syntax of the graph commands

(since you will be able to infer the rules for yourself after seeing a number of examples),

the Intro : Options (20) section discusses some of the unique ways that options are used in

Stata graph commands and compares them to the way that options are used in other Stata

commands.

I strived to ﬁnd a balance to make this book comprehensive but not overwhelming. As

a result, I have omitted some options I thought would be seldom used. So, just because a

feature is not illustrated in this book, this does not mean that Stata cannot do that task,

and I would refer to [G]graph for more details. I try to include frequent cross-references

to [G]graph; for example, see also [G]axis options. I view this book as a complement to

the Stata Graphics Reference Manual, and I hope that these cross-references will help you

use these two books in a complementary manner. Note that, whenever you see references to

[G]xyz, you can either ﬁnd “xyz” in the Stata Graphics Reference Manual or type whelp

xyz within Stata. The manual and the help have the same information, although the help

may be more up to date and allows hyperlinking to related topics.

Each chapter is broken into a number of sections showing diﬀerent features and options

for the particular kind of graph being discussed in the chapter. The examples illustrate how

these options or features can be used, focusing on examples that isolate these features so you

are not distracted by irrelevant aspects of the Stata command or graph. While this approach

improves the clarity of presentation, it does sacriﬁce some realism since graphs frequently

have many options used together. To address this, there is a section addressing strategies for

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published form of the book may be distributed or reproduced, either electronically or in printed form.

xiv Preface

building up more complicated graphs, Intro : Building graphs (29), and a section giving tips

on creating more complicated graphs, Appendix : More examples (366). These sections are

geared to help you see how you can combine options to make more complex and feature-rich

graphs.

While this book is printed in color, this does not mean that it ignores how to create

monochrome (black & white) graphs. Some of the examples are shown using monochrome

graphs illustrating how you can vary colors using multiple shades of gray and how you

can vary other attributes, such as marker symbol and size, line width, and pattern, and so

forth. I have tried to show options that would appeal to those creating color or monochrome

graphs.

The graphs in this book were created using a set of schemes speciﬁcally created for this

book. Despite diﬀerences in their appearance, all the schemes increase the size of textual

and other elements in the graphs (e.g., titles) to make them more readable, given the small

size of the graphs in this book. You can see more about the schemes in Intro : Schemes

(14) and how to obtain them in Appendix : Online supplements (382). While one purpose of

the diﬀerent schemes is to aid in your visual enjoyment of the book, they are also used to

illustrate the utility of schemes for setting up the look and default settings for your graphs.

See Appendix : Online supplements (382) for information about how you can obtain these

schemes.

Stata has a number of graph commands for producing special-purpose statistical graphs.

Examples include graphs for examining the distributions of variables (e.g., kdensity,pnorm,

or gladder), regression diagnostic plots (e.g., rvfplot or lvr2plot), survival plots (e.g.,

sts or ltable), time series plots (e.g., ac or pac), and ROC plots (e.g., roctab or lsens). To

cover these graphs in enough detail to add something worthwhile would have expanded the

scope and size of this book and detracted from its utility. Instead, I have included a section,

Appendix : Stat graphs (345), that illustrates a number of these kinds of graphs to help you see

the kinds of graphs these commands create. This is followed by Appendix : Stat graph options

(352), which illustrates how you can customize these kinds of graphs using the options

illustrated in this book.

If I may close on a more personal note, writing this book has been very rewarding and

exciting. While writing, I kept thinking about the kind of book you would want to help you

take full advantage of the powerful, but surprisingly easy to use, features of Stata graphics.

I hope you like it!

Simi Valley, California

February 2004

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1 Introduction

This chapter starts oﬀ by telling you a little bit about the organization of this book and

giving you tips to help you use it most eﬀectively. The next section gives a brief overview

of the diﬀerent kinds of Stata graphs we will be examining in this book, followed by an

overview of the diﬀerent kinds of schemes that will be used for showing the graphs in this

book. The fourth section illustrates the structure of options in Stata graph commands. In

a sense, the second to fourth sections of this chapter are a thumbnail preview of the entire

book, showing the types of graphs covered, how you can control their overall look, and the

general structure of options used within those graphs. By contrast, the ﬁnal section is about

the process of creating graphs.

1.1 Using this book

I hope that you are eager to start reading this book but will take just a couple of minutes

to read this section to get some suggestions that will make the book more useful to you.

First of all, there are many ways you might read this book, but perhaps I can suggest

some tips:

•Please consider reading this chapter before reading the other chapters, as it provides

key information that will make the rest of the book more understandable.

•While you might read a traditional book cover to cover, this book has been written

so that the chapters stand on their own. You should feel free to dive into any chapter

or section of any chapter.

•Sometimes you might ﬁnd it useful to visually scan the graphs rather than to read. I

think this is a good way to familiarize yourself with the kinds of features available in

Stata graphs. If a certain feature catches your eye, you can stop and see the command

that made the graph and perhaps even read the text explaining the command.

•Likewise, you might scan a chapter just by looking at the graphs and the part of the

command in red, which is the part of the command we are discussing for that graph.

For example, scanning the chapter on bar charts in this way would quickly familiarize

you with the kinds of features available for bar graphs and show you how to obtain

those features.

As you have probably noticed, the right margin contains what I call the Visual Table

of Contents. I hope you will ﬁnd it a useful tool for quickly ﬁnding the information you

seek. I frequently use the Visual Table of Contents to cross-reference information within

the book. By design, Stata graphs share many features in common. For example, you use

the same kinds of options to control legends across diﬀerent types of graphs. It would be

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

2Chapter 1. Introduction

repetitive to go into detail about legends for bar charts, box plots, and so on. Within each

kind of graph, legends are brieﬂy described and illustrated, but the details are described in

the Options chapter in the section titled Legend. This is cross-referenced in the book by

saying something like “for more details, see Options : Legend (287)”, which indicates that

you should look to the Visual Table of Contents and thumb to the Options chapter and

then to the Legend section, which begins on page 287.

Sometimes it may take an extra cross-reference to get the information you need. Say

that you want to make the ytitle() large for a bar chart, so you ﬁrst consult Bar : Y-axis

(143). This gives you some information about using ytitle(), but then that section refers

you to Options : Axis titles (254), where more details about axis titles are described. This

section then refers you to Options : Textboxes (303) for more complete details about options

you can use to control the display of text. That section shows more details but then refers to

Styles : Textsize (344), where all of the possible text sizes are described. I know this sounds

like a lot of jumping around, but I hope that it feels more like drilling down for additional

detail, that you feel you are in control of the level of detail that you want, and that the

Visual Table of Contents eases the process of getting the additional details.

Most pages of this book have three graphs per page, each graph being composed of

the graph itself, the command that produced it, and some descriptive text. An example is

shown below, followed by some points to note.

graph twoway scatter propval100 ownhome, msymbol(Sh)

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

In this example, we use the msymbol()

(marker symbol) option to make the

symbols large hollow squares; see

Options : Markers (235) for more details.

Note that the msymbol() option is only

useful for the types of graphs that have

marker symbols, and Stata will ignore

this option if you use it with a

command like the graph twoway

histogram command.

Uses allstates.dta & scheme vg s2c

•Note that the command itself is displayed in a typewriter font, and the part of

the command we are discussing (i.e., msymbol(Sh))isinthis color, both in the

command and when referenced in the descriptive text.

•When commands or parts of commands are given in the descriptive text (e.g., graph

twoway histogram), they are displayed in typewriter font.

•Many of the descriptions contain cross-references, for example, Options : Markers (235),

which means to ﬂip to the Options chapter and then to the section Markers. Equiva-

lently, go to page 235.

•The names of some options are shorthand for two or more words that are sometimes

explained; for instance, “we use the msymbol() (marker symbol) option to make ...”.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.1 Using this book 3

•The descriptive text always concludes by telling you the name of the data ﬁle and

scheme used for making the graph. In this case, the data ﬁle was allstates.dta, and the

scheme was vg s2c.scheme. You can read the data ﬁle over the Internet by using the

vguse command, a command added to Stata when you install the online supplements;

see Appendix : Online supplements (382). If you are connected to the Internet, and your

Stata is fully up to date, you can simply type vguse allstates to use that ﬁle over

the Internet, and you can run the graph command shown to create the graph.

If you want your graphs to look like the ones in the book, you can display them using

the same schemes. See Appendix : Online supplements (382) for information about how to

download the schemes used in this book. Once you have downloaded the schemes, you can

then type the following in the Stata Command window:

.set scheme vg s2c

. vguse allstates

. graph twoway scatter propval100 ownhome, msymbol(Sh)

After you issue the set scheme vg s2c command, subsequent graph commands will

show graphs using the vg s2c scheme. If you prefer, you could add the scheme(vg sc2)

option to the graph command to specify the scheme used just for that graph; for example,

. graph twoway scatter propval100 ownhome, msymbol(Sh) scheme(vg s2c)

In general, all commands and options are provided in their complete form. Commands

and options are generally not abbreviated. However, for purposes of typing, you may wish

to use abbreviations. The previous example could have been abbreviated to

. gr tw sc propval100 ownhome, m(Sh)

and even the gr could have been omitted, leaving

. tw sc propval100 ownhome, m(Sh)

The tw could also have been omitted, leaving

. sc propval100 ownhome, m(Sh)

For guidance on appropriate abbreviations, consult [G]graph.

I should note that, while this book is designed for creating graphs in Stata version 8 and

beyond, many of the examples take advantage of numerous enhancements that have been

released as online updates subsequent to the initial version 8 release. As a result, some

features will either look diﬀerent or may not work at all in Stata 8.0 or 8.1. Therefore, it is

very important that your copy of Stata be fully up to date. Please verify that your copy of

Stata is up to date and obtain any free updates; to do this, enter Stata, type

. update query

and follow the instructions. After the update is complete, you can use the help whatsnew

command to learn about the updates you have just received, as well as prior updates

documenting the evolution of Stata. Because Stata sometimes evolves beyond the printed

manual, you might ﬁnd that some commands or options are documented via the online help

but not in your manual. For example, graph twoway tsline was released after the printed

manual and, as of the ﬁrst printing of this book, is only documented via the online help

(help tsline).

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4Chapter 1. Introduction

What if you are using a newer version of Stata than version 8.2? It is possible that, in

the future, Stata may evolve to make the behavior of some of these commands change. If

this happens, you can use the version command to ask Stata to run the graph commands

as though they were run under version 8.2. For example, if you were running Stata version 9

but wanted a graph command to run as though you were running Stata 8.2, you could type

. version 8.2 : graph twoway scatter propval100 ownhome

and the command would be executed as if you were running version 8.2.

This book has a number of associated online resources to complement the book. Ap-

pendix : Online supplements (382) has more information about these online resources and how

to access them. I strongly suggest that you install the online supplements, which make it

easier to run the examples from the book. To install the supplemental programs, schemes,

and help ﬁles, just type from within Stata

. net from http://www.stata-press.com/data/vgsg

. net install vgsg

For an overview of what you have installed, type whelp vgsg within Stata. Then, with the

vguse command, you can use any dataset from the book. Likewise, all the custom schemes

used in the book will be installed into your copy of Stata and can be used to display the

graphs, as described earlier in this section.

1.2 Types of Stata graphs

Stata has a wide variety of graph types. This section introduces the types of graphs

Stata produces and covers twoway plots (including scatterplots, line plots, ﬁt plots, ﬁt plots

with conﬁdence intervals, area plots, bar plots, range plots, and distribution plots), scat-

terplot matrices, bar charts, box plots, dot plots, and pie charts. We will start oﬀ with a

section showing the variety of twoway plots that can be created with graph twoway.For

this introduction, we have combined them into six families of related plots: scatterplots and

ﬁt plots, line plots, area plots, bar plots, range plots, and distribution plots. We will start

by illustrating scatterplots and ﬁt plots.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.2 Types of Stata graphs 5

graph twoway scatter propval100 popden

Here is a basic scatterplot. The variable

propval100 is placed on the y-axis, and

popden is placed on the x-axis. See

Twoway : Scatter (35) for more details

about these kinds of plots.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

twoway scatter propval100 popden

We can start this command with just

twoway, and Stata understands that

this is shorthand for graph twoway.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

twoway lfit propval100 popden

We can make a linear ﬁt line (lfit)

predicting propval100 from popden.

See Twoway : Fit (49) for more

information about these kinds of plots.

Uses allstates.dta & scheme vg s2c

20 40 60 80 100

Fitted values

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6Chapter 1. Introduction

twoway (scatter propval100 popden) (lfit propval100 popden)

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+ Fitted values

Stata allows us to overlay twoway

graphs. In this case, we make a classic

plot showing a scatterplot overlaid with

a ﬁt line using the scatter and lfit

commands. For more details about

overlaying graphs, see

Twoway : Overlaying (87).

Uses allstates.dta & scheme vg s2c

twoway (scatter propval100 popden) (lfit propval100 popden)

(qfit propval100 popden)

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+ Fitted values

Fitted values

The ability to combine twoway plots is

not limited to just overlaying two plots;

we can overlay multiple plots. Here, we

overlay a scatterplot with a linear ﬁt

line (lfit) and a quadratic ﬁt line

(qfit).

Uses allstates.dta & scheme vg s2c

twoway (scatter propval100 popden) (mspline propval100 popden)

(fpfit propval100 popden) (mband propval100 popden)

(lowess propval100 popden)

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+ Median spline

predicted propval100 Median bands

lowess propval100 popden

Stata has other kinds of ﬁt methods in

addition to linear and quadratic ﬁts.

This example includes a median spline

(mspline), fractional polynomial ﬁt

(fpfit), median band (mband), and

lowess (lowess). For more details, see

Twoway : Fit (49).

Uses allstates.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.2 Types of Stata graphs 7

twoway (lfitci propval100 popden) (scatter propval100 popden)

In addition to being able to plot a ﬁt

line, we can also plot a linear ﬁt line

with a conﬁdence interval using the

lfitci command. We also overlay the

linear ﬁt and conﬁdence interval with a

scatterplot. See Twoway : CI ﬁt (50) for

more information about ﬁt lines with

conﬁdence intervals.

Uses allstates.dta & scheme vg s2c

0 50 100 150

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

95% CI Fitted values

% homes cost $100K+

twoway dropline close tradeday

This dropline graph shows the closing

prices of the S&P 500 by trading day

for the ﬁrst 40 days of 2001. A

dropline graph is like a scatter plot

since each data point is shown with a

marker, but a dropline for each marker

is shown as well. For more details, see

Twoway : Scatter (35).

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

twoway spike close tradeday

Here, we use a spike graph to show the

same graph as the previous graph. It is

like the dropline plot, but no markers

are put on the top. For more details,

see Twoway : Scatter (35).

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

8Chapter 1. Introduction

twoway dot close tradeday

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

The dot plot, like the scatter

command, shows markers for each data

point but also adds a dotted line for

each of the x-values. For more details,

see Twoway : Scatter (35).

Uses spjanfeb2001.dta & scheme vg s2c

twoway line close tradeday, sort

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

The line command is used in this

example to make a simple line graph.

See Twoway : Line (54) for more details

about line graphs.

Uses spjanfeb2001.dta & scheme vg s2c

twoway connected close tradeday, sort

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

The twoway connected graph is similar

to twoway line, except that a symbol

is shown for each data point. For more

information, see Twoway : Line (54).

Uses spjanfeb2001.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.2 Types of Stata graphs 9

twoway tsline close, sort

The tsline (time-series line) command

makes a line graph where the x-variable

is a date variable that has previously

been declared using tsset;see

[TS]tsset. This example shows the

closing price of the S&P 500 by trading

date. For more information, see

Twoway : Line (54).

Uses sp2001ts.dta & scheme vg s2c

1000 1100 1200 1300 1400

Closing price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

twoway tsrline high low, sort

This command uses tsrline (time

series range line) to make a line graph

showing the high and low prices of the

S&P 500 by trading date. For more

information, see Twoway : Line (54).

Uses sp2001ts.dta & scheme vg s2c

900 1000 1100 1200 1300 1400

High price/Low price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

twoway area close tradeday, sort

An area plot is similar to a line plot,

but the area under the line is shaded.

See Twoway : Area (61) for more

information about area plots.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

10 Chapter 1. Introduction

twoway bar close tradeday

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Here is an example of a twoway bar

plot. For each x-value, a bar is shown

corresponding to the height of the

y-variable. Note that this shows a

continuous x-variable as compared with

the graph bar command, which would

be useful when we have a categorical

x-variable. See Twoway : Bar (62) for

more details about bar plots.

Uses spjanfeb2001.dta & scheme vg s2c

twoway rarea high low tradeday, sort

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

This example illustrates the use of

rarea (range area) to graph the high

and low prices with the area ﬁlled. If

we used rline (range line), the area

would not be ﬁlled. See Twoway : Range

(64) for more details.

Uses spjanfeb2001.dta & scheme vg s2c

twoway rconnected high low tradeday, sort

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

The rconnected (range connected)

command makes a graph similar to the

previous one, except that a marker is

shown at each value of the x-variable

and the area in between is not ﬁlled. If

we instead used rscatter (range

scatter), the points would not be

connected. See Twoway : Range (64) for

more details.

Uses spjanfeb2001.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.2 Types of Stata graphs 11

twoway rcap high low tradeday, sort

Here, we use rcap (range cap) to graph

the high and low prices with a spike and

a cap at each value of the x-variable. If

you used rspike instead, spikes would

be displayed but not caps. If we used

rcapsym, the caps would be symbols

and you could modify the symbol. See

Twoway : Range (64) for more details.

Uses spjanfeb2001.dta & scheme vg s2c

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rbar high low tradeday, sort

Here, we use the rbar to graph the

high and low prices with bars at each

value of the x-variable. See

Twoway : Range (64) for more details.

Uses spjanfeb2001.dta & scheme vg s2c

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

twoway histogram popk, freq

The twoway histogram command can

be used to show the distribution of a

single variable. It is often useful when

overlaid with other twoway plots;

otherwise, the histogram command

would be preferable. See

Twoway : Distribution (74) for more

details.

Uses allstates.dta & scheme vg s2c

0 10 20 30

Frequency

0 5000 10000 15000 20000

Pop/1,000

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

12 Chapter 1. Introduction

twoway kdensity popk

0 .00005 .0001 .00015

kdensity popk

0 5000 10000 15000 20000 25000

The twoway kdensity command shows

a kernel-density plot and is useful for

examining the distribution of a single

variable. It can be overlaid with other

twoway plots; otherwise, the kdensity

command would be preferable. See

Twoway : Distribution (74) for more

details.

Uses allstates.dta & scheme vg s2c

twoway function y=normden(x), range(-4 4)

0 .1 .2 .3 .4

−4−2 0 2 4

The twoway function command allows

us to graph an arbitrary function over a

range of values we specify. See

Twoway : Distribution (74) for more

details.

Uses allstates.dta & scheme vg s2c

graph matrix propval100 rent700 popden

% homes

cost

$100K+

% rents

$700+/mo

Pop/10

sq.

miles

100

0 50 100

0 20 40

5000

10000

0 5000 10000

We can use the graph matrix

command to show a scatterplot matrix.

See Matrix (95) for more details.

Uses allstates.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.2 Types of Stata graphs 13

graph hbar popk, over(division)

The graph hbar (horizontal bar)

command is often used to show the

values of a continuous variable broken

down by one or more categorical

variables. Note that graph hbar is

merely a rotated version of graph bar.

See Bar (107) for more details.

Uses allstates.dta & scheme vg s2c

0 5,000 10,000 15,000

mean of popk

Pacific

Mountain

W.S.C.

E.S.C.

S. Atl.

W.N.C.

E.N.C.

Mid Atl

N. Eng.

graph hbox popk, over(division)

We can show the previous graph as a

box plot using the graph hbox

(horizontal box) command. The graph

hbox command is commonly used for

showing the distribution of one or more

continuous variables, broken down by

one or more categorical variables. Note

that graph hbox is merely a rotated

version of graph box. See Box (157) for

more details.

Uses allstates.dta & scheme vg s2c 0 5,000 10,000 15,000 20,000

Pop/1,000

Pacific

Mountain

W.S.C.

E.S.C.

S. Atl.

W.N.C.

E.N.C.

Mid Atl

N. Eng.

graph dot popk, over(division)

The previous plot could also be shown

as a dot plot using graph dot.Dot

plots are often used to show one or

more summary statistics for one or

more continuous variables, broken down

by one or more categorical variables.

See Dot (193) for more details.

Uses allstates.dta & scheme vg s2c

0 5,000 10,000 15,000

mean of popk

Pacific

Mountain

W.S.C.

E.S.C.

S. Atl.

W.N.C.

E.N.C.

Mid Atl

N. Eng.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

14 Chapter 1. Introduction

graph pie popk, over(region)

NE N Cntrl

South West

The graph pie command can be used

to show pie charts. See Pie (217) for

more details.

Uses allstates.dta & scheme vg s2c

1.3 Schemes

While the previous section was about the diﬀerent types of graphs Stata can make, this

section is about the diﬀerent kinds of looks that you can have for Stata graphs. The basic

starting point for the look of a graph is a scheme, which controls just about every aspect

of the look of the graph. A scheme sets the stage for the graph, but you can use options

to override the settings in a scheme. As you might surmise, if you choose (or develop) a

scheme that produces graphs similar to the ﬁnal graph you wish to make, you can reduce

the need to customize your graphs using options. Here, we give you a basic ﬂavor of what

schemes can do and introduce you to the schemes you will be seeing throughout the book.

See Intro : Using this book (1) for more details about how to select and use schemes and

Appendix : Online supplements (382) for more information about how to download them.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

1.3 Schemes 15

twoway scatter propval100 rent700 ownhome

This scatterplot illustrates the vg s1c

scheme. It is based on the s1color

scheme but increases the sizes of

elements in the graph to make them

more readable. This scheme is in black

and white and has a white background

in the plot region but is light gray in

the surrounding graph region.

Uses nlsw.dta & scheme vg s2m

graph hbar wage, over(occ7, label(nolabels)) blabel(group, position(base))

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

0 2 4 6 8 10

mean of wage

This horizontal bar chart shows an

example of the vg palec scheme. It is

basedonthes2color scheme but

makes the colors of the

bars/boxes/markers paler by decreasing

the intensity of the colors. As shown in

this example, one use of this scheme is

to make the colors of the bars pale

enough to include text labels inside of

bars.

Uses nlsw.dta & scheme vg palec

graph hbar wage, over(occ7, label(nolabels)) blabel(group, position(base))

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

0 2 4 6 8 10

mean of wage

This example is the same as the last

example but uses the vg palem scheme,

the monochrome equivalent of the

vg palec scheme. This scheme is based

on the s2mono scheme but makes the

colors of the bars/boxes/markers paler

by decreasing the intensity of the

colors.

Uses nlsw.dta & scheme vg palem

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

1.3 Schemes 17

scatter propval100 rent700 ownhome

This scatterplot illustrates the vg outc

scheme. It is based on the s2color

scheme but makes the ﬁll color of the

bars/boxes/markers white, so they

appear hollow. The plot region is a

light blue to contrast with the white ﬁll

color. In this case, this scheme is useful

to help us see number of markers

present where numerous markers are

close or partially overlapping.

Uses allstates.dta & scheme vg outc

0 20 40 60 80 100

40 50 60 70 80

% who own home

% homes cost $100K+ % rents $700+/mo

scatter propval100 rent700 ownhome

This example is similar to the previous

one but illustrates the vg outm scheme,

the monochrome equivalent of the

vg outc scheme. It is based on the

s2mono scheme but makes the ﬁll color

of the bars/boxes/markers white, so

they appear hollow.

Uses allstates.dta & scheme vg outm

0 20 40 60 80 100

40 50 60 70 80

% who own home

% homes cost $100K+ % rents $700+/mo

twoway (scatter ownhome borninstate if stateab=="DC", mlabel(stateab))

(scatter ownhome borninstate), legend(off)

This is an example of the vg samec

scheme, based on s2color, and makes

all of the markers, lines, bars, etc., the

same color, shape, and pattern. Here,

the second scatter command labels

Washington, DC, which normally would

be shown in a diﬀerent color, but with

this scheme, the marker is the same.

This scheme has a monochrome

equivalent called vg samem that is not

illustrated.

Uses allstates.dta & scheme vg samec

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

18 Chapter 1. Introduction

graph hbar commute, over(division) asyvar

0 5 10 15 20 25

mean of commute

N. Eng.

Mid Atl

E.N.C.

W.N.C.

S. Atl.

E.S.C.

W.S.C.

Mountain

Pacific

This horizontal bar chart shows an

example of the vg lgndc scheme. It is

basedonthes2color scheme but

changes the default attributes of the

legend, namely, showing the legend in

one column to the left of the plot

region, with the key and symbols

placed atop each other. This can be an

eﬃcient way to place the legend to the

left of the graph. There is also a

vg lgndm scheme, which is monochrome

and is not illustrated here.

Uses allstates.dta & scheme vg lgndc

graph bar commute, over(division) asyvar legend(rows(3))

0 5 10 15 20 25

mean of commute

N. Eng. Mid Atl E.N.C.

W.N.C. S. Atl. E.S.C.

W.S.C. Mountain Pacific

This bar chart shows an example of the

vg past scheme. It is based on the

s2color scheme but selects subdued

pastel colors and provides a sand

background for the surrounding graph

region and an eggshell color for the

inner plot region and legend area.

Uses allstates.dta & scheme vg past

twoway scatter rent700 propval100

% rents $700+/mo

0 20 40 60 80 100

% homes cost $100K+

This bar chart shows an example of the

vg rose scheme. It is based on the

s2color scheme but uses a diﬀerent set

of colors, having an eggshell

background and a light rose color for

the plot area. The grid lines are

omitted by default, and the labels for

the y-axis are horizontal by default.

Uses allstates.dta & scheme vg rose

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.3 Schemes 19

graph bar commute, over(division) asyvar legend(rows(3))

This bar chart shows an example of the

vg blue scheme. It is based on the

s2color scheme but uses a set of blue

colors, with a light blue background

and a light blue-gray color for the plot

area. The grid lines are omitted by

default, and the labels for the y-axis are

horizontal by default.

Uses allstates.dta & scheme vg blue 0

mean of commute

N. Eng. Mid Atl E.N.C.

W.N.C. S. Atl. E.S.C.

W.S.C. Mountain Pacific

graph bar commute, over(division) asyvar legend(rows(3))

This is an example using the vg teal

scheme. This scheme is also based on

the s2color scheme but uses an

olive–teal background. It also

suppresses the display of grid lines and

makes the labels for the y-axis display

horizontally by default.

Uses allstates.dta & scheme vg teal

mean of commute

N. Eng. Mid Atl E.N.C.

W.N.C. S. Atl. E.S.C.

W.S.C. Mountain Pacific

graph bar commute, over(division) asyvar legend(rows(3))

This bar chart shows an example of the

vg brite scheme. It is based on the

s2color scheme but selects a bright set

of colors and changes the background

to light khaki.

Uses allstates.dta & scheme vg brite

0 5 10 15 20 25

mean of commute

N. Eng. Mid Atl E.N.C.

W.N.C. S. Atl. E.S.C.

W.S.C. Mountain Pacific

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

20 Chapter 1. Introduction

This section has just scratched the surface of all there is to know about schemes in Stata,

but I hope that it helps you see how schemes create a starting point for your graph and

that, by choosing a scheme that is most similar to the look you want, you can save time

and eﬀort in customizing your graphs.

1.4 Options

Learning to create eﬀective Stata graphs is ultimately about using options to customize

the look of a graph until you are pleased with it. This section illustrates the general rules

and syntax for Stata graph commands, starting with their general structure, followed by

illustrations showing how options work in the same way across diﬀerent kinds of commands.

Stata graph options work much like other options in Stata; however, there are additional

features that extend their power and functionality. While we will use the twoway scatter

command for illustration, most of the principles illustrated extend to all kinds of Stata

graph commands.

twoway scatter propval100 rent700

0 20 40 60 80 100

% homes cost $100K+

0 10 20 30 40

% rents $700+/mo

Consider this basic scatterplot. To add

a title to this graph, we can use the

title() option as illustrated in the

next example.

Uses allstates.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.4 Options 21

twoway scatter propval100 rent700,

title("This is a title for the graph")

Just as with any Stata command, the

title() option comes after a comma,

and in this case, it contains a quoted

string that becomes the title of the

graph.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

0 10 20 30 40

% rents $700+/mo

This is a title for the graph

twoway scatter propval100 rent700,

title("This is a title for the graph", box)

Starting with Stata 8, options can have

options of their own. Let’s put a box

around the title of the graph. We can

use title(, box), placing box as an

option within title(). If the default

for the current scheme had included a

box, then we could have used the nobox

option to suppress it.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

0 10 20 30 40

% rents $700+/mo

This is a title for the graph

twoway scatter propval100 rent700,

title("This is a title for the graph", box size(small))

Let’s take the last graph and modify

the title to make it small. We can add

another option to the title() option

by adding the size(small) option.

Here, we see that one of the options is a

keyword (box) and that another option

allows us to supply a value

(size(small)).

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

0 10 20 30 40

% rents $700+/mo

This is a title for the graph

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Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

22 Chapter 1. Introduction

twoway scatter propval100 rent700,

title("This is a title for the graph", box size(small))

msymbol(S)

0 20 40 60 80 100

% homes cost $100K+

0 10 20 30 40

% rents $700+/mo

This is a title for the graph Say that we want the symbols to be

displayed as squares. We can add

another option called msymbol(S) to

indicate that we want the marker

symbol to be displayed as a square (S

for square). Adding one option at a

time is a common way to build a Stata

graph. In the next graph, we will

change gears and start building a new

graph to show other aspects of options.

Uses allstates.dta & scheme vg s2c

twoway scatter propval100 rent700

0 20 40 60 80 100

% homes cost $100K+

0 10 20 30 40

% rents $700+/mo

Let’s return to this simple scatterplot.

Say that we want the labels for the

x-axistochangefrom010203040to

0 5 10 15 20 25 30 35 40.

Uses allstates.dta & scheme vg s2c

twoway scatter propval100 rent700, xlabel(0(5)40)

0 20 40 60 80 100

% homes cost $100K+

0 5 10 15 20 25 30 35 40

% rents $700+/mo

Here, we add the xlabel() option to

label the x-axis from 0 to 40,

incrementing by 5. But say that we

want the labels to be displayed larger.

Uses allstates.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

1.4 Options 23

twoway scatter propval100 rent700, xlabel(0(5)40, labsize(huge))

Here, we add the labsize() (label size)

option to increase the size of the labels

for the x-axis. Say that we were happy

with the original numbering (0 10 20 30

40) but wanted the labels to be huge.

How would we do that?

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

0 5 10 15 20 25 30 35 40

% rents $700+/mo

twoway scatter propval100 rent700, xlabel(, labsize(huge))

The xlabel() option we use here

indicates that we are content with the

numbers chosen for the label of the

x-axis because we have nothing before

the comma. After the comma, we add

the labsize() option to increase the

size of the labels for the x-axis.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

0 10 20 30 40

% rents $700+/mo

Let’s consider some examples using the legend() option to show that some options do

not require or permit the use of commas within them. Also, this allows us to show a case

where you might properly specify an option over and over again.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

24 Chapter 1. Introduction

twoway scatter propval100 rent700 popden

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+ % rents $700+/mo

Here, we show two y-variables,

propval100 and rent700, graphed

against population density, popden.

Note that Stata has created a legend,

helping us see which symbols

correspond to which variables. We can

use the legend() option to customize

it.

Uses allstates.dta & scheme vg s2c

twoway scatter propval100 rent700 popden, legend(cols(1))

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+

% rents $700+/mo

Using the legend(cols(1)) option, we

make the legend display in a single

column. Note that we did not use a

comma because, with the legend()

option, there is no natural default

argument. If we had included a comma

within the legend() option, Stata

would have reported this as an error.

Uses allstates.dta & scheme vg s2c

twoway scatter propval100 rent700 popden,

legend(cols(1) label(1 "Property Value"))

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

Property Value

% rents $700+/mo

This example adds another option

within the legend() option, label(),

which changes the label for the ﬁrst

variable.

Uses allstates.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

1.4 Options 25

twoway scatter propval100 rent700 popden,

legend(cols(1) label(1 "Property Value") label(2 "Rent"))

Here, we add another label() option

for the legend() option, but in this

case, we change the label for the second

variable. Note that we can use the

label() option repeatedly to change

the label for the diﬀerent variables.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

Property Value

Rent

Finally, let’s consider an example that shows how to use the twoway command to over-

lay two plots, how each graph can have its own options, and how options can apply to the

overall graph.

twoway (scatter propval100 popden)

(lfit propval100 popden)

Consider this graph, which shows a

scatterplot predicting property value

from population density and shows a

linear ﬁt between these two variables.

Say that we wanted to change the

symbol displayed in the scatterplot and

the thickness of the line for the linear

ﬁt.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+ Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

26 Chapter 1. Introduction

twoway (scatter propval100 popden, msymbol(S))

(lfit propval100 popden, clwidth(vthick))

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+ Fitted values

Note that we add the msymbol() option

to the scatter command to change the

symbol to a square, and we add the

clwidth() (connect line width) option

to the lfit command to make the line

very thick. When we overlay two plots,

each plot can have its own options that

operate on its respective parts of the

graph. However, some parts of the

graph are shared, for example, the title.

Uses allstates.dta & scheme vg s2c

twoway (scatter propval100 popden, msymbol(S))

(lfit propval100 popden, clwidth(vthick)),

title("This is the title of the graph")

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+ Fitted values

This is the title of the graph Note that we add the title() option

to the very end of the command placed

after a comma. That ﬁnal comma

signals that options concerning the

overall graph are to follow, in this case,

the title() option.

Uses allstates.dta & scheme vg s2c

One of the beauties of Stata graph commands is the way that diﬀerent graph commands

share common options. If we want to customize the display of a legend, we do it using the

same options, whether we are using a bar graph, a box plot, a scatterplot, or any other

kind of Stata graph. Once we learn how to control legends with one type of graph, we have

learned how to control legends for all types of graphs. Let’s look at a couple of examples.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

1.4 Options 27

twoway scatter propval100 rent700 popden, legend(position(1))

Consider this scatterplot. We have

added a legend() option to make the

legend display in the one o’clock

position on the graph, putting the

legend in the top right corner.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

0 2000 4000 6000 8000 10000

Pop/10 sq. miles

% homes cost $100K+ % rents $700+/mo

graph bar propval100 rent700, over(nsw) legend(position(1))

Here, we use the graph bar command,

which is a completely diﬀerent

command from the previous one. Even

though the graphs are diﬀerent, the

legend() option we supply is the same

and has the same eﬀect. Many (but not

all) options function in this way,

sharing a common syntax and having

common eﬀects.

Uses allstates.dta & scheme vg s2c

0 10 20 30 40

North South West

mean of propval100 mean of rent700

graph matrix propval100 rent700 popden, legend(position(1))

Contrast this example with the previous

two. The graph matrix command does

not support the legend() option

because this graph does not need or

produce legends. In the Matrix (95)

chapter, for example, there are no

references to legends, an indication that

this is not a relevant option for this

kind of graph. Note that, even though

we included this additional irrelevant

option, Stata ignored it and produced

an appropriate graph anyway.

Uses allstates.dta & scheme vg s2c

% homes

cost

$100K+

% rents

$700+/mo

Pop/10

sq.

miles

100

0 50 100

0 20 40

5000

10000

0 5000 10000

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

28 Chapter 1. Introduction

Because legends work the same way with diﬀerent types of Stata graph commands, we

can save pages by describing legends in detail in one place: Options : Legend (287). However,

it is useful to see examples of legends for each type of graph that uses them. Each chapter,

therefore, provides a brief section describing legends for each type of graph discussed in

that chapter. Likewise, most options are described in detail in Options (235) with a brief

section in every chapter discussing how each option works in speciﬁc types of graphs. As

we saw in the case of legends, some options are not appropriate for some types of graphs,

so those options will not be discussed with the commands that do not support them.

While an option like legend() can be used with many, but not all, kinds of Stata graph

commands, other kinds of options can be used with almost every kind of Stata graph.

These are called Standard Options. To help you diﬀerentiate these kinds of options, they

are discussed in their own chapter, Standard options (313). Since these options can be used

with most types of graph commands, they are generally not discussed in the chapters about

the diﬀerent types of graphs, except when their usage interacts with the options illustrated.

For example, subtitle() is a Standard Option, but its behavior takes on a special meaning

when used with the legend() option, so the subtitle() option is discussed in the context of

legends. Consistent with what we have seen before, the syntax of Standard Options follows

the same kinds of rules we have illustrated, and their usage and behavior are uniform across

the many types of Stata graph commands.

To recap, this section was not about any particular options, but about some of the rules

for using these options and how they behave. Some options permit options. In some cases,

you may want to specify only options. Some options allow you to include one or more

options, but no comma is required. When you overlay multiple graphs using twoway,you

may have options that go along with each graph, as well as overall options that appear at

the end of the command. Finally, the syntax of a certain option is the same across the

diﬀerent graph commands that use the options, but not all options are useful for all kinds

of graph commands.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

1.5 Building graphs 29

1.5 Building graphs

I have three agendas in writing this section. First, I will show the process of building

complex graphs a little bit at a time. At the same time, I illustrate how to use the resources

of this book to get the bits of information needed to build these graphs. Finally, I show that,

even though a complete Stata graph command might look complicated and overwhelming,

the process of building it slowly is actually very straightforward and logical. Let’s ﬁrst build

a bar chart that looks at property values broken down by region of the country. Then, we

will modify the legend and bar characteristics, add titles, and so forth.

graph display

Say that we want to create this graph.

For now, the syntax is concealed, just

showing the graph display command

to show the previously drawn graph. It

might be overwhelming at ﬁrst to

determine all of the options needed to

make this graph. To ease our task, we

will build it one bit at a time, reﬁning

the graph and ﬁxing any problems we

ﬁnd.

Uses allstates.dta & scheme vg past

66.57

53.00

18.46

10.01

28.91

11.60 11.35

19.85

52.46

% homes over $100K

N. Eng.

Mid Atl

E.N.C.

W.N.C.

S. Atl.

E.S.C.

W.S.C.

Mountain

Pacific

Region

North South West

graph bar propval100, over(nsw) over(division)

We begin by seeing that this is a bar

chart and look at Bar : Y-variables (107)

and Bar : Over (111). We take our ﬁrst

step towards making this graph by

making a bar chart showing

propval100 and adding over(nsw) and

over(division) to break the means

down by nsw and division.

Uses allstates.dta & scheme vg past

0 20 40 60 80

mean of propval100

N. Eng.Mid AtlE.N.C.W.N.C.S. Atl.E.S.C.W.S.C.MountainPacific

NorthSouthWestNorthSouthWestNorthSouthWestNorthSouthWestNorthSouthWestNorthSouthWestNorthSouthWestNorthSouthWestNorthSouthWest

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

30 Chapter 1. Introduction

graph bar propval100, over(nsw) over(division) nofill

0 20 40 60 80

mean of propval100

N. Eng.Mid AtlE.N.C.W.N.C.S. Atl.E.S.C.W.S.C.MountainPacific

North North North North South South South West West

The previous graph is not quite what

we want because we see every division

shown with every nsw, but for example,

the Paciﬁc region only appears in the

West. In Bar : Over (111), we see that

we can add the nofill option to show

only the combinations of nsw and

division that exist in the data ﬁle.

Next, we will look at the colors of the

bars.

Uses allstates.dta & scheme vg past

graph bar propval100, over(nsw) over(division) nofill asyvars

0 20 40 60 80

mean of propval100

N. Eng. Mid Atl E.N.C. W.N.C. S. Atl. E.S.C. W.S.C.MountainPacific

North South

West

The last graph is getting closer, but we

want the bars for North, South, and

West to be displayed in diﬀerent colors

and labeled with a legend. In

Bar : Y-variables (107), we see that the

asyvars option will accomplish this.

Next, we will change the title for the

y-axis.

Uses allstates.dta & scheme vg past

graph bar propval100, over(nsw) over(division) nofill asyvars

ytitle("% homes over $100K")

0 20 40 60 80

% homes over $100K

N. Eng. Mid Atl E.N.C. W.N.C. S. Atl. E.S.C. W.S.C.MountainPacific

North South

West

Now, we want to put a title on the

y-axis. In Bar : Y-axis (143), we see

examples illustrating the use of

ytitle() for putting a title on the

y-axis. Here, we put a title on the

y-axis, but now we want to change the

labels for the y-axis to go from 0 to 80,

incrementing by 10.

Uses allstates.dta & scheme vg past

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

1.5 Building graphs 31

graph bar propval100, over(nsw) over(division) nofill asyvars

ytitle("% homes over $100K") ylabel(0(10)80, angle(0))

The Bar : Y-axis (143) section also tells

us about the ylabel() option. In

addition to changing the labels, we also

want to change the angle of the labels,

and in that section, we see that we can

use the angle() option to change the

angle of the labels. Now that we have

the y-axis labeled as we wish, let’s next

look at the title for the x-axis.

Uses allstates.dta & scheme vg past 0

% homes over $100K

N. Eng. Mid Atl E.N.C. W.N.C. S. Atl. E.S.C. W.S.C.MountainPacific

North South

West

graph bar propval100, over(nsw) over(division) nofill asyvars

ytitle("% homes over $100K") ylabel(0(10)80, angle(0)) b1title(Region)

After having used the ytitle() option

to label the y-axis, we might be

tempted to use the xtitle() option to

label the x-axis, but this axis is a

categorical variable. In Bar : Cat axis

(123), we see that this axis is treated

quite diﬀerently because of that. To

put a title below the graph, we use the

b1title() option. Now, let’s turn our

attention to formatting the legend.

Uses allstates.dta & scheme vg past

% homes over $100K

N. Eng. Mid Atl E.N.C. W.N.C. S. Atl. E.S.C. W.S.C.MountainPacific

Region

North South

West

graph bar propval100, over(nsw) over(division) nofill asyvars

ytitle("% homes over $100K") ylabel(0(10)80, angle(0)) b1title(Region)

legend(rows(1) position(1) ring(0))

Here, we want to use the legend()

option to make the legend have one row

in the top right corner within the plot

area. In Bar : Legend (130), we see that

the rows(1) option makes the legend

appear in one row and that the

position(1) option puts the legend in

the 1 o’clock position. The ring(0)

option puts the legend inside the plot

region. Next, let’s label the bars.

Uses allstates.dta & scheme vg past 0

% homes over $100K

N. Eng. Mid Atl E.N.C. W.N.C. S. Atl. E.S.C. W.S.C.MountainPacific

Region

North South West

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

32 Chapter 1. Introduction

graph bar propval100, over(nsw) over(division) nofill asyvars

ytitle("% homes over $100K") ylabel(0(10)80, angle(0)) b1title(Region)

legend(rows(1) position(1) ring(0)) blabel(bar)

66.5667

18.46

10.0143

28.9125

11.6 11.35

19.85

52.46

% homes over $100K

N. Eng. Mid Atl E.N.C. W.N.C. S. Atl. E.S.C. W.S.C.MountainPacific

Region

North South West

We want each bar to be labeled with

the height of the bar, and Bar : Legend

(130) shows how we can do this. This

section shows how to use the blabel()

(bar label) option to label the bars in

lieu of legends. blabel() also can label

the bars with their height, using

blabel(bar).

Uses allstates.dta & scheme vg past

graph bar propval100, over(nsw) over(division) nofill asyvars

ytitle("% homes over $100K") ylabel(0(10)80, angle(0)) b1title(Region)

legend(rows(1) position(1) ring(0)) blabel(bar, format(%4.2f))

66.57

53.00

18.46

10.01

28.91

11.60 11.35

19.85

52.46

% homes over $100K

N. Eng. Mid Atl E.N.C. W.N.C. S. Atl. E.S.C. W.S.C.MountainPacific

Region

North South West

We want the labels for each bar to end

in two decimal places, and we see in

Bar : Legend (130) that we can use the

format() option to format these

numbersaswewish.

Uses allstates.dta & scheme vg past

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

1.5 Building graphs 33

graph bar propval100, over(nsw) over(division, label(angle(45))) nofill

ytitle("% homes over $100K") ylabel(0(10)80, angle(0)) b1title(Region)

legend(rows(1) position(1) ring(0)) blabel(bar, format(%4.2f)) asyvars

Finally, in Bar : Cat axis (123), we see

that we can add the label(angle(45))

option to the over() option to specify

that labels for that variable be shown

at a 45-degree angle so they do not

overlap each other.

Uses allstates.dta & scheme vg past

66.57

53.00

18.46

10.01

28.91

11.60 11.35

19.85

52.46

% homes over $100K

N. Eng.

Mid Atl

E.N.C.

W.N.C.

S. Atl.

E.S.C.

W.S.C.

Mountain

Pacific

Region

North South West

This section has shown that it is not that diﬃcult to create a complex graph by building

it one step at a time. You can use the resources in this book to seek out each piece of

information you need and then put those pieces together the way you want to create your

own graphs. For more information about how to integrate options to create complex Stata

graphs, see Appendix : More examples (366).

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Using this book Types of Stata graphs Schemes Options Building graphs

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2 Twoway graphs

The graph twoway command represents not just a single kind of graph but actually

over thirty diﬀerent kinds of graphs. Many of these graphs are similar in appearance and

function, so I have grouped them into eight families, which form the ﬁrst eight sections

of this chapter. These ﬁrst eight sections, which cover scatterplots to distribution plots,

cover the general features of these plots and brieﬂy mention some important options. These

are followed by a section giving an overview of the options that can be used with twoway

graphs. (For further details about the options that can be used with twoway graphs, see

Options (235) and the sections within that chapter.) The chapter concludes with a section

illustrating how you can overlay twoway graphs. For more details about graph twoway,see

[G]graph twoway.

2.1 Scatterplots

This section covers the use of scatterplots. Because scatterplots are so commonly used,

this section will cover more details about the use of these graphs than subsequent sections.

Also, this section will introduce some of the kinds of options that can be used with many

kinds of twoway plots, with cross-references to Options (235).

graph twoway scatter ownhome propval100

Here is a basic scatterplot. Note that

this command starts with graph

twoway, which indicates that this is a

twoway graph. scatter indicates that

we are creating a twoway scatterplot.

These are followed by the variable to be

placed on the y-axis and then the

variable for the x-axis.

Uses allstates.dta & scheme vg s2c

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

36 Chapter 2. Twoway graphs

twoway scatter ownhome propval100

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

Since it can be cumbersome to type

graph twoway scatter, Stata allows

you to shorten this to twoway scatter.

Uses allstates.dta & scheme vg s2c

scatter ownhome propval100

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

In fact, some graph twoway commands

are so frequently used that Stata

permits you to omit the graph twoway,

as we have done here, and just start the

command with scatter. While this

can save some typing, this can

sometimes conceal the fact that the

command is really a twoway graph and

that these are a special class of graphs.

For clarity, I will generally present

these graphs starting with twoway.

Uses allstates.dta & scheme vg s2c

twoway scatter ownhome propval100, msymbol(Sh)

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

You can control the marker symbol with

the msymbol() option. Here, we make

the symbols large, hollow squares. See

Options : Markers (235) for more details

about controlling the marker symbol,

size, and color, and see Styles : Symbols

(342) for the symbols you can select.

Uses allstates.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.1 Scatterplots 37

twoway scatter ownhome propval100, mcolor(maroon)

You can control the marker color with

the mcolor() option. Here, we make

the markers maroon. See Styles : Colors

(328) for other colors you could choose

and also Options : Markers (235) for

more details.

Uses allstates.dta & scheme vg s2c

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

twoway scatter ownhome propval100, msize(vlarge)

You can control the marker size with

the msize() option. Using

msize(vlarge), we make the markers

very large. Note that we switched to

the vg outc scheme, showing

white-ﬁlled markers, which can be

useful when the markers are large. See

Styles : Markersize (340) for other sizes

you could choose and also

Options : Markers (235) for more details.

Uses allstates.dta & scheme vg outc

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

twoway scatter ownhome propval100 [aweight=rent700], msize(small)

You can also use a weight variable to

determine the size of the symbols.

Using [aweight=rent700],wesizethe

symbols according to the proportion of

rents that exceed 700 dollars per

month, allowing us to graph three

variables at once. We add the

msize(small) option to shrink the size

of all the markers so they do not get

too large. See Options : Markers (235)

for more details.

Uses allstates.dta & scheme vg outc

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

38 Chapter 2. Twoway graphs

twoway scatter ownhome propval100, mlabel(stateab)

MS MO

ND OH

SD TN

UT VT

WV WI

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

The mlabel(stateab) option can be

used to add a marker label with the

state abbreviation. See

Options : Marker labels (247) for more

details about how you can control the

size, position, color, and angle of

marker labels.

Uses allstates.dta & scheme vg outc

twoway scatter ownhome propval100, mlabel(stateab) mlabsize(vlarge)

ID IL

MSMO

ND OH

SD TN

UT VT

WV WI

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

The mlabsize(vlarge) option controls

the marker label size. Here, we make

the marker label very large.

Uses allstates.dta & scheme vg outc

twoway scatter ownhome propval100, mlabel(stateab) mlabposition(12)

MS MO

ND OH

SD TN

UT VT

WV WI

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

The mlabposition() option controls

the marker label position with respect

to the marker. Here, we place the

marker labels at the 12 o’clock position

with respect to the markers, placing the

labels directly above the points they

label. See Options : Marker labels (247)

for examples illustrating the

mlabvposition() option, which

permits diﬀerent marker label positions

for diﬀerent observations.

Uses allstates.dta & scheme vg outc

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.1 Scatterplots 39

twoway scatter ownhome propval100, mlabel(stateab)

mlabposition(0) msymbol(i)

The mlabposition(0) option places

the marker label in the center position.

To keep it from being obscured by the

marker symbol, we also add the

msymbol(i) option to make the marker

symbol invisible. In eﬀect, the marker

symbols have been replaced by the

marker labels.

Uses allstates.dta & scheme vg outc

MS MO

ND OH

SD TN

UT VT

WV WI

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

twoway scatter fv propval100

Say that we ran the following

commands:

. regress ownhome propval100

. predict fv

The variable fv represents the ﬁt

values, and here we graph fv against

propval100. As we expect, all of the

points fall along a line, but they are not

connected. The next few examples will

consider options you can use to connect

points; see Options : Connecting (250)

for more details. For variety, we have

switched to the vg past scheme.

Uses allstates.dta & scheme vg past

60 65 70 75

yhat ownhome|propval100

0 20 40 60 80 100

% homes cost $100K+

twoway scatter fv propval100, connect(l) sort

We add the connect(l) option to

indicate that the points should be

connected with a line. We also add the

sort option, which is generally

recommended when you connect

observations and the data are not

already sorted on the x-variable.

Uses allstates.dta & scheme vg past

60 65 70 75

yhat ownhome|propval100

0 20 40 60 80 100

% homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

40 Chapter 2. Twoway graphs

twoway scatter fv ownhome propval100, connect(l i) sort

40 50 60 70 80

0 20 40 60 80 100

% homes cost $100K+

yhat ownhome|propval100 % who own home

We can show both the observations and

the ﬁt values in one graph. The

connect(l i) option speciﬁes that the

ﬁrst y-variable should be connected

with straight lines (lfor line) and the

second y-variable should not be

connected (ifor invisible connection).

Uses allstates.dta & scheme vg past

twoway scatter fv ownhome propval100, msymbol(i .) connect(l i)

sort

40 50 60 70 80

0 20 40 60 80 100

% homes cost $100K+

yhat ownhome|propval100 % who own home

The msymbol(i .) option speciﬁes

that the ﬁrst y-variable should not have

symbols displayed (ifor invisible

symbol) and that the second y-variable

should have the default symbols

displayed.

Uses allstates.dta & scheme vg past

twoway scatter fv ownhome propval100, msymbol(i .) connect(l i)

sort legend(label(1 Pred. Perc. Own))

40 50 60 70 80

0 20 40 60 80 100

% homes cost $100K+

Pred. Perc. Own % who own home

The legend() option can be used to

control the legend. We use the label()

option to specify the contents of the

ﬁrst item in the legend. See

Options : Legend (287) for more details

on legends.

Uses allstates.dta & scheme vg past

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.1 Scatterplots 41

twoway scatter fv ownhome propval100, msymbol(i .) connect(l i)

sort legend(label(1 Pred. Perc. Own) order(2 1))

The order() option can be used to

specify the order in which the items in

the legend are displayed.

Uses allstates.dta & scheme vg past

40 50 60 70 80

0 20 40 60 80 100

% homes cost $100K+

% who own home Pred. Perc. Own

twoway scatter fv ownhome propval100, msymbol(i .) connect(l i)

sort legend(label(1 Pred. Perc. Own) order(2 1) cols(1))

The cols(1) option makes the items in

the legend display in a single column.

Uses allstates.dta & scheme vg past

40 50 60 70 80

0 20 40 60 80 100

% homes cost $100K+

% who own home

Pred. Perc. Own

twoway scatter ownhome propval100,

xtitle("Percent homes over $100K") ytitle("Percent who own home")

The xtitle() and ytitle() option

can be used to specify the titles for the

x-andy-axes. See Options : Axis titles

(254) for more details about how to

control the display of axes. Note that

we are now using the vg s2m scheme,

one you might favor for graphs that will

be printed in black and white.

Uses allstates.dta & scheme vg s2m

40 50 60 70 80

Percent who own home

0 20 40 60 80 100

Percent homes over $100K

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

42 Chapter 2. Twoway graphs

twoway scatter ownhome propval100,

ytitle("Percent who own home", size(huge))

40 50 60 70 80

Percent who own home

0 20 40 60 80 100

% homes cost $100K+

Here, we use the size(huge) option to

make the title on the y-axis huge. For

other text sizes you could choose, see

Styles : Textsize (344).

Uses allstates.dta & scheme vg s2m

twoway scatter ownhome propval100, xlabel(0(10)100) ylabel(40(5)80)

40 45 50 55 60 65 70 75 80

% who own home

0 10 20 30 40 50 60 70 80 90 100

% homes cost $100K+

We use the ylabel() and xlabel()

options to control the labeling of the x-

and y-axes. We label the x-axis from 0

to 100, incrementing by 10, and the

y-axis from 40 to 80, incrementing by 5.

See Options : Axis labels (256) for more

details on labeling axes.

Uses allstates.dta & scheme vg s2m

twoway scatter ownhome propval100, xlabel(#10) ylabel(#5)

40 50 60 70 80

% who own home

0 10 20 30 40 50 60 70 80 90

% homes cost $100K+

In this example, we use the

xlabel(#10) option to ask Stata to use

approximately 10 nice labels and the

ylabel(#5) option to use

approximately 5 nice labels. In this

case, our gentle request was observed

exactly, but in some cases, Stata will

choose somewhat diﬀerent values to

create axis labels it believes are logical.

Uses allstates.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.1 Scatterplots 43

twoway scatter ownhome propval100, xlabel(#10) ylabel(#5, nogrid)

Using the nogrid option, we can

suppress the display of the grid. Note

that this option is placed within the

ylabel() option, thus suppressing the

grid for the y-axis. If the grid were

absent, and we wished to include it, we

could add the grid option. (You can

also specify grid or nogrid within the

xlabel() option to control grids for the

x-axis.) For more details, see

Options : Axis labels (256).

Uses allstates.dta & scheme vg s2m

40 50 60 70 80

% who own home

0 10 20 30 40 50 60 70 80 90

% homes cost $100K+

twoway scatter ownhome propval100, xlabel(#10) ylabel(#5, nogrid)

yline(55 75, lwidth(thin) lcolor(black) lpattern(dash))

The yline() option is used to add a

thin, black, dashed line to the graph

where yequals 55 and 75.

Uses allstates.dta & scheme vg s2m

40 50 60 70 80

% who own home

0 10 20 30 40 50 60 70 80 90

% homes cost $100K+

twoway scatter ownhome propval100, xscale(alt)

Here, we use the xscale() option to

request that the x-axis be placed in its

alternate position, in this case at the

top instead of at the bottom. To learn

more about axis scales, including

suppressing, extending, or relocating

them, see Options : Axis scales (265).

Uses allstates.dta & scheme vg s2m

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

44 Chapter 2. Twoway graphs

twoway scatter ownhome propval100, by(nsw)

50 60 70 8050 60 70 80

0 50 100

North South

West

% who own home

% homes cost $100K+

Graphs by Region North, South, or West

The by(nsw) option is used here to

make separate graphs for states in the

North, South, and West. At the bottom

left corner, you can see a note that

describes how the separate graphs

arose. This is based on the variable

label for nsw; if this variable had not

been labeled, it would have read Graphs

by nsw. See Options : By (272) for more

details about using the by() option.

Uses allstates.dta & scheme vg s2m

twoway scatter ownhome propval100, by(nsw, total)

50 60 70 8050 60 70 80

0 50 100 0 50 100

North South

West Total

% who own home

% homes cost $100K+

Graphs by Region North, South, or West

The total option can be used within

the by() option to add an additional

graph showing all the observations.

Uses allstates.dta & scheme vg s2m

twoway scatter ownhome propval100, by(nsw, total compact)

50 60 70 8050 60 70 80

0 50 1000 50 100

North South

West Total

% who own home

% homes cost $100K+

Graphs by Region North, South, or West

The compact option can be used to

make the graphs display more

compactly.

Uses allstates.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.1 Scatterplots 45

twoway scatter ownhome propval100, text(47 62 "Washington, DC")

We can use the text() option to add

text to the graph. We add text to label

the observation belonging to

Washington, DC. See

Options : Adding text (299) for more

information about adding text in the

section.

Uses allstates.dta & scheme vg s2m

Washington, DC

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

twoway scatter ownhome propval100, text(47 62 "Washington, DC", size(large)

margin(medsmall) blwidth(vthick) box)

Stata gives you considerable control

over the display of text you add to the

graph, as well as the ability to enclose

the text in a box and control the

characteristics of the box. See

Options : Textboxes (303) for more

details.

Uses allstates.dta & scheme vg s2m

Washington, DC

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

twoway (scatter ownhome propval100) (scatteri 42.6 62.1 "DC")

This graph uses the scatteri (scatter

immediate) command to plot and label

a point for Washington, DC. The values

42.6 and 62.1 are the values for

ownhome and propval100 for

Washington, DC, and are followed by

"DC", which acts as a marker label for

that point. If we had instead speciﬁed

(9) "DC", then “DC” would have been

plotted at the 9 o’clock position.

Uses allstates.dta & scheme vg s2m

40 50 60 70 80

0 20 40 60 80 100

% who own home y

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

46 Chapter 2. Twoway graphs

twoway (scatter ownhome propval100)

(scatteri 42.6 62.1 "DC" 55.9 89 (8) "HI"),legend(off)

40 50 60 70 80

0 20 40 60 80 100

This graph extends the previous

example by adding a second point for

Hawaii and providing a position for the

marker label for Hawaii, placing it at

the 8 o’clock position. In addition, the

legend(off) option suppresses the

legend. Finally, this graph uses the

vg samec scheme, so the markers

created via scatteri look identical to

the other markers.

Uses allstates.dta & scheme vg samec

This section concludes by looking at some additional graph commands that make graphs

similar to twoway scatter, namely, twoway spike,twoway dropline,andtwoway dot.

Most of the options we have illustrated before apply to these graphs as well, so they will

not be repeated here. We will switch to using the vg blue scheme for the rest of the graphs

in this section.

twoway scatter r yhat

−40

−20

resid propval100|urban

0 20 40 60

yhat propval100|urban

Imagine that we ran a regression

predicting propval100 from urban and

generated the residual, calling it r,and

the predicted value, calling it yhat.

Consider this graph using the scatter

command to display the residual by the

predicted value.

Uses allstates.dta & scheme vg blue

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.1 Scatterplots 47

twoway spike r yhat

This same graph could be shown using

the spike command. This produces a

spike plot, and each spike, by default,

originates from 0.

Uses allstates.dta & scheme vg blue

−40

−20

resid propval100|urban

0 20 40 60

yhat propval100|urban

twoway spike r yhat, blcolor(navy) blwidth(thick)

You can use the blcolor() (bar line

color) option to set the color of the

spikes and the blwidth() (bar line

width) option to set the width of the

spikes. Here, we make the spikes thick

and navy. See Styles : Colors (328) for

more details about specifying colors

and see Styles : Linewidth (337) for more

details about specifying line widths.

Uses allstates.dta & scheme vg blue −40

−20

resid propval100|urban

0 20 40 60

yhat propval100|urban

twoway spike r yhat, base(10)

By default, the base is placed at 0,

which is a very logical choice when

displaying residuals since our interest is

in deviations from 0. For illustration,

we use the base(10) option to set the

base of the y-axis to be 10, and the

spikes are displayed with respect to 10.

Uses allstates.dta & scheme vg blue

−40

−20

resid propval100|urban

0 20 40 60

yhat propval100|urban

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

48 Chapter 2. Twoway graphs

twoway spike r yhat, horizontal xtitle(Title for x-axis)

ytitle(Title for y-axis)

Title for y−axis

−40 −20 0 20 40 60

Title for x−axis

The spike command supports the

horizontal option, which swaps the

position of the rand yhat variables.

Note that the x-axis still remains at the

bottom and the y-axis still remains at

the left.

Uses allstates.dta & scheme vg blue

twoway dropline r yhat, msymbol(Oh)

−40

−20

resid propval100|urban

0 20 40 60

yhat propval100|urban

Atwoway dropline plot is much like a

spike plot but permits a symbol, as

well. It supports the horizontal,

base(),blcolor(),andblwidth()

options just like twoway spike, so these

are not illustrated. But you can use

marker symbol options to control the

symbol. Here, we add the msymbol(Oh)

option to obtain hollow circles as the

symbols; see Options : Markers (235) for

more details.

Uses allstates.dta & scheme vg blue

twoway dropline r yhat, msymbol(O) msize(vlarge)

mfcolor(gold) mlcolor(olive) mlwidth(thick)

−40

−20

resid propval100|urban

0 20 40 60

yhat propval100|urban

Here, we make the symbols very large

circles and use mfcolor() to make the

marker ﬁll color gold, mlcolor() to

make the marker line color olive, and

mlwidth() to make the marker line

width thick. For more information, see

Options : Markers (235).

Uses allstates.dta & scheme vg blue

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.2 Regression ﬁts and splines 49

twoway dot close tradeday, msize(large) msymbol(O)

mfcolor(eltgreen) mlcolor(emerald) mlwidth(thick)

The dot command is similar to a

scatterplot but shows dotted lines for

each value of the x-variable, making it

more useful when the x-values are

equally spaced. In this example, we

look at the closing price of the S&P 500

by trading day and make the markers

ﬁlled with eltgreen with thick emerald

outlines.

Uses spjanfeb2001.dta & scheme

vg blue

1250

1300

1350

1400

Closing price

0 10 20 30 40

Trading day number

2.2 Regression ﬁts and splines

This section focuses on the twoway commands that are used for displaying ﬁt values:

lfit,qfit,fpfit,mband,mspline,andlowess. For more information, see [G]graph

twoway lﬁt,[

G]graph twoway qﬁt,[G]graph twoway fpﬁt,[G]graph twoway

mband,[

G]graph twoway mspline, and [G]graph twoway lowess.Weusethe

allstates data ﬁle, omitting Washington, DC, and show the graphs using the vg s2c

scheme.

twoway (scatter ownhome pcturban80) (lfit ownhome pcturban80)

Here, we show a scatterplot of ownhome

by pcturban80. In addition, we overlay

a linear ﬁt lfit predicting ownhome

from pcturban80. See

Twoway : Overlaying (87) if you would

like more information about overlaying

twoway graphs.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20.0 40.0 60.0 80.0 100.0

Percent urban

% who own home Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

50 Chapter 2. Twoway graphs

twoway (scatter ownhome pcturban80) (lfit ownhome pcturban80)

(qfit ownhome pcturban80)

55 60 65 70 75 80

20.0 40.0 60.0 80.0 100.0

Percent urban

% who own home Fitted values

Fitted values

It is sometimes useful to overlay ﬁt

plots to compare the ﬁt values. In this

example, we overlay a linear ﬁt lfit

and quadratic ﬁt qfit and can see

some discrepancies between them.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome pcturban80) (mspline ownhome pcturban80)

(fpfit ownhome pcturban80) (lowess ownhome pcturban80)

55 60 65 70 75 80

20.0 40.0 60.0 80.0 100.0

Percent urban

% who own home Median spline

predicted ownhome lowess ownhome pcturban80

Stata supports a number of other ﬁt

methods. Here, we show an mspline

(median spline) overlaid with fpfit

(fractional polynomial ﬁt) and lowess.

Another similar command, not shown,

is mband (median band).

Uses allstatesdc.dta & scheme vg s2c

2.3 Regression conﬁdence interval (CI) ﬁts

This section focuses on the twoway commands that are used for displaying conﬁdence

intervals around ﬁt values: lfitci,qfitci,andfpfitci. The options permitted by these

three commands are virtually identical so we will use lfitci to illustrate these options.

(Note, however, that fpfitci does not permit the options stdp,stdf,andstdr.) For

more information, see [G]graph twoway lﬁtci,[G]graph twoway qﬁtci, and [G]graph

twoway fpﬁtci.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.3 Regression conﬁdence interval (CI) ﬁts 51

twoway (lfitci ownhome pcturban80) (scatter ownhome pcturban80)

This graph uses the lfitci command

to produce a linear ﬁt with conﬁdence

interval. The conﬁdence interval, by

default, is computed using the standard

error of prediction. We overlay this

with a scatterplot.

Uses allstatesdc.dta & scheme vg rose

20 40 60 80 100

Percent urban

95% CI Fitted values

% who own home

twoway (scatter ownhome pcturban80) (lfitci ownhome pcturban80)

This example is the same as the

previous example; however, the order of

the scatter and lfitci commands is

reversed. Note that the order matters

since the points that fell within the

conﬁdence interval are not displayed

because they are masked by the

shading of the conﬁdence interval.

Uses allstatesdc.dta & scheme vg rose 55

20.0 40.0 60.0 80.0 100.0

Percent urban

% who own home 95% CI

Fitted values

twoway (lfitci ownhome pcturban80, stdf)

(scatter ownhome pcturban80)

Here, we add the stdf option, which

computes the conﬁdence intervals using

the standard error of forecast. If

samples were drawn repeatedly, this

conﬁdence interval would capture 95%

of the observations. With 50

observations, we would expect 2 or 3

observations to fall outside of the

conﬁdence interval, and this

corresponds to the data shown here.

Uses allstatesdc.dta & scheme vg rose

20 40 60 80 100

Percent urban

95% CI Fitted values

% who own home

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

52 Chapter 2. Twoway graphs

twoway (lfitci ownhome pcturban80, stdf level(90))

(scatter ownhome pcturban80)

20 40 60 80 100

Percent urban

90% CI Fitted values

% who own home

We can use the level() option to set

the conﬁdence level for the conﬁdence

interval. Here, we make the conﬁdence

level 90%.

Uses allstatesdc.dta & scheme vg rose

twoway (lfitci ownhome pcturban80, nofit)

(scatter ownhome pcturban80)

55 60 65 70 75 80

20 40 60 80 100

Percent urban

95% CI % who own home

We now look at how you can control

the display of the ﬁt line. We can use

the nofit option to suppress the

display of the ﬁt line. Note that we

have switched to the vg brite scheme

for a diﬀerent look for the graphs.

Uses allstatesdc.dta & scheme vg brite

twoway (lfitci ownhome pcturban80, clpattern(dash) clwidth(thick))

(scatter ownhome pcturban80)

55 60 65 70 75 80

20 40 60 80 100

Percent urban

95% CI Fitted values

% who own home

You can supply options like connect(),

clpattern() (connect line pattern),

clwidth() (connect line width), and

clcolor() (connect line color) to

control how the ﬁt line will be

displayed. Here, we use the

clpattern(dash) and clwidth(thick)

options to make the ﬁt line dashed and

thick. See Options : Connecting (250) for

more details.

Uses allstatesdc.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.3 Regression conﬁdence interval (CI) ﬁts 53

twoway (lfitci ownhome pcturban80, bcolor(stone))

(scatter ownhome pcturban80)

We use the bcolor(stone) option to

change the color of the area and outline

of the conﬁdence interval. You can use

the options illustrated with twoway

rarea to control the display of the area

encompassing the conﬁdence interval,

namely, bcolor(),bfcolor(),

blcolor(),blwidth(),and

blpattern(). See Twoway : Range (64)

and [G]graph twoway rarea for more

details.

Uses allstatesdc.dta & scheme vg brite

55 60 65 70 75 80

20 40 60 80 100

Percent urban

95% CI Fitted values

% who own home

twoway (lfitci ownhome pcturban80, ciplot(rline))

(scatter ownhome pcturban80)

The ciplot() option can be used to

select a diﬀerent command for

displaying the conﬁdence interval. The

default command is twoway rarea and

can be selected via the ciplot(rarea)

option. Here, we use the

ciplot(rline) option, which displays

the conﬁdence interval as two lines

without any ﬁlled area. The valid

options include rarea,rbar,rspike,

rcap,rcapsym,rscatter,rline,and

rconnected.

Uses allstatesdc.dta & scheme vg brite

55 60 65 70 75 80

20 40 60 80 100

Percent urban

95% CI Fitted values

% who own home

twoway

(lfitci ownhome pcturban80, ciplot(rline) blcolor(green) blpattern(dash)

blwidth(thick)) (scatter ownhome pcturban80)

By choosing the rline command for

displaying the conﬁdence interval, we

can then use options appropriate for

the twoway rline command. Here, we

make the line green, dashed, and thick.

See Styles : Colors (328),

Styles : Linepatterns (336), and

Styles : Linewidth (337) for more details

about colors, line patterns, and line

widths.

Uses allstatesdc.dta & scheme vg brite

55 60 65 70 75 80

20 40 60 80 100

Percent urban

95% CI Fitted values

% who own home

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

54 Chapter 2. Twoway graphs

2.4 Line plots

This section focuses on the twoway commands for creating line plots, including the

twoway line and twoway connected commands. The line command is the same as

scatter, except that the points are connected by default and marker symbols are not

permitted, whereas the twoway connected command permits marker symbols. This sec-

tion also illustrates twoway tsline and twoway tsrline, which are useful for drawing line

plots when the x-variable is a date variable. Since all these commands are related to the

twoway scatter command, they support most of the options you would use with twoway

scatter. For more information, see [G]graph twoway line,[G]graph twoway con-

nected,andhelp graph tsline.

twoway line close tradeday, sort

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Here, we show an example using

twoway line showing the closing price

across trading days. Note the inclusion

of the sort option, which is

recommended when you have points

connected in a Stata graph.

Uses spjanfeb2001.dta & scheme vg s2c

twoway line close tradeday, sort clwidth(vthick) clcolor(maroon)

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Here, we show options controlling the

width and color of the lines. Using

clwidth(vthick) (connect line width)

and clcolor(maroon) (connect line

color), we make the line very thick and

maroon. See Options : Connecting (250)

for more examples. Note that you

cannot use options that control marker

symbols with graph twoway line.

Uses spjanfeb2001.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.4 Line plots 55

twoway connected close tradeday, sort

This twoway connected graph is

similar to the twoway line graphs we

saw before, except that when you use

connected, a marker is shown for each

data point.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

twoway scatter close tradeday, connect(l) sort

This graph is identical to the previous

graph, except this graph is made with

the scatter command using the

connect(l) option. This illustrates the

convenience of using connected since

you do not need to manually specify

the connect() option.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

twoway connected close tradeday, sort

msymbol(Dh) mcolor(blue) msize(large)

We can use marker symbol options,

such as msymbol(),mcolor(),and

msize() to control the marker symbols.

Here, we make the symbols large, blue,

hollow diamonds. See Options : Markers

(235) for more examples.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

56 Chapter 2. Twoway graphs

twoway connected close tradeday, sort

clcolor(cranberry) clpattern(dash) clwidth(thick)

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

You can control the look of the lines

with connect options such as

clwidth(),clcolor(),and

clpattern() (connect line pattern). In

this example, we make the line

cranberry, dashed, and thick. See

Options : Connecting (250) for more

details on connecting points.

Uses spjanfeb2001.dta & scheme vg s2c

twoway connected high low tradeday, sort

1200 1250 1300 1350 1400

0 10 20 30 40

Trading day number

High price Low price

You can graph multiple variables at

once. In this case, we graph the high

and low prices across trading days.

Uses spjanfeb2001.dta & scheme vg s2c

twoway connected high low tradeday, sort

clwidth(thin thick) msymbol(Oh S)

1200 1250 1300 1350 1400

0 10 20 30 40

Trading day number

High price Low price

When graphing multiple variables, you

can specify connect and marker symbol

options to control each line. In this

case, we use a thin line for the high

price and a thick line for the low price.

We also diﬀerentiate the two lines by

using diﬀerent marker symbols, hollow

circles for the high price and squares for

the low price.

Uses spjanfeb2001.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.4 Line plots 57

Stata has additional commands for creating line plots where the x-variable is a date

variable, namely, twoway tsline and twoway tsrline.Thetsline command is similar

to the line command, and the tsrline is similar to the rline command, but both of these

ts commands oﬀer extra features, making it easier to reference the x-variable in terms of

dates. Note that these commands are not currently documented in [G]graph but are doc-

umented via help tsline. We will use the sp2001ts data ﬁle, which has the prices for

the S&P 500 index for 2001 with the trading date stored as a date variable named date.

Before saving the ﬁle sp2001ts,thetsset date, daily command was used to tell Stata

that the variable date represents the time variable and that it represents daily data.

twoway tsline close

The tsline (time-series line) graph

shows the closing price on the y-axis

and the date on the x-axis. Note that

we did not specify the x-variable in the

graph command. Stata knew the

variable representing time because we

previously issued the tsset date,

daily command before saving the

sp2001ts ﬁle. Note that if you save the

data ﬁle, Stata remembers the time

variable, and you do not need to set it

again.

Uses sp2001ts.dta & scheme vg s1c

1000 1100 1200 1300 1400

Closing price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

twoway tsrline low high

We can also use the tsrline

(time-series range) graph to show the

low price and high price for each day.

Uses sp2001ts.dta & scheme vg s1c

900 1000 1100 1200 1300 1400

Low price/High price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

58 Chapter 2. Twoway graphs

twoway tsline close, clwidth(thick) clcolor(navy)

1000 1100 1200 1300 1400

Closing price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

As with twoway line,youcanuse

connect options to control the line.

Here, we make the line thick and navy.

Uses sp2001ts.dta & scheme vg s1c

twoway tsline close

if (date >= mdy(1,1,2001)) & (date <= mdy(3,31,2001))

1100 1200 1300 1400

Closing price

1Jan01 1Feb01 1Mar01 1Apr01

Date

You can use if to subset cases to

graph. Here, we graph the closing

prices between January 1, 2001, and

March 31, 2001. See the next example

for an easier way of doing this.

Uses sp2001ts.dta & scheme vg s1c

twoway tsline close if tin(01jan2001,31mar2001)

1100 1200 1300 1400

Closing price

1Jan01 1Feb01 1Mar01 1Apr01

Date

When using the tsline command, you

can use tin() (time in between) to

specify that you want to graph just the

cases between January 1, 2001, and

March 31, 2001, inclusively.

Uses sp2001ts.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.4 Line plots 59

twoway tsline close, ttitle(Day of Year)

We can use the ttitle() (time title)

option to give a title to the time

variable. We specify this as a ttitle()

instead of xtitle() since this refers to

the axis with the time variable.

Uses sp2001ts.dta & scheme vg s1c

1000 1100 1200 1300 1400

Closing price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Day of Year

twoway tsline close,

tlabel(01jan2001 31mar2001 30jun2001 30sep2001 01jan2002)

We can use the tlabel() option to

label the time points on the time axis.

Note that we speciﬁed these dates using

date literals, and Stata knew how to

interpret these and appropriately label

the graph with these values.

Uses sp2001ts.dta & scheme vg s1c

1000 1100 1200 1300 1400

Closing price

1Jan01 31Mar01 30Jun01 30Sep01 1Jan02

Date

twoway tsline close,

tlabel(01jan2001 30jun2001 01jan2002 ) tmlabel(31mar2001 30sep2001)

We can use the tmlabel() option to

include minor labels.

Uses sp2001ts.dta & scheme vg s1c

1000 1100 1200 1300 1400

Closing price

31Mar01 30Sep01

1Jan01 30Jun01 1Jan02

Date

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

60 Chapter 2. Twoway graphs

twoway tsline close,

tlabel(01jan2001 30jun2001 01jan2002 ) tmtick(31mar2001 30sep2001)

1000 1100 1200 1300 1400

Closing price

1Jan01 30Jun01 1Jan02

Date

We can use the tmtick() option to

include minor ticks instead.

Uses sp2001ts.dta & scheme vg s1c

twoway tsline close,

tline(01apr2001 01jul2001 01oct2001)

1000 1100 1200 1300 1400

Closing price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

The tline() option can be used to

include lines at certain time points.

Here, we place lines at the start of the

second, third, and fourth quarters.

Uses sp2001ts.dta & scheme vg s1c

twoway tsline close,

ttext(1035 01apr2001 "Start of Q2", orientation(vertical))

Start of Q2

1000 1100 1200 1300 1400

Closing price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

We can use the ttext() option to add

text to the graph. The ﬁrst coordinate

refers to the position on the y-axis, and

the second coordinate is the position on

the time axis in terms of the date.

Uses sp2001ts.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.5 Area plots 61

2.5 Area plots

This section illustrates the use of area graphs using twoway area. These graphs are

similar to twoway line graphs, except that the area under the line is shaded. As a result,

many of the options that you would use with twoway line are applicable; see Twoway : Line

(54) for more details. For even more details, see [G]graph twoway area. We will use

the spjanfeb2001 data ﬁle, which has the prices for the S&P 500 index for January and

February 2001.

twoway area close tradeday, sort

This is an example of a twoway area

graph. Because this graph is composed

of connected points, the sort option is

recommended in case the data are not

already sorted by tradeday. If the data

are not sorted, and the sort option is

not speciﬁed, then the points are

connected in the order they appear in

the data ﬁle and will generally not be

the graph you desire.

Uses spjanfeb2001.dta & scheme

vg palec

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

twoway area close tradeday, horizontal sort

xtitle(Title for x-axis) ytitle(Title for y-axis)

The horizontal option swaps the

position of the close and tradeday

variables. Note that the x-axis remains

at the bottom and the y-axis remains

at the left.

Uses spjanfeb2001.dta & scheme

vg palec

0 10 20 30 40

Title for y−axis

1250 1300 1350 1400

Title for x−axis

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

62 Chapter 2. Twoway graphs

twoway area close tradeday, sort base(1320.28)

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

You can use the base() option to

indicate a base from which the area is

to be shaded. In this example, the base

is the closing price on the ﬁrst trading

day, and thus all the subsequent points

are a kind of deviation from the ﬁrst

day’s closing price.

Uses spjanfeb2001.dta & scheme

vg palec

twoway area close tradeday, sort bcolor(emerald)

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

The bcolor() option sets the color of

the shaded area and the line. Here, we

make the shaded area and line emerald.

Although it is not shown, you can also

use the bfcolor() and blcolor()

options to control the ﬁll color and line

color and the blwidth() option to

control the thickness of the outline.

Uses spjanfeb2001.dta & scheme

vg palec

2.6 Bar plots

This section illustrates the use of twoway bar graphs using twoway bar. These graphs

show a bar for each x-value where the height of the bar corresponds to the value of the

y-variable. For more details, see [G]graph twoway bar. We will continue to use the

spjanfeb2001 data ﬁle, which has the prices for the S&P 500 index for January and Febru-

ary, 2001, but show the graphs using the vg s1m scheme. twoway bar is useful for creating

bar graphs with overlays of lines, points, or other plot types and can be useful with evenly

spaced x-variable data. graph bar is more useful for creating bar graphs with categorical

data.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.6 Bar plots 63

twoway bar close tradeday

Consider this bar chart, which shows

the closing prices of the S&P 500

broken down by the trading day of the

year.

Uses spjanfeb2001.dta & scheme

vg s1m

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

twoway bar close tradeday, horizontal

xtitle(Title for x-axis) ytitle(Title for y-axis)

We can make the close and tradeday

variables trade places with the

horizontal option. Note that the

x-axis still remains at the bottom and

the y-axis still remains at the left.

Uses spjanfeb2001.dta & scheme

vg s1m

0 10 20 30 40

Title for y−axis

1250 1300 1350 1400

Title for x−axis

twoway bar close tradeday, base(1200)

Unless we specify otherwise, the base

forthebarchartsisthetradingday

with the lowest price. In this example,

the closing price on day 40 was 1239.94,

so unless we specify the base() option,

the base would be 1239.94. As a result,

the bar for day 40 would have a zero

height. Here, we change the base to

1200 to give this bar a height.

Uses spjanfeb2001.dta & scheme

vg s1m

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

64 Chapter 2. Twoway graphs

twoway bar close tradeday, barwidth(.7)

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Unless otherwise speciﬁed, the width of

each bar is one x-unit (in this case, one

day). By making the width of the bars

.7, we can obtain a small gap between

the bars.

Uses spjanfeb2001.dta & scheme

vg s1m

twoway bar close tradeday, bfcolor(gs15) blcolor(gs5)

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

We can use the bfcolor() (bar ﬁll

color) option to set the color of the

inside of the bars and the blcolor (bar

line color) option to set the color of the

bar outlines. Here, we make the bars

light gray on the inside and dark gray

on the outside. See Styles : Colors (328)

for more colors you can choose.

Uses spjanfeb2001.dta & scheme

vg s1m

2.7 Range plots

This section focuses on twoway commands that display range plots. The major charac-

teristic these graphs share is that, for each x-value, there are two corresponding y-values. A

common example is a conﬁdence interval where, for each x-value, there are upper and lower

conﬁdence limits. We ﬁrst show examples of all of these types of graphs and then consider

the options that can be used to customize them. For more information, see [G]graph

twoway rarea,[

G]graph twoway rbar,[G]graph twoway rspike,[G]graph twoway

rcap,[

G]graph twoway rcapsym,[G]graph twoway rscatter,[G]graph twoway

rline, and [G]graph twoway rconnected. We will start by looking at the rconnected,

rscatter,rline,andrarea graphs, which use combinations of lines, symbols, and shading

to display range plots. These examples use the spjanfeb2001 data ﬁle.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.7 Range plots 65

twoway rconnected high low tradeday, sort

The rconnected (range connected)

graph shows the high and low prices by

tradeday, the number of days stocks

have been traded in the year. The

rconnected plot shows a separate line

for the high and low prices, and a

marker appears for each x-value. The

sort option is recommended because

the points are connected by lines and is

needed if the data were not already

sorted on tradeday.

Uses spjanfeb2001.dta & scheme

vg rose

1200

1250

1300

1350

1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rscatter high low tradeday

The rscatter graph is similar to the

rconnected graph, except that lines

connecting the symbols are not plotted.

Uses spjanfeb2001.dta & scheme

vg rose

1200

1250

1300

1350

1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rline high low tradeday, sort

The rline graph is similar to the

rconnected graph, except that symbols

are not plotted at each level of x. Note

the inclusion of the sort option. This

option is recommended because the

points are connected by lines and is

needed if the data were not already

sorted on tradeday.

Uses spjanfeb2001.dta & scheme

vg rose 1200

1250

1300

1350

1400

High price/Low price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

66 Chapter 2. Twoway graphs

twoway rarea high low tradeday, sort

1200

1250

1300

1350

1400

High price/Low price

0 10 20 30 40

Trading day number

The rarea graph is similar to the

rline graph, except that you can

control the ﬁll color of the area between

the high and low values.

Uses spjanfeb2001.dta & scheme

vg rose

Next, we discuss the rcap,rspike,andrcapsym graphs, which use combinations of

spikes, caps, and symbols to display range plots. These plots are followed by rbar,which

uses bars to display range plots. These next examples are shown using the vg s2m scheme.

twoway rcap high low tradeday

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

The rcap graph shows a spike ranging

from the low to high values and puts a

cap at the top and bottom of each

spike.

Uses spjanfeb2001.dta & scheme

vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.7 Range plots 67

twoway rspike high low tradeday

The rspike graph is similar to the

rcap graph, except that no caps are

placed on the spikes.

Uses spjanfeb2001.dta & scheme

vg s2m

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rcapsym high low tradeday

The rcapsym graph is similar to the

rcap graph, except that instead of

caps, symbols are placed at the end of

the spikes. You can choose among the

symbols to use for a scatterplot.

Uses spjanfeb2001.dta & scheme

vg s2m

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rbar high low tradeday

The rbar graph uses bars for each

value of xto show the high and low

values of y.

Uses spjanfeb2001.dta & scheme

vg s2m

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

68 Chapter 2. Twoway graphs

Let’s now consider options you can use with the rconnected,rscatter,rline,and

rarea graphs. We will start by looking at rconnected plots since many of the options used

in that kind of graph also apply to rscatter,rline,andrarea graphs. These graphs will

be shown using the vg s1c scheme.

twoway rconnected high low tradeday, sort

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

Here is a general rconnected graph.

Uses spjanfeb2001.dta & scheme vg s1c

twoway rconnected high low tradeday, sort horizontal

xtitle(Title for x-axis) ytitle(Title for y-axis)

0 10 20 30 40

Title for y−axis

1200 1250 1300 1350 1400

Title for x−axis

With the horizontal option, you can

swap the axes where high/low and

tradeday appear. Note that the x-axis

remains at the bottom and the y-axis

remains at the left.

Uses spjanfeb2001.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.7 Range plots 69

twoway rconnected high low tradeday, sort

blwidth(thick) blcolor(dkgreen) blpattern(dash)

You can control the look of the lines

with connect options such as

connect(),blwidth(),blcolor(),

and blpattern(). Here, we make the

lines thick, dark green, and dashed. See

Options : Connecting (250) for more

examples, and see more details in

Styles : Connect (332), Styles : Linewidth

(337), Styles : Colors (328), and

Styles : Linepatterns (336).

Uses spjanfeb2001.dta & scheme vg s1c

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rconnected high low tradeday, sort

msymbol(Oh) msize(large) mcolor(lavender)

You can control the look of the marker

symbols with options such as

msymbol(),msize(),andmcolor().

Here, we make the marker symbols

large, hollow, lavender circles. For more

details about options related to

symbols, see Options : Markers (235).

Uses spjanfeb2001.dta & scheme vg s1c

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rscatter high low tradeday, sort msymbol(Sh) msize(medium)

mlwidth(thick)

The options you can use with rscatter

are just a subset of those you would use

with rconnected, where the connecting

options would not be relevant. Here, we

use the marker options to make the

symbols medium, hollow squares with

thick outlines. For more details about

options related to marker symbols, see

Options : Markers (235).

Uses spjanfeb2001.dta & scheme vg s1c

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

70 Chapter 2. Twoway graphs

twoway rline high low tradeday, sort blwidth(thick) blcolor(blue)

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

The options you can use with rline are

a subset of those you would use with

rconnected; namely, the marker

symbol and marker label options are

not relevant. Here, we show the use of

connect options to make the lines thick

and blue. For more details about

connect options, see

Options : Connecting (250).

Uses spjanfeb2001.dta & scheme vg s1c

twoway rarea high low tradeday, sort bcolor(teal)

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

The rarea graph is similar to the

rline graph, but in addition to being

able to control the characteristics of the

line, you can also control the color of

the area between the low and high

lines. Here, we use the bcolor() option

to make the color of the line and the

area teal.

Uses spjanfeb2001.dta & scheme vg s1c

twoway rarea high low tradeday, sort

blcolor(emerald) bfcolor(teal) blwidth(thick)

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

Here, we make the color of the line

emerald with the blcolor() option,

the ﬁll color teal with the bfcolor()

option, and the line thick with the

blwidth() option.

Uses spjanfeb2001.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.7 Range plots 71

Now, let’s look at options that can be used with the rcap,rspike,andrcapsym graphs.

The options permitted by the rcap option are similar to the options used with the rspike

and rcapsym graphs. For these examples, we will use the vg s2c scheme.

twoway rcap high low tradeday

Here is an rcap graph with the default

options. The rcap command supports

the horizontal option, which would

make the variables high/low and

tradeday swap positions.

Uses spjanfeb2001.dta & scheme vg s2c

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rcap high low tradeday, msize(small)

The msize() option usually is used to

control the size of a marker and is

adapted for this kind of graph to

control the size of the cap. In this case,

the cap is made small.

Uses spjanfeb2001.dta & scheme vg s2c

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

72 Chapter 2. Twoway graphs

twoway rcap high low tradeday, blcolor(cranberry) blwidth(thick)

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

The blcolor() option is used to

control the color of the line, in this case

making the line cranberry. The

blwidth() option is used to set the

width of the line; in this case, the line is

made thick. Although it is not shown

here, you could also control the pattern

of the line with the blpattern()

option. See Options : Connecting (250)

for more details.

Uses spjanfeb2001.dta & scheme vg s2c

twoway rspike high low tradeday, blcolor(red) blwidth(thin)

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

The options used for rspike are

basically the same as those for rcap,

except that the msize() option is not

appropriate since there are no markers

to size. Here, for example, we use

blcolor() and blwidth() to make the

lines red and thin.

Uses spjanfeb2001.dta & scheme vg s2c

twoway rcapsym high low tradeday, msymbol(Oh) msize(large)

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

The options used for rcapsym are

basically the same as for rcap, except

that you can use marker options to

select the marker that goes at the top

and bottom of each spike, and you can

also use marker label options to label

the markers (however, this is probably

not very useful and is not illustrated).

In this case, we use the msymbol()

option to place hollow circles at the end

of the spikes and the msize() option to

make the symbols large.

Uses spjanfeb2001.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

2.7 Range plots 73

We will now explore options that can be used with twoway rbar, and we will switch to

using the vg brite scheme.

twoway rbar high low tradeday

Here is a basic rbar graph with the

default options. As with the other

graphs in this family, we could have

added the horizontal option to switch

the position of the high/low and

tradeday variables, but this is not

shown.

Uses spjanfeb2001.dta & scheme

vg brite

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

twoway rbar high low tradeday, barwidth(.7)

The barwidth() option can be used to

set the width of the bar. This width is

in units of the x-variable. We set the

bars to be .7 units wide, so they no

longer touch each other.

Uses spjanfeb2001.dta & scheme

vg brite

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

74 Chapter 2. Twoway graphs

twoway rbar high low tradeday, bcolor(sienna)

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

The bcolor() (bar color) option sets

the color of the bar and the outline,

making the color sienna.

Uses spjanfeb2001.dta & scheme

vg brite

twoway rbar high low tradeday, bfcolor(sienna)

blcolor(cranberry) blwidth(thick)

1200 1250 1300 1350 1400

High price/Low price

0 10 20 30 40

Trading day number

With the bfcolor() (bar ﬁll color)

option, we set the ﬁll color of the bar to

be sienna and then use the blcolor()

(bar line color) option to set the color

of the outline to be cranberry. We also

use the blwidth() (bar line width)

option to make the lines surrounding

the bars thick.

Uses spjanfeb2001.dta & scheme

vg brite

2.8 Distribution plots

This section describes the use of twoway histogram and twoway kdensity for showing

the distribution of a single variable. In addition, this section also shows the use of twoway

function for showing the relationship between xand yusing a function that you specify.

See [G]graph twoway histogram,[G]graph twoway kdensity, and [G]graph twoway

function for more information. We will start by showing the twoway histogram command

and consider options that allow you to control such things as the number of bins, the width

of the bins, and the starting point for the bins. Then, we will show options that control the

scaling of the y-axis. The next few graphs use the vg past scheme.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.8 Distribution plots 75

twoway histogram ttl exp

We begin by showing a histogram of

the variable total work experience.

Note that, unlike many other twoway

plots, this command takes only one

variable that is graphed on the x-axis.

The y-axis represents the density, such

that the sum of the areas of the bars

equals 1. If you are not going to

combine this graph with other twoway

graphs, the histogram command may

be preferable to twoway histogram.

Uses nlsw.dta & scheme vg past

0 .02 .04 .06 .08 .1

Density

0 10 20 30

Tot. work exper.

twoway histogram ttl exp, bin(10)

We can control the number of bins that

are used to display the histogram using

the bin() option. Here, we request

that 10 bins be used.

Uses nlsw.dta & scheme vg past

0 .02 .04 .06 .08

Density

0 10 20 30

Tot. work exper.

twoway histogram ttl exp, width(5)

We can control the width of each bar

using the width() option. Here, we

make each bar 5 units wide. As you

might imagine, you can use either the

bin() option or the width() option

but not both.

Uses nlsw.dta & scheme vg past

0 .02 .04 .06 .08

Density

0 10 20 30

Tot. work exper.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

76 Chapter 2. Twoway graphs

twoway histogram ttl exp, start(-2.5) width(5)

0 .02 .04 .06 .08

Density

0 10 20 30

Tot. work exper.

We add the start() option to indicate

that we want the lower limit of the ﬁrst

bintostartat−2.5.

Uses nlsw.dta & scheme vg past

twoway histogram ttl exp, fraction width(1)

0 .02 .04 .06 .08 .1

Fraction

0 10 20 30

Tot. work exper.

If we use the fraction option, the

y-axis is scaled such that the height of

each bar is the probability of falling

within the range of x-values represented

by the bar. Thus, if we specify the

width of bars to be 1, the sum of the

heights of the bars is 1.

Uses nlsw.dta & scheme vg past

twoway histogram ttl exp, percent width(1)

0 2 4 6 8 10

Percent

0 10 20 30

Tot. work exper.

The percent option is similar to the

fraction option, except that the y-axis

is represented as a percentage instead

of a proportion. If we also specify a bar

width of 1, the sum of the heights of

the bars is 100%.

Uses nlsw.dta & scheme vg past

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.8 Distribution plots 77

twoway histogram ttl exp, frequency width(1)

The frequency option changes the

scaling of the y-axis to represent the

number of cases that fall within the

range of x-values represented by the

bar. If we specify a bar width of 1, the

sum of the heights of the bars equals

the number of nonmissing values for

ttl exp.

Uses nlsw.dta & scheme vg past

0 50 100 150 200

Frequency

0 10 20 30

Tot. work exper.

Let’s now consider options that control the width of the bars and other characteristics

of the bars, such as color. Then, we will show you how to display the graph as a horizontal

histogram and demonstrate options that allow you to treat varname as a discrete variable.

We will use the vg blue scheme for these graphs.

twoway histogram ttl exp, gap(20)

The gap() option speciﬁes the gap

between each of the bars. The gap is

created by reducing the width of the

bars. By default, the gap is 0, meaning

that the bars touch exactly and the

bars are reduced by 0%. Here, we

reduce the size of the bars by 20%,

making a small gap between the bars.

Uses nlsw.dta & scheme vg blue

.02

.04

.06

.08

Density

0 10 20 30

Tot. work exper.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

78 Chapter 2. Twoway graphs

twoway histogram ttl exp, gap(99.99)

.02

.04

.06

.08

Density

0 10 20 30

Tot. work exper.

Here, we reduce the size of the bars

99.99%, making the bars 0.01% of their

normal size.

Uses nlsw.dta & scheme vg blue

twoway histogram ttl exp, barwidth(.5)

.02

.04

.06

.08

Density

0 10 20 30

Tot. work exper.

Another way you can control the width

of the bars is though the barwidth()

option. Here, we indicate that we wish

each bar to be .5 x-units wide.

Uses nlsw.dta & scheme vg blue

twoway histogram ttl exp, bfcolor(olive teal) blcolor(teal)

blwidth(thick)

.02

.04

.06

.08

Density

0 10 20 30

Tot. work exper.

We use the bfcolor() (bar ﬁll color)

option to make the ﬁll color of the bar

olive–teal and the blcolor() (bar line

color) option to make the bar line color

teal. The blwidth() (bar line width)

option makes the line around the bar

thick. The section Styles : Colors (328)

shows more about colors, and

Styles : Linewidth (337) shows more

about line widths.

Uses nlsw.dta & scheme vg blue

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.8 Distribution plots 79

We will now brieﬂy consider some other options that can be used with twoway histogram,

showing how you can swap the position of the x-andy-axes, and the discrete option for

use with discrete variables. We will use the vg s1m scheme for the next set of graphs.

twoway histogram ttl exp, horizontal

We can use the horizontal option to

swap the position of ttl exp and its

density, making a horizontal display of

the histogram.

Uses nlsw.dta & scheme vg s1m

0 10 20 30

Tot. work exper.

0 .02 .04 .06 .08 .1

Density

twoway histogram grade, discrete

Here, we use the discrete option to

tell Stata that the variable grade is a

discrete variable and can take on only

integer values. In this example, each

bin has a width of 1, and the bars are

toonarrowtobeuseful.

Uses nlsw.dta & scheme vg s1m

0 .1 .2 .3 .4

Density

5 10 15 20

current grade completed

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

80 Chapter 2. Twoway graphs

twoway histogram grade, discrete width(2)

0 .05 .1 .15 .2 .25

Density

5 10 15 20

current grade completed

We add the width() option, and the

bars have a width of 2.

Uses nlsw.dta & scheme vg s1m

We will now consider kernel-density plots that can be created using twoway kdensity.

For more details, see [G]graph twoway kdensity. As with histograms, if you are not going

to combine the kernel-density plot with other twoway plots, and sometimes even when you

are, the kdensity command is preferable to twoway kdensity. We will explore a handful

of options that are useful for controlling the display of these graphs. These graphs will use

the vg s2c scheme.

twoway kdensity ttl exp

0 .02 .04 .06 .08

kdensity ttl_exp

0 10 20 30

Here is a kernel-density plot of total

work experience. We could have added

the horizontal option to display the

graph as a horizontal plot, but this

option is not shown.

Uses nlsw.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.8 Distribution plots 81

twoway kdensity ttl exp, biweight

By default, Stata uses a Epanechnikov

kernel for computing the density

estimates. Here, we use the biweight

option to use the biweight kernel for

computing the densities. Other

methods include cosine,gauss,

parzen,rectangle,andtriangle.

Uses nlsw.dta & scheme vg s2c

0 .02 .04 .06 .08 .1

kdensity ttl_exp

0 10 20 30

twoway kdensity ttl exp, range(0 40)

You can use the range() option to

specify the range of the x-values at

which the kernel density is computed

and displayed. Here, we expand the

rangetospanfrom0to40.

Uses nlsw.dta & scheme vg s2c

0 .02 .04 .06 .08

kdensity ttl_exp

0 10 20 30 40

twoway (histogram ttl exp, width(1) frequency)

(kdensity ttl exp, area(2246))

In this example, we overlay a histogram

of ttl exp, scaling the y-axis as the

frequency of values in each bin. We

overlay this with a kdensity plot but

want to scale the y-axis in a

commensurate manner. By using the

area() option, we can specify that the

sum of the area of the kernel density

should sum to 2246, the sample size.

Uses nlsw.dta & scheme vg s2c

0 50 100 150 200

0 10 20 30

Frequency kdensity ttl_exp, area=2246

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

82 Chapter 2. Twoway graphs

twoway kdensity ttl exp, clwidth(thick) clpattern(dash)

0 .02 .04 .06 .08

kdensity ttl_exp

0 10 20 30

We can use options such as clcolor(),

clwidth(),andclpattern() to alter

the characteristics of the line. Here, we

use the clwidth() and clpattern()

options to make the line thick and

dashed. See Styles : Linewidth (337),

Styles : Linepatterns (336), and

Styles : Colors (328) for more details.

Uses nlsw.dta & scheme vg s2c

twoway function y=normden(x), range(-4 4)

0 .1 .2 .3 .4

−4−2 0 2 4

We conclude by showing how you can

use twoway function to graph an

arbitrary function. We graph the

function y=normden(x) to show a

normal curve. We add the range(-4

4) to specify that we want the x-values

to range from −4 to 4. Otherwise, the

graph would show the x-values ranging

from 0 to 1.

Uses nlsw.dta & scheme vg s2c

2.9 Options

This section discusses the use of options with twoway, showing the types of options you

can use. For more details, see Options (235). This section uses the vg outm scheme for

displaying the graphs.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.9 Options 83

twoway scatter ownhome propval100

Consider this basic scatterplot. We will

use this for illustrating options.

Uses allstates.dta & scheme vg outm

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

twoway scatter ownhome propval100, msymbol(S)

We can use the msymbol() option to

control the marker symbols. Here, we

use squares as symbols. See

Options : Markers (235) for more details.

Uses allstates.dta & scheme vg outm

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

twoway scatter ownhome propval100, msymbol(S) mlabel(stateab)

We can use the mlabel() option to

control the marker labels. Here, we

label each of the markers with the

variable stateab showing the two-letter

abbreviation for each state next to each

marker. See Options : Marker labels

(247) for more information about

marker labels.

Uses allstates.dta & scheme vg outm

MS MO

ND OH

SD TN

UT VT

WV WI

40 50 60 70 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

84 Chapter 2. Twoway graphs

twoway scatter fv ownhome propval100, connect(l .) sort

40 50 60 70 80

0 20 40 60 80 100

% homes cost $100K+

yhat ownhome|propval100 % who own home

Say that we regressed ownhome on

propval100 and generated predicted

values named fv. Here, we make a

scatterplot and ﬁt line in the same

graph using the connect(l .) option

to connect the values of fv but not the

values of ownhome. We also add the

sort option, which is generally

recommended when using the

connect() option. See

Options : Connecting (250) for more

details.

Uses allstates.dta & scheme vg outm

twoway scatter propval100 rent700 ownhome,

xtitle(Percent of households that own their own home)

0 20 40 60 80 100

40 50 60 70 80

Percent of households that own their own home

% homes cost $100K+ % rents $700+/mo

We can add a title to the x-axis using

the xtitle() option, as illustrated

here. See Options : Axis titles (254) for

more details about titles.

Uses allstates.dta & scheme vg outm

twoway scatter propval100 rent700 ownhome, ylabel(0(10)100)

0 10 20 30 40 50 60 70 80 90 100

40 50 60 70 80

% who own home

% homes cost $100K+ % rents $700+/mo

We can label the y-axis from 0 to 100,

incrementing by 10, using the

ylabel(0(10)100) option as shown

here. See Options : Axis labels (256) for

more information about labeling axes.

Uses allstates.dta & scheme vg outm

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.9 Options 85

twoway scatter propval100 rent700 ownhome,

ylabel(0(10)100) yscale(alt)

Stata gives you a number of options

that you can use to control the axis

scale for both the x-andy-axes. For

example, here we use yscale(alt) to

move the y-axis to its alternate

position, moving it from the left to the

right. See Options : Axis scales (265) for

more details about the options for

controlling the axis scales.

Uses allstates.dta & scheme vg outm

0 10 20 30 40 50 60 70 80 90 100

40 50 60 70 80

% who own home

% homes cost $100K+ % rents $700+/mo

twoway (scatter propval100 ownhome)

(scatter rent700 ownhome, yaxis(2))

In this example, we show propval100

by ownhome and also rent700 by

ownhome, but for this second plot, we

put the y-axis on the second y-axis with

the yaxis(2) option. See

Options : Axis selection (269) for more

information about using and controlling

additional axes.

Uses allstates.dta & scheme vg outm

0 10 20 30 40

% rents $700+/mo

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home...

% homes cost $100K+ % rents $700+/mo

twoway scatter propval100 rent700 ownhome,

ylabel(0(10)100) yscale(alt) by(north)

The by() option allows you to see a

graph broken down by one or more

by() variables. Here, we show the

graph from above further broken down

by whether the state was part of the

North, making two graphs that are

combined together into a single graph.

The section Options : By (272) shows

more details and examples about the

use of the by() option.

Uses allstates.dta & scheme vg outm

0 10 20 30 40 50 60 70 80 90 100

50 60 70 80 50 60 70 80

S & W North

% homes cost $100K+ % rents $700+/mo

% who own home

Graphs by Region North or Not

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

86 Chapter 2. Twoway graphs

twoway scatter propval100 rent700 ownhome, legend(cols(1))

0 20 40 60 80 100

40 50 60 70 80

% who own home

% homes cost $100K+

% rents $700+/mo

The legend() option allows you to

control the contents and display of the

legend. Here, we use the

legend(cols(1)) option to indicate

that we want the legend to display as a

single column. See Options : Legend

(287) for more details about the

legend() option.

Uses allstates.dta & scheme vg outm

twoway scatter propval100 ownhome, text(62 45 "DC")

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

In this graph, there is a single

observation that stands out from the

rest. Rather than use the mlabel()

option to label all of the markers, we

may want to label just the outlying

point. Here, we use the text() option

to add the text DC at the (y,x)

coordinates of (62,45), in eﬀect labeling

that point; see Options : Adding text

(299) for more details.

Uses allstates.dta & scheme vg outm

twoway scatter propval100 ownhome,

title(This is a Title, box bfcolor(dimgray)

blcolor(black) blwidth(thick))

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

This is a Title Most items of text on a Stata graph

actually display within a box. We

illustrate this with the title() option

showing how we can place a box around

this text. We make the background

color of the box light gray and the

outline thick and black. These options

are described in more detail in

Options : Textboxes (303).

Uses allstates.dta & scheme vg outm

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.10 Overlaying plots 87

2.10 Overlaying plots

One of the terriﬁc features of twoway graphs is the ability to overlay them, giving you

the ﬂexibility to create more complex graphs. This section shows two strategies you can

use. The ﬁrst strategy is graphing multiple y-variables against a single x-variable in a sin-

gle twoway command. The second strategy is specifying multiple commands within a single

twoway command, thus overlaying these graphs atop each other. It is also possible to create

separate graphs and glue them together using the graph combine command, which is dis-

cussed in Appendix : Save/Redisplay/Combine (358). We ﬁrst start by illustrating how you

can specify multiple y-variables against a single x-variable using a single twoway command.

twoway scatter propval100 rent700 urban

We can use twoway scatter to graph

multiple y-variables against a single

x-variable in a single plot. Here, we

show propval100 and rent700 against

urban. Note that we are now using the

vg teal scheme.

Uses allstates.dta & scheme vg teal

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

twoway scatter propval100 rent700 urban, msymbol(Oh t)

The msymbol() option can be used to

select the marker symbols for the

multiple y-variables. Here, we plot the

variable propval100 with hollow

circles, and rent700 is plotted with

triangles.

Uses allstates.dta & scheme vg teal

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

88 Chapter 2. Twoway graphs

twoway scatter propval100 rent700 urban, mstyle(p2 p8)

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

The mstyle() (marker style) option

can be used to choose among marker

styles. These composite styles set the

symbol, size, ﬁll, color, outline color,

and outline width for the markers.

Uses allstates.dta & scheme vg teal

twoway line high low close tradeday, sort

1200

1250

1300

1350

1400

0 10 20 30 40

Trading day number

High price Low price

Closing price

We will brieﬂy switch to using the

spjanfeb2001 data ﬁle. You can also

graph multiple y-variables against a

single x-variable with a line graph.

This works with twoway line,as

illustrated here, as well as with twoway

connected and twoway tsline.

Uses spjanfeb2001.dta & scheme vg teal

twoway line high low close tradeday, sort clwidth(thick thick .)

1200

1250

1300

1350

1400

0 10 20 30 40

Trading day number

High price Low price

Closing price

Here, we use the clwidth() option to

change the width of the lines, making

the lines for the high and low prices

thick and leaving the line for the

closing price at the default width.

Uses spjanfeb2001.dta & scheme vg teal

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.10 Overlaying plots 89

twoway line high low close tradeday, sort clstyle(p1 p1 p2)

When we graph multiple y-variables, we

can use clstyle() (connect line style)

to control many characteristics of the

lines at once. Here, we plot the high

and low prices with the same style, p1,

and the closing price printed with a

second style, p2.

Uses spjanfeb2001.dta & scheme vg teal 1200

1250

1300

1350

1400

0 10 20 30 40

Trading day number

High price Low price

Closing price

twoway line high low close tradeday, sort clstyle(p1 p1 p2)

clwidth(thick thick .)

Here, we combine clstyle() and

clwidth() to make the lines for the

high and low prices the same style and

make them both thick. The third line is

drawn with the p2 style, and the

thickness is left at its default value.

Uses spjanfeb2001.dta & scheme vg teal

1200

1250

1300

1350

1400

0 10 20 30 40

Trading day number

High price Low price

Closing price

twoway (scatter propval100 urban) (lfit propval100 urban)

We return to the allstates data ﬁle.

We can overlay multiple twoway

graphs. Here, we show a common kind

of overlay: scatterplot overlaid with a

linear ﬁt between the two variables.

Note that both the scatter command

and the lfit command are surrounded

by parentheses.

Uses allstates.dta & scheme vg teal 0

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

90 Chapter 2. Twoway graphs

twoway (scatter propval100 urban) (lfit propval100 urban)

(qfit propval100 urban)

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Fitted values

Fitted values

We can add a quadratic ﬁt to the

previous graph by adding a qfit

command, so we can compare a linear

ﬁt and quadratic ﬁt to see if there are

nonlinearities in the ﬁt. Note that the

legend does not clearly diﬀerentiate

between the linear and quadratic ﬁt; we

will show you how to modify the legend

to label this more clearly below.

Uses allstates.dta & scheme vg teal

twoway (scatter propval100 urban, msymbol(Oh))

(lfit propval100 urban, clpattern(dash))

(qfit propval100 urban, clwidth(thick))

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Fitted values

Fitted values

We add the msymbol(Oh) option to the

scatter command, placing it after the

comma, as it normally would be placed,

but before the closing parenthesis that

indicates the end of the scatter

command. We also add the

clpattern(dash) option to the lfit

command to make the line dashed and

add the clwidth(thick) option to the

qfit command to make the line thick.

Uses allstates.dta & scheme vg teal

twoway (scatter propval100 urban) (lfit propval100 urban)

(qfit propval100 urban), legend(label(2 Linear Fit) label(3 Quad Fit))

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Linear Fit

Quad Fit

While each graph subcommand can

have its own options, some options can

apply to the entire graph. As

illustrated here, we add a legend to the

graph to clarify the diﬀerence in the ﬁt

values, and this option appears

following a comma after the closing

parenthesis following the qfit

command. The legend() option

appears at the end of the command

since it applies to the entire graph.

Uses allstates.dta & scheme vg teal

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.10 Overlaying plots 91

twoway (scatter propval100 urban) (lfit propval100 urban)

(qfit propval100 urban, legend(label(2 Linear Fit) label(3 Quad Fit)))

We can make the previous graph in a

diﬀerent, but less appropriate, way.

The legend() option is given as an

option of the qfit() command, not at

the very end as in the previous graph

command. But Stata is forgiving of

this, and even when such options are

inappropriately given within a

particular command, it treats them as

though they were given at the end of

the command.

Uses allstates.dta & scheme vg teal

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Linear Fit

Quad Fit

twoway (qfitci propval100 urban) (scatter propval100 urban)

Another common example of overlaying

graphs is to overlay a ﬁt line with

conﬁdence interval and a scatterplot.

Uses allstates.dta & scheme vg teal

100

20 40 60 80 100

Percent urban 1990

95% CI Fitted values

% homes cost $100K+

twoway (scatter propval100 urban) (qfitci propval100 urban)

However, note the order in which you

overlay these two kinds of graphs. In

this example, the qfitci was drawn

after the scatter, and as a result, the

points are obscured by the conﬁdence

interval.

Uses allstates.dta & scheme vg teal

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ 95% CI

Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

92 Chapter 2. Twoway graphs

twoway (rarea high low date) (spike volmil date)

500

1000

1500

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

High price/Low price Volume (millions)

We now switch to the sp2001ts data

ﬁle. Here, we overlay the high and low

closing prices with the volume of shares

sold. But, since both are placed on the

same y-axis, it is diﬃcult to see the

spikes of volmil, volume in millions.

Uses sp2001ts.dta & scheme vg teal

twoway (rarea high low date) (spike volmil date, yaxis(2)),

legend(span)

1.5

2.5

Volume (millions)

900

1000

1100

1200

1300

1400

High price/Low price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date...

High price/Low price Volume (millions)

By placing volmil on the second y-axis

using the yaxis(2) option, we can now

see the volume, but it obstructs the

stock prices. Note that we added the

option legend(span) to allow the

legend to be wider than the plot region

of the graph.

Uses sp2001ts.dta & scheme vg teal

twoway (rarea high low date) (spike volmil date, yaxis(2)),

legend(span) yscale(range(500 1400) axis(1)) yscale(range(0 5) axis(2))

1.5

2.5

Volume (millions)

900

1000

1100

1200

1300

1400

High price/Low price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date...

High price/Low price Volume (millions)

We use the yscale() option to modify

the range for the ﬁrst y-axis to lift its

range into the top third of the graph,

and another yscale() option to modify

the range for the second y-axis, pushing

the stock market volume down to the

bottom third.

Uses sp2001ts.dta & scheme vg teal

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

2.10 Overlaying plots 93

While the previous examples (and other examples in this book) have used the parenthet-

ical notation for overlaid graphs, Stata also permits double vertical bars (||) for separating

graphs. To illustrate this, some of the graphs from above will be repeated using this nota-

tion. These examples will be shown using the vg s2m scheme.

twoway scatter propval100 urban || lfit propval100 urban

We switch back to the allstates data

ﬁle. Here, the || notation is used to

separate the scatter command from

the lfit command.

Uses allstates.dta & scheme vg s2m

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Fitted values

twoway scatter propval100 urban || lfit propval100 urban ||

qfit propval100 urban

Here, we create three overlaid graphs

using the || notation.

Uses allstates.dta & scheme vg s2m

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Fitted values

Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Scatter Fit CI ﬁt Line Area Bar Range Distribution Options Overlaying

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

94 Chapter 2. Twoway graphs

twoway scatter propval100 urban, msymbol(Oh) ||

lfit propval100 urban, clwidth(thick) ||

qfit propval100 urban, clwidth(medium)

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Fitted values

Fitted values

This example shows how to use the ||

notation with options for each of the

commands.

Uses allstates.dta & scheme vg s2m

twoway scatter propval100 urban, msymbol(Oh) ||

lfit propval100 urban, clwidth(thick) ||

qfit propval100 urban, clwidth(medium) ||,

legend(label(2 Linear Fit) label(3 Quad Fit))

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ Linear Fit

Quad Fit

This is another example using the ||

notation, in this case illustrating how to

have options on each of the commands,

along with the legend() as an overall

option.

Uses allstates.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

3 Scatterplot matrix graphs

This chapter will explore the use of the graph matrix command for creating scatterplot

matrices among two or more variables. Many of the options that you can use with graph

twoway scatter apply to these kinds of graphs, as well; see Twoway : Scatter (35) and

Options (235) for related information. This chapter illustrates the use of marker options

and marker labels, as well as options for controlling the display of axes. It also includes

options speciﬁc to the graph matrix command, as well as the use of the by() option. For

more details about scatterplot matrices, see [G]graph matrix.

3.1 Marker options

This section looks at controlling and labeling the markers in scatterplot matrices. This

section will show how to change the marker symbol, size, and color (both ﬁll and out-

line color) and how to label the markers. You can label markers using the graph matrix

command just as you could when using the graph twoway scatter command. See also

Options : Markers (235) and Options : Marker labels (247) for more details. These examples

will use the vg s1m scheme.

graph matrix propval100 ownhome borninstate, msymbol(Oh)

You can control the marker symbol

with the msymbol() (marker symbol)

option. Here, we make the symbols

hollow circles. Other values that we

could specify include D(diamond), T

(triangle), S(square), and X(x). Using

a lowercase letter (dinstead of D)

makes the symbol smaller. For circles,

diamonds, triangles, and squares, you

can append an h(e.g., Oh) to indicate

that the symbol should be hollow; see

Styles : Symbols (342) for more

examples.

Uses allstates.dta & scheme vg s1m

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

40 60 80

20 40 60 80

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Marker options Axes Matrix options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

96 Chapter 3. Scatterplot matrix graphs

graph matrix heatdd cooldd tempjan tempjuly, msymbol(p)

Heating

degree

days

Cooling

degree

days

Average

January

temperature

Average

July

temperature

5000

10000

0 5000 10000

2000

4000

0 2000 4000

100

0 50 100

100

60 80 100 When you have a large number of

observations, the msymbol(p) option

can be very useful since it displays a

very small point for each observation

and can help you to see the overall

relationships among the variables.

Here, we switch to the citytemp data

ﬁle to illustrate this.

Uses citytemp.dta & scheme vg s1m

graph matrix propval100 ownhome borninstate, msize(vlarge)

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

40 60 80

20 40 60 80

The size of the markers can be changed

using the msize() (marker size) option.

Here, we make the markers very large.

Other values we could have chosen

include vtiny,tiny,vsmall,small,

medsmall,medium,medlarge,large,

vlarge,huge,vhuge,andehuge;see

Styles : Markersize (340) for more details.

We also could have speciﬁed the size as

a multiple of the original size of the

marker; e.g., msize(*2) makes the

marker twice as big.

Uses allstates.dta & scheme vg s1m

graph matrix propval100 ownhome borninstate, mcolor(gs8)

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

40 60 80

20 40 60 80

The mcolor() (marker color) option

can be used to control the color of the

symbols. Among the colors you can

choose are 16 gray-scale colors named

gs0 (black) to gs16 (white). We show a

graph using symbols that are in the

middle of this scale using the

mcolor(gs8) option; see Styles : Colors

(328) for more information about

specifying colors.

Uses allstates.dta & scheme vg s1m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

3.1 Marker options 97

graph matrix propval100 ownhome borninstate, msize(vlarge)

mfcolor(gs13) mlcolor(gs0)

The mfcolor() (marker ﬁll color) and

mlcolor() (marker line color) options

allow you to control the ﬁll color (inside

color) and outline color (periphery

color) of the markers. Below, we make

the ﬁll color light gray by specifying

mfcolor(gs13) and the line color black

by specifying mlcolor(gs0).Weuse

the msize() option to make the

markers very large to help see the eﬀect

of these options.

Uses allstates.dta & scheme vg s1m

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

40 60 80

20 40 60 80

graph matrix propval100 ownhome borninstate, mlabel(stateab)

We can label the markers using the

mlabel() (marker label) option. In this

example, we label the markers with the

two-letter postal abbreviation by

supplying the option mlabel(stateab).

Even though many of the labels

overlap, the most interesting

observations are those that stand out

and have readable labels, such as DC

and NV. For additional details, see

Options : Marker labels (247).

Uses allstates.dta & scheme vg s1m

AK AZ

OR PA

TX UT

WY AL

FL GA

IN IA

KS KY

MT NE

OR PA

CO CT

ID IL

NH NJ

NDOH

SDTN

UT VT

WY AL

AZ AR

CO CT

FL GA

ID IL

IN IA

KS KY

MT NE

NH NJ

NDOH

SDTN

AK AZ

CA CO

CTDE

HI ID

IL IN

NY NC

RI SC

TX UT

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

40 60 80

20 40 60 80

graph matrix propval100 ownhome borninstate, mlabel(stateab)

mlabsize(large)

You can use the mlabsize() (marker

label size) option to control the size of

the marker label. Here, we indicate

that the marker labels should be large.

You can also specify the size of the

marker label as a multiple of the

original size of the marker label; e.g.,

specifying mlabsize(*1.5) would make

the labels 1.5 times their normal size.

Uses allstates.dta & scheme vg s1m

AK AZ

OR PA

TX UT

WY AL

FL GA

IN IA

KS KY

MT NE

NH NJ

OR PA

CO CT

ID IL

MD MA

NHNJ

UT VT

WY AL

AZ AR

CO CT

FL GA

ID IL

IN IA

KS KY

MD MA

MT NE

NH NJ

GA HI

AK AZ

CA CO

DC FL

HI ID

ILIN

MA MI

NY NC

RI SC

TX UT

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

40 60 80

20 40 60 80

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Marker options Axes Matrix options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

98 Chapter 3. Scatterplot matrix graphs

3.2 Controlling axes

This section looks at labeling axes in scatterplot matrices. It shows how to label axes of

scatterplots, control the scale of axes, and insert titles along the diagonal. For more details,

see Options : Axis labels (256), Options : Axis scales (265) and [G]axis options. This section

uses the vg s2c scheme.

graph matrix urban propval100 borninstate

Percent

urban

1990

% homes

cost

$100K+

% born in

state of

residence

100

40 60 80 100

100

0 50 100

20 40 60 80

Let’s look at a scatterplot matrix of

three variables: urban,propval100,

and borninstate.

Uses allstates.dta & scheme vg s2c

graph matrix urban propval100 borninstate,

xlabel(30(10)100, axis(1)) ylabel(30(10)100, axis(1))

Percent

urban

1990

% homes

cost

$100K+

% born in

state of

residence

100

30 40 50 60 70 80 90 100

100

0 50 100

20 40 60 80

The way you control the axis labels

with a scatterplot matrix is somewhat

diﬀerent than with other kinds of

graphs. Here, we use the xlabel() and

ylabel() options to control the x-and

y-labels for the ﬁrst variable, urban,to

be scaled 30 to 100 in increments of 10.

This applies to the ﬁrst variable

because we speciﬁed the axis(1)

option.

Uses allstates.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

3.2 Controlling axes 99

graph matrix urban propval100 borninstate,

xlabel(0(20)100, axis(2)) ylabel(0(20)100, axis(2))

We can change the label for the second

variable, propval100, in a similar

manner, but we need to specify

axis(2). In this example, we label the

second variable ranging from 0 to 100

in increments of 20.

Uses allstates.dta & scheme vg s2c

Percent

urban

1990

% homes

cost

$100K+

% born in

state of

residence

100

40 60 80 100

100

0 20 40 60 80 100

20 40 60 80

graph matrix urban propval100 borninstate,

xlabel(0(20)100, axis(1)) ylabel(0(20)100, axis(1))

xlabel(0(20)100, axis(2)) ylabel(0(20)100, axis(2))

xlabel(0(20)100, axis(3)) ylabel(0(20) 100, axis(3))

Let’s label all these variables using the

same scale, from 0 to 100 in increments

of 20. As you can see, this involves

quite a bit of typing, applying the

xlabel() and ylabel() for axis(1),

axis(2),andaxis(3), which applies

this to the ﬁrst, second, and third

variables. However, the next example

shows a more eﬃcient way to do this.

Uses allstates.dta & scheme vg s2c

Percent

urban

1990

% homes

cost

$100K+

% born in

state of

residence

100

0 20 40 60 80 100

100

0 20 40 60 80 100

100

0 20 40 60 80 100

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Marker options Axes Matrix options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

100 Chapter 3. Scatterplot matrix graphs

graph matrix urban propval100 borninstate,

maxes(xlabel(0(20)100) ylabel(0(20)100))

Percent

urban

1990

% homes

cost

$100K+

% born in

state of

residence

100

0 20 40 60 80 100

100

0 20 40 60 80 100

100

0 20 40 60 80 100

Stata has a simpler way of applying the

same labels to all the variables in the

scatterplot matrix by using the maxes()

(multiple axes) option. This example

labels the x-andy-axes from 0 to 100

with increments of 20 for all variables.

Uses allstates.dta & scheme vg s2c

graph matrix urban propval100 borninstate,

maxes(xlabel(0(20)100) ylabel(0(20)100))

xlabel(20(20)100, axis(1)) ylabel(20(20)100, axis(1))

Percent

urban

1990

% homes

cost

$100K+

% born in

state of

residence

100

20 40 60 80 100

100

0 20 40 60 80 100

100

0 20 40 60 80 100

You might want to label most of the

variables in the scatterplot matrix the

same way but with one or more

exceptions in a diﬀerent way. In this

example, we label all the variables from

0 to 100, incrementing by 20, but then

override the labeling for urban to make

it 20 to 100, incrementing by 20. We do

this by adding additional xlabel() and

ylabel() options that apply just for

axis(1).

Uses allstates.dta & scheme vg s2c

graph matrix urban propval100 borninstate,

maxes(xlabel(0(20)100) ylabel(0(20)100) xtick(0(10)100) ytick(0(10)100))

Percent

urban

1990

% homes

cost

$100K+

% born in

state of

residence

100

0 20 40 60 80 100

100

0 20 40 60 80 100

100

0 20 40 60 80 100

Here, we label all of the variables from

0 to 100, in increments of 20, and also

add ticks from 0 to 100, in increments

of 10. Note that the xtick() and

ytick() options work the same way as

the xlabel() and ylabel() options.

We place these options within the

maxes() option, and they apply to all

of the axes. See Options : Axis labels

(256) and Options : Axis scales (265) for

more details.

Uses allstates.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

3.2 Controlling axes 101

graph matrix urban propval100 borninstate,

diagonal("% Urban" "% Homes Over $100K" "% Born in State")

When you use twoway scatter,you

can use xtitle() and ytitle() to

control the titles for the axes. By

contrast, when using graph matrix,

you can control the titles that are

displayed along the diagonal with the

diagonal() option. We use the

diagonal() option to change the titles

for all variables.

Uses allstates.dta & scheme vg s2c

Urban

% Homes

Over

$100K

% Born

State

100

40 60 80 100

100

0 50 100

20 40 60 80

graph matrix urban propval100 borninstate,

diagonal("% Urban" ."% Born in State")

We do not have to change all the titles.

If we want to change just some of the

titles, we can place a period (.) for the

labels where we want the label to stay

the same. In this example, we change

the titles for the ﬁrst and third

variables but leave the second as is.

Uses allstates.dta & scheme vg s2c

Urban

% homes

cost

$100K+

% Born

State

100

40 60 80 100

100

0 50 100

20 40 60 80

graph matrix urban propval100 borninstate,

diagonal("% Urban" . "% Born in State", bfcolor(eggshell))

We can control the display of the text

on the diagonal using textbox options.

For example, we make the background

color of the text area eggshell using the

bfcolor(eggshell) option. See

Options : Textboxes (303) for more

examples of textbox options.

Uses allstates.dta & scheme vg s2c

Urban

% homes

cost

$100K+

% Born

State

100

40 60 80 100

100

0 50 100

20 40 60 80

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Marker options Axes Matrix options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

102 Chapter 3. Scatterplot matrix graphs

3.3 Matrix options

This section shows options that you can use to control the look of the scatterplot matrix,

including showing just the lower half of the matrix, jittering markers, and scaling the size

of marker text. For more details, see [G]graph matrix. These graphs use the vg s2m

scheme.

graph matrix propval100 ownhome region, half

% homes

cost

$100K+

% who

own

home

Census

region

0 50 100

40 60 80

You can use the half option to display

just the lower diagonal of the

scatterplot matrix.

Uses allstates.dta & scheme vg s2m

graph matrix propval100 ownhome region, jitter(3)

% homes

cost

$100K+

% who

own

home

Census

region

100

0 50 100

40 60 80

1 2 3 4

You can use the jitter() option to

add random noise to the points; the

higher the value given, the more

random noise is added. This is

especially useful when numerous

observations have the same (x,y)

values, so a number of observations can

appear as a single point.

Uses allstates.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

3.4 Graphing by groups 103

graph matrix propval100 ownhome region, scale(1.5)

The scale() option can be used to

magnify the contents of the graph,

including the markers, labels, and lines,

but not the overall size of the graph.

Here, we increase the size of these

items, making them 1.5 times their

normal size. Note that, unlike other

similar options, this option does not

take an asterisk preceding the

multiplier; i.e., we specify 1.5 but not

*1.5.

Uses allstates.dta & scheme vg s2m

% homes

cost

$100K+

% who

own

home

Census

region

100

0 50 100

40 60 80

1 2 3 4

3.4 Graphing by groups

This section looks at the use of the by() option for showing separate graphs based on the

levels of a by() variable. For more information, see Options : By (272) and [G]by option.

This section uses the vg brite scheme.

graph matrix propval100 ownhome borninstate, by(north)

The by() option can be used with

graph matrix to show separate

scatterplot matrices by a particular

variable. Here, we show separate

scatterplot matrices for households in

northern states and non-northern

states.

Uses allstates.dta & scheme vg brite

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

50 60 70 80

20 40 60 80

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

60 70 80

40 60 80

S & W North

Graphs by Region North or Not

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Marker options Axes Matrix options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

104 Chapter 3. Scatterplot matrix graphs

graph matrix propval100 ownhome borninstate, by(north, compact)

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

50 60 70 80

20 40 60 80

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

100

0 50 100

60 70 80

40 60 80

S & W North

Graphs by Region North or Not

To display the graphs closer together,

you can use the compact option.

Uses allstates.dta & scheme vg brite

twoway scatter propval100 ownhome, by(north, compact)

0 50 100

50 60 70 8050 60 70 80

S & W North

% homes cost $100K+

% who own home

Graphs by Region North or Not

If we compare the previous scatterplot

matrix to this twoway scatterplot, we

see that the compact option does not

make the scatterplot matrix as compact

as it does with a regular twoway

scatter command, which joins the two

graphs on their edges by omitting the

y-labels between the two graphs.

Uses allstates.dta & scheme vg brite

graph matrix propval100 ownhome borninstate, by(north, compact)

maxes(ylabel(, nolabels))

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

0 50 100

50 60 70 80

20 40 60 80

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

0 50 100

60 70 80

40 60 80

S & W North

Graphs by Region North or Not

We can make the graph matrix display

more compactly with the by() option

by using the maxes(ylabel(,

nolabels)) option to suppress the

labels on all of the y-axes. Then, when

we use the compact option, the edges of

the plots are pushed closer together.

Uses allstates.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

3.4 Graphing by groups 105

graph matrix propval100 ownhome borninstate, by(north, compact scale(*1.3))

maxes(ylabel(, nolabels))

We can use the scale() option to

increase the size of the markers, labels,

and text to make them more readable.

This is especially useful when graphs

get small.

Uses allstates.dta & scheme vg brite

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

0 50 100

50 60 70 80

20 40 60 80

% homes

cost

$100K+

% who

own

home

% born in

state of

residence

0 50 100

60 70 80

40 60 80

S & W North

Graphs by Region North or Not

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Marker options Axes Matrix options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4 Bar graphs

This chapter will explore how to create bar charts using the graph bar command. It

will show how you can use graph bar to graph one or more continuous y-variables and how

you can break them down by one or more categorical variables. In addition, this chapter

will illustrate how you can control the display of each of the axes, the legend, and the look of

the bars, and how to use the by() option. We will start this chapter by looking at features

related to graphing one or more y-variables. For this entire chapter, we will use the nlsw

data ﬁle.

4.1 Y-variables

A bar chart graphs one or more continuous variables broken down by one or more cat-

egorical variables. The continuous variables are graphed on the y-axis and are referred to

as y-variables. This section shows you how to specify the y-variables using the graph bar

command, how to include one or more y-variables, and how to obtain diﬀerent summary

statistics for the y-variables. For more information, see [G]graph bar. This section begins

using the vg past scheme.

graph bar ttl exp

This is probably the most basic bar

chart that you can make (and perhaps

the most boring, as well). It shows the

average total work experience for all

observations in the ﬁle. It graphs a

single y-variable using the default

summary statistic, the mean.

Uses nlsw.dta & scheme vg past

0 5 10 15

mean of ttl_exp

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

107

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

108 Chapter 4. Bar graphs

graph bar prev exp tenure ttl exp

0 5 10 15

mean of prev_exp mean of tenure

mean of ttl_exp

You can specify multiple y-variables to

be plotted at one time. Here, we graph

the mean of previous, current, and total

work experience in the same plot. The

bars are plotted touching each other,

and a legend indicates which bar

corresponds to which variable.

Uses nlsw.dta & scheme vg past

graph bar (median) prev exp tenure ttl exp

0 5 10 15

p 50 of prev_exp p 50 of tenure

p 50 of ttl_exp

This graph is much like the last one,

but it shows the median of these

y-variables. Note that we only speciﬁed

(median) before prev exp but it

applied to all the y-variables that

follow. You can summarize the

y-variables using any of the summary

statistics permitted by the collapse

command (e.g., mean,sd,sum,median,

and p10); see [R]collapse.

Uses nlsw.dta & scheme vg past

graph bar (median) prev exp tenure (mean) ttl exp

0 5 10 15

p 50 of prev_exp p 50 of tenure

mean of ttl_exp

In this example, we get the median of

the ﬁrst two y-variables and then the

mean of the last y-variable. I don’t

know, however, how often you would do

this.

Uses nlsw.dta & scheme vg past

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.1 Y-variables 109

graph bar (mean) meanwage=wage (median) medwage=wage

You can plot diﬀerent summary

statistics for the same y-variable, but

you must specify a target name for the

statistic being created. Here, we create

meanwage for the mean of wage and

medwage for the median of wage.Ifwe

omitted the meanwage= and medwage=

from this command, Stata would return

an error indicating that the name for

the mean of wage conﬂicts with the

median of wage.

Uses nlsw.dta & scheme vg past

0 2 4 6 8

mean of wage p 50 of wage

We now consider a handful of options that are useful when you have multiple y-variables.

These options allow you to display the y-variables as though they were categories of the

same variable, to create stacked bar charts, and to display the y-variables as percentages of

the total y-variables. These options are illustrated in the following graphs using the vg s1m

scheme.

graph bar prev exp tenure ttl exp hours

First, consider this bar chart showing

four y-variables. Each y-variable is

shown with a diﬀerent colored bar and

with a legend indicating which

y-variable corresponds to which bar.

See the next example for another way

to diﬀerentiate these four bars.

Uses nlsw.dta & scheme vg s1m

0 10 20 30 40

mean of prev_exp mean of tenure

mean of ttl_exp mean of hours

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

110 Chapter 4. Bar graphs

graph bar prev exp tenure ttl exp hours, ascategory

0 10 20 30 40

mean of prev_exp mean of tenure mean of ttl_exp mean of hours

You can use the ascategory option to

indicate that you want Stata to graph

multiple y-variables using the style that

would be used for the levels of an

over() variable. Comparing this graph

with the previous graph, note how the

bars for the diﬀerent variables are the

same color and labeled on the x-axis

rather than using a legend.

Uses nlsw.dta & scheme vg s1m

graph bar prev exp tenure, over(occ5)

0 2 4 6 8

Prof/Mgmt Sales Clerical Labor/Ops Other

mean of prev_exp mean of tenure

Consider this graph, where we show

work experience prior to one’s current

job (prev exp) and work experience at

one’s current job (tenure)brokendown

by occ5. The total of previous and

current work experience represents

total work experience, and you might

want to show each bar as a percent of

total work experience. The next

example shows how you can do that.

Uses nlsw.dta & scheme vg s1m

graph bar prev exp tenure, over(occ5) percentages

0 20 40 60 80

percent

Prof/Mgmt Sales Clerical Labor/Ops Other

mean of prev_exp mean of tenure

Here, we show the time worked before

one’s current job, prev exp, and time

at the current job, tenure, in terms of

their percentage of the total (i.e.,

percentage of total work experience).

We can view the bars in this way using

the percentages option.

Uses nlsw.dta & scheme vg s1m

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.2 Graphing bars over groups 111

graph bar prev exp tenure, over(occ5) stack

The stack option shows the y-variables

as a stacked bar chart. This allows you

to see the mean of each y-variable, as

well as the mean of the total

y-variables.

Uses nlsw.dta & scheme vg s1m

0 5 10 15

Prof/Mgmt Sales Clerical Labor/Ops Other

mean of prev_exp mean of tenure

graph bar prev exp tenure, over(occ5) percentages stack

We can also combine the stack and

percentages options to create a

stacked bar chart in terms of

percentages.

Uses nlsw.dta & scheme vg s1m

0 20 40 60 80 100

percent

Prof/Mgmt Sales Clerical Labor/Ops Other

mean of prev_exp mean of tenure

4.2 Graphing bars over groups

This section focuses on the use of the over() option for showing bar charts by one

or more categorical variables. It illustrates the use of the over() option with a single

y-variable and with multiple y-variables. We also look at some basic options, including

options for displaying the over() variable as though its levels were multiple y-variables,

including missing values on the over() variable, and suppressing empty combinations of

multiple over() variables. See the group options and over subopts tables of [G]graph bar

for more details.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

112 Chapter 4. Bar graphs

graph hbar wage, over(occ5)

0 2 4 6 8 10

mean of wage

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Here, we use the over() option to show

the average wages broken down by

occupation. Note that we are using

graph hbar to produce horizontal,

rather than vertical, bar charts.

Uses nlsw.dta & scheme vg brite

graph hbar wage, over(occ5) over(collgrad)

0 5 10 15

mean of wage

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Here, we use the over() option twice to

show the wages broken down by

occupation and whether one graduated

college. Note the appropriate way to

produce this graph is to use two over()

options, rather than using a single

over() option with two variables. As

we will see later, each over() can have

its own options, allowing you to

customize the display of each over()

variable.

Uses nlsw.dta & scheme vg brite

graph hbar wage, over(urban2) over(occ5) over(collgrad)

0 5 10 15

mean of wage

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Rural Metro

We can even add a third over()

option, in this case using over(urban2)

to compare those living in rural versus

urban areas. Note the change in the

look of the graph when we add the

third over() variable. This is because

Stata is now treating the ﬁrst over()

variable as though it were multiple

y-variables. Because of this, you can

only specify one y-variable when you

have three over() options.

Uses nlsw.dta & scheme vg brite

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.2 Graphing bars over groups 113

Now, let’s look at examples of using multiple y-variables with the over() option. We

ﬁrst consider a simple bar graph with multiple y-variables. These examples will use the

vg lgndc scheme, which places the legend to the left of the graph and displays it in a sin-

gle, stacked column.

graph hbar prev exp tenure ttl exp

This graph shows the overall mean of

previous, current, and total work

experience.

Uses nlsw.dta & scheme vg lgndc

0 5 10 15

mean of prev_exp

mean of tenure

mean of ttl_exp

graph hbar prev exp tenure ttl exp, over(occ5)

We can take the graph from above and

break the means down by whether one

graduated from college by adding the

over(occ5) option.

Uses nlsw.dta & scheme vg lgndc

0 5 10 15

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

mean of prev_exp

mean of tenure

mean of ttl_exp

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

114 Chapter 4. Bar graphs

graph hbar prev exp tenure ttl exp, over(occ5) over(union)

0 5 10 15

union

nonunion

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

mean of prev_exp

mean of tenure

mean of ttl_exp

We can take the previous graph and

further break the results down by

whether one belongs to a union. Note,

however, that we cannot add a third

over() option when we have multiple

y-variables.

Uses nlsw.dta & scheme vg lgndc

Now let’s consider options that may be used in combination with the over() option to

customize the behavior of the graphs. We show how you can treat the levels of the variable

in the ﬁrst over() option as though they were multiple y-variables and can even graph

those levels as percentages or stacked bar charts. You can also request that missing values

for the levels of the over() variables be displayed, and you can suppress empty levels when

multiple over() options are used. These examples are shown below using the vg rose

scheme.

graph bar wage, over(occ5) over(union)

mean of wage

nonunion union

Prof/MgmtSalesClericalLabor/OpsOther Prof/MgmtSalesClericalLabor/OpsOther

Consider this graph, where we show

wages broken down by occupation and

whether one belongs to a union. The

labels for the levels of occ5 overlap, but

this is mended in the next example.

Uses nlsw.dta & scheme vg rose

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.2 Graphing bars over groups 115

graph bar wage, over(occ5) over(union) asyvars

If we add the asyvars option, then the

ﬁrst over() variable (occ5) is graphed

as if there were ﬁve y-variables

corresponding to the ﬁve levels of occ5.

The levels of occ5 are shown as

diﬀerently colored bars pushed next to

each other and labeled using the

legend.

Uses nlsw.dta & scheme vg rose 0

mean of wage

nonunion union

Prof/Mgmt Sales

Clerical Labor/Ops

Other

graph bar wage, over(occ5) over(union) asyvars percentages

With the levels of occ5 considered as

y-variables, we can use some of the

options that apply when we have

multiple y-variables. Here, we request

that the values be plotted as

percentages.

Uses nlsw.dta & scheme vg rose

percent of mean of wage

nonunion union

Prof/Mgmt Sales

Clerical Labor/Ops

Other

graph bar wage, over(occ5) over(union) asyvars percentages stack

Again, because we are treating the

levels of occ5 as though they were

multiple y-variables, we can add the

stack option to view the graph as a

stacked bar chart.

Uses nlsw.dta & scheme vg rose

100

percent of mean of wage

nonunion union

Prof/Mgmt Sales

Clerical Labor/Ops

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

116 Chapter 4. Bar graphs

graph hbar wage, over(urban3) over(union)

0 2 4 6 8 10

mean of wage

union

nonunion

Urban

Suburb

Rural

Urban

Suburb

Rural

Consider this graph, where we use the

over(union) option to compare the

mean wages of union workers with

nonunion workers. One aspect this

graph hides is that there are a number

of missing values on the variable union.

Uses nlsw.dta & scheme vg rose

graph hbar wage, over(urban3) over(union) missing

0 2 4 6 8 10

mean of wage

union

nonunion

Urban

Suburb

Rural

Urban

Suburb

Rural

Urban

Suburb

Rural

By adding the missing option, we then

see a category for those who are

missing on the union variable, shown as

the third set of bars. The label for this

bar is a single dot, which is the Stata

indicator of missing values. The section

Bar : Cat axis (123) shows how you can

give this bar a more meaningful label.

Uses nlsw.dta & scheme vg rose

graph bar wage, over(grade) over(collgrad)

mean of wage

not college grad college grad

4 5 6 7 8 9 101112131415161718 4 5 6 7 8 9 101112131415161718

Consider this bar chart, which breaks

wages down by two variables: the last

grade that one completed and whether

one is a college graduate. By default,

Stata shows all possible combinations

for these two variables. In most cases,

all combinations are possible, but not

in this case.

Uses nlsw.dta & scheme vg rose

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.3 Options for groups, over options 117

graph bar wage, over(grade) over(collgrad) nofill

If you only want to display the

combinations of the over() variables

that exist in the data, use the nofill

option.

Uses nlsw.dta & scheme vg rose

mean of wage

not college grad college grad

4 5 6 7 8 9 10 11 12 13 14 15 13 14 15 16 17 18

4.3 Options for groups, over options

This section considers some of the options that can be used with the over() and

yvaroptions() options for customizing the display of the bars. We will focus on controlling

the spacing between the bars and the order in which the bars are displayed. Other options

that control the display of the x-axis (such as the labels) are covered in Bar : Cat axis (123).

For more information on the over() options covered in this section, see the over subopts

table in [G]graph bar.

We ﬁrst consider options that control the spacing among the bars and switch to the

vg s2m scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

118 Chapter 4. Bar graphs

graph hbar wage, over(grade4) over(union)

0 2 4 6 8 10

mean of wage

union

nonunion

Coll Grad

Some Coll

HS Grad

Not HS

Coll Grad

Some Coll

HS Grad

Not HS

Consider this graph, where we show the

mean wages broken down by grade4

and union.Usinggraph hbar displays

the chart as a horizontal bar chart,

which can be useful when you have

many categories to compare.

Uses nlsw.dta & scheme vg s2m

graph hbar wage, over(grade4, gap(*3)) over(union)

0 2 4 6 8 10

mean of wage

union

nonunion

Coll Grad

Some Coll

HS Grad

Not HS

Coll Grad

Some Coll

HS Grad

Not HS

We can change the gap between the

levels of grade4. Here, we make that

gap three times as large as it normally

would have been. This leads to thinner

bars with a greater gap between them.

Uses nlsw.dta & scheme vg s2m

graph hbar wage, over(grade4, gap(*.3)) over(union)

0 2 4 6 8 10

mean of wage

union

nonunion

Coll Grad

Some Coll

HS Grad

Not HS

Coll Grad

Some Coll

HS Grad

Not HS

Here, we shrink the gap between the

levels of grade4, making the gaps 30%

of the size they normally would have

been. This leads to wider bars with a

smaller gap between them.

Uses nlsw.dta & scheme vg s2m

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.3 Options for groups, over options 119

graph hbar wage, over(grade4, gap(*.2)) over(union, gap(*3))

We can control the gap with respect to

each of the over() variables at the

same time. In this example, we make

the gap among the grade4 categories

smaller (20% their original size) and

the gap between the levels of union

larger (three times the normal size).

Uses nlsw.dta & scheme vg s2m

0 2 4 6 8 10

mean of wage

union

nonunion

Coll Grad

Some Coll

HS Grad

Not HS

Coll Grad

Some Coll

HS Grad

Not HS

So far, we have let Stata control the order in which the bars are displayed. By default,

the bars formed by over() variables are ordered in ascending sequence according to the

values of the over() variable. However, Stata gives you considerable ﬂexibility in the or-

dering of the bars, as illustrated in the following examples using the vg s2c scheme.

graph hbar wage, over(occ7, descending)

Consider this graph showing average

wages broken down by the seven levels

of occupation. The bars are normally

ordered by the levels of occ7, going

from 1 to 7, where 1 is Prof and 7 is

Other. Using the descending option

switches the order of the bars. They

still are ordered according to the seven

levels of occupation, but the bars are

ordered going from 7 to 1.

Uses nlsw.dta & scheme vg s2c 0 2 4 6 8 10

mean of wage

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

120 Chapter 4. Bar graphs

graph hbar wage, over(occ7, sort(occ7alpha))

0 2 4 6 8 10

mean of wage

Other

Sales

Prof

Operat.

Mgmt

Labor

Cler.

We might want to put these bars in

alphabetical order (but with Other still

appearing last). We can do this by

recoding occ7 into a new variable (say

occ7alpha) such that as occ7alpha

goes from 1 to 7, the occupations are

alphabetical. We recoded occ7 with

these assignments: 4 = 1, 6 = 2, 2 = 3,

5=4,1=5,3=6,and7=7;see

[R]recode. Then, the

sort(occ7alpha) option has the eﬀect

of alphabetizing the bars.

Uses nlsw.dta & scheme vg s2c

graph hbar wage, over(occ7, sort(1))

0 2 4 6 8 10

mean of wage

Mgmt

Prof

Cler.

Other

Sales

Operat.

Labor

Here, we sort the variables on the

height of the bars (in ascending order).

The sort(1) means to sort the bars

according to the height of the ﬁrst

y-variable, in this case, the mean of

wage.

Uses nlsw.dta & scheme vg s2c

graph hbar wage, over(occ7, sort(1) descending)

0 2 4 6 8 10

mean of wage

Labor

Operat.

Sales

Other

Cler.

Prof

Mgmt

Adding the descending option yields

bars in descending order.

Uses nlsw.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.3 Options for groups, over options 121

graph hbar wage hours, over(occ7, sort(1))

Here, we plot two y-variables. In

addition to wages, we also show the

average hours worked per week.

Including the sort(1) option sorts the

bars according to the mean of wage

since that is the ﬁrst y-variable.

Uses nlsw.dta & scheme vg s2c

0 10 20 30 40

Mgmt

Prof

Cler.

Other

Sales

Operat.

Labor

mean of wage mean of hours

graph hbar wage hours, over(occ7, sort(2))

Changing sort(1) to sort(2) sorts the

bars according to the second y-variable,

the mean of hours.

Uses nlsw.dta & scheme vg s2c

0 10 20 30 40

Mgmt

Operat.

Other

Prof

Sales

Cler.

Labor

mean of wage mean of hours

graph hbar wage hours, over(occ7, sort(2)) over(married)

We can use the sort() option when

there are additional over() variables.

Here, the sort(2) option orders the

bars according to the mean number of

hours worked within each level of

married.

Uses nlsw.dta & scheme vg s2c

0 10 20 30 40 50

married

single

Mgmt

Operat.

Other

Prof

Sales

Cler.

Labor

Mgmt

Prof

Other

Operat.

Sales

Cler.

Labor

mean of wage mean of hours

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

122 Chapter 4. Bar graphs

graph hbar wage hours, over(occ7, sort(2)) over(married, descending)

0 10 20 30 40 50

single

married

Mgmt

Prof

Other

Operat.

Sales

Cler.

Labor

Mgmt

Operat.

Other

Prof

Sales

Cler.

Labor

mean of wage mean of hours

Each over() option can have its own

separate sorting options. In this

example, we add the descending

option to the second over() option,

and the levels of married are now

shown with those who are married

appearing ﬁrst.

Uses nlsw.dta & scheme vg s2c

graph hbar (sum) wage, over(collgrad) over(occ7) asyvars stack

0 1,000 2,000 3,000 4,000 5,000

sum of wage

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

not college grad college grad

Say that we were to graph the sum of

wage broken down by collgrad and

occ7. We further treat the levels of

collgrad as y-variables and form a

stacked bar chart. We might want to

sort these bars based on the sum of

wages for each occupation. See the next

example for how we can do that.

Uses nlsw.dta & scheme vg s2c

graph hbar (sum) wage,

over(collgrad) over(occ7, sort((sum) wage)) asyvars stack

0 1,000 2,000 3,000 4,000 5,000

sum of wage

Sales

Prof

Mgmt

Other

Labor

Operat.

Cler.

not college grad college grad

Here, we add sort((sum) wage) to the

over() option for occ7, and then the

bars are sorted on the sum of wages at

each level of occ7, sorting the bars on

their total height.

Uses nlsw.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.4 Controlling the categorical axis 123

graph hbar (sum) wage,

over(collgrad) over(occ7, sort((sum) wage) descending) asyvars stack

Here, we add the descending option to

change the sort order from highest to

lowest. Note the placement of the

descending option outside of the

sort() option.

Uses nlsw.dta & scheme vg s2c

0 1,000 2,000 3,000 4,000 5,000

sum of wage

Cler.

Operat.

Labor

Other

Mgmt

Prof

Sales

not college grad college grad

4.4 Controlling the categorical axis

This section describes ways that you can label categorical axes. Bar charts are special

since their x-axis is formed by categorical variables. This section describes options you can

use to customize these categorical axes. For more details, see [G]cat axis label options

and [G]cat axis line options.

We will start by exploring how you can change the labels for the bars on the x-axis.

graph bar wage, over(grade6) over(south) asyvars

This bar chart breaks wages down by

education level and whether one lives in

the South. Adding the asyvars option

graphs the levels of education level as

diﬀerently colored bars, as though they

were diﬀerent y-variables. More

importantly, note that the variable

south is coded 0/1 and has no labels,

leaving the x-axis poorly labeled.

Uses nlsw.dta & scheme vg s2c

0 5 10 15

mean of wage

0 1

No HS Some HS

HS Grad Some Coll

Coll Grad Post Grad

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

124 Chapter 4. Bar graphs

graph bar wage, over(grade6) over(south, relabel(1 "N & W" 2 "South"))

asyvars

0 5 10 15

mean of wage

N & W South

No HS Some HS

HS Grad Some Coll

Coll Grad Post Grad

The relabel() option is used to

change the labels displayed for the

levels of south, giving the x-axis more

meaningful labels. Note that we wrote

relabel(1 "N & W") and not

relabel(0 "N & W") since these

numbers do not represent the actual

levels of south but the ordinal position

of the levels, i.e., ﬁrst and second.

Uses nlsw.dta & scheme vg s2c

graph bar wage, over(grade6) over(union, relabel(3 "missing"))missing

asyvars

0 5 10 15

mean of wage

nonunion union missing

No HS Some HS

HS Grad Some Coll

Coll Grad Post Grad

Consider this example, where we show

wages broken down by education and

union membership with the missing

option to show a separate category for

missing values. Normally, the bar for

the missing category would be labeled

with a dot, but here we add the

relabel() option to label that

category with the word “missing”.

Uses nlsw.dta & scheme vg s2c

graph hbar wage, over(grade6) over(south, relabel(1 "N & W" 2 "South"))

over(smsa, relabel(1 "Non Metro" 2 "Metro"))

0 5 10 15

mean of wage

Metro

Non Metro

South

N & W

South

N & W

No HS Some HS

HS Grad Some Coll

Coll Grad Post Grad

This is an example of a bar chart with

three over() variables, two of which we

relabel. The relabel() option is used

to change the labels for the levels of

south and smsa. Note each over()

option can have its own relabel()

option.

Uses nlsw.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.4 Controlling the categorical axis 125

graph hbar prev exp tenure ttl exp, ascategory over(age3)

This bar chart shows three y-variables,

but we use the ascategory option to

plot the diﬀerent y-variables as

categorical variables on the x-axis. The

default labels on the x-axis are not bad,

but we might want to change them.

Uses nlsw.dta & scheme vg s2c

0 5 10 15

42/46

38/41

34/37

mean of ttl_exp

mean of tenure

mean of prev_exp

mean of ttl_exp

mean of tenure

mean of prev_exp

mean of ttl_exp

mean of tenure

mean of prev_exp

graph hbar prev exp tenure ttl exp, ascategory over(age3)

yvaroptions(relabel(1 "Previous Exp" 2 "Current Exp" 3 "Total Exp"))

If the three level-of-experience variables

were indicated by an over() option, we

would use the over(, relabel())

option to change the labels. Instead,

since we have treated the multiple

y-variables as categories, we then use

yvaroptions(relabel()) to modify

the labels on the x-axis.

Uses nlsw.dta & scheme vg s2c

0 5 10 15

42/46

38/41

34/37

Total Exp

Current Exp

Previous Exp

Total Exp

Current Exp

Previous Exp

Total Exp

Current Exp

Previous Exp

graph hbar prev exp tenure ttl exp, ascategory

over(age3, relabel(1 "34-37 yrs" 2 "38-41 yrs" 3 "42-46 yrs"))

yvaroptions(relabel(1 "Previous Exp" 2 "Current Exp" 3 "Total Exp"))

This example is similar to the previous

example, but we have added a

relabel() option to the over()

variable as well. As before, we use

yvaroptions(relabel()) to modify

the labels for the multiple y-variables,

and then we also use the relabel()

option within the over() option to

change the labels for age.

Uses nlsw.dta & scheme vg s2c

0 5 10 15

42−46 yrs

38−41 yrs

34−37 yrs

Total Exp

Current Exp

Previous Exp

Total Exp

Current Exp

Previous Exp

Total Exp

Current Exp

Previous Exp

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

126 Chapter 4. Bar graphs

graph hbar prev exp tenure ttl exp, ascategory xalternate

over(age3, relabel(1 "34-37 yrs" 2 "38-41 yrs" 3 "42-46 yrs"))

yvaroptions(relabel(1 "Previous Exp" 2 "Current Exp" 3 "Total Exp"))

0 5 10 15

42−46 yrs

38−41 yrs

34−37 yrs

Total Exp

Current Exp

Previous Exp

Total Exp

Current Exp

Previous Exp

Total Exp

Current Exp

Previous Exp

If we wish, we can move the x-axis to

the opposite side of the graph. Here, we

add the xalternate option, which

moves the labels for the x-axis to the

opposite side, in this case from the left

to the right. You can also use the

yalternate option to move the y-axis

to its opposite side.

Uses nlsw.dta & scheme vg s2c

In the previous examples, we saw that the relabel option can be used in the over()

option to control the labeling of over() variables and can be used within yvaroptions()

to control the labeling of multiple y-variables (provided that the ascategory option is used

to convert the multiple y-variables into categories). We will further explore other over()

options, which can be used with either over() or yvaroptions().

graph bar wage, over(occ7, label(nolabels))

0 2 4 6 8 10

mean of wage

We can use the label(nolabels)

option to suppress the display of the

labels associated with the levels of

occ7.Thelabel(nolabels) option is

generally not useful alone but is very

useful in combination with other means

to label the bars. Consider the next

example.

Uses nlsw.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.4 Controlling the categorical axis 127

graph bar wage, over(occ7, label(nolabels)) blabel(group)

By adding the blabel(group) (bar

label) option, the bars are labeled with

the name of the group to which the bar

belongs. See Bar : Legend (130) for more

about blabel().

Uses nlsw.dta & scheme vg s2c

Prof Mgmt

Sales

Cler.

Operat.

Labor

Other

0 2 4 6 8 10

mean of wage

graph bar wage, over(occ7, label(angle(45))) over(collgrad)

This graph shows wages broken down

by occupation and by whether one

graduated college. The

label(angle(45)) option is added to

rotate the labels for occupation by 45

degrees. If this had been omitted, the

labels would have overlapped each

other.

Uses nlsw.dta & scheme vg s2c

0 5 10 15

mean of wage

not college grad college grad

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

graph bar wage, over(occ7, label(alternate)) over(collgrad)

Compare this graph with the previous

example. This example uses the

label(alternate) strategy to avoid

overlapping by alternating the labels for

occupation.

Uses nlsw.dta & scheme vg s2c

0 5 10 15

mean of wage

not college grad college grad

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

128 Chapter 4. Bar graphs

graph bar wage hours ttl exp, ascategory over(collgrad)

yvaroptions(label(alternate))

0 10 20 30 40

not college grad college grad

mean of wage

mean of hours

mean of ttl_exp

mean of wage

mean of hours

mean of ttl_exp

This is another example of using the

label(alternate) option, but in this

case, it is used in the context of

alternating labels created by multiple

y-variables converted to categories

using the ascategory option. In such a

case, the option is speciﬁed as

yvaroptions(label(alternate)).

Uses nlsw.dta & scheme vg s2c

graph bar wage hours ttl exp, ascategory over(union) nolabel

0 10 20 30 40

nonunion union

wage hours ttl_exp wage hours ttl_exp

If we add the nolabel option, the

names of the variables are shown

instead of the value labels.

Uses nlsw.dta & scheme vg s2c

graph hbar wage, over(occ5, label(labcolor(green)))

over(collgrad, label(labcolor(maroon) labsize(small)))

0 5 10 15

mean of wage

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

We can change the color of the labels

using the labcolor() option. Here, we

make the label for occ5 green and the

label for collgrad maroon. We also

use labsize(small) to make the labels

for collgrad small. See Styles : Colors

(328) and Styles : Textsize (344) for more

details about other values you could

choose.

Uses nlsw.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.4 Controlling the categorical axis 129

graph bar wage,

over(age3, label(ticks tlwidth(thick) tlength(*2) tposition(crossing)))

over(collgrad)

Stata permits you to add ticks using

the ticks option. At the same time, we

modify the attributes of the ticks,

making the tick line width thick, the

tick length twice as long as normal, and

the tick position crossing the x-axis.

See [G]cat axis label options for

more details and other options for

controlling ticks.

Uses nlsw.dta & scheme vg s2c

0 2 4 6 8 10

mean of wage

not college grad college grad

34/37 38/41 42/46 34/37 38/41 42/46

graph bar wage, over(age3, label(labgap(*5))) over(collgrad)

The labgap(*5) option increases the

gap between the label and the axis,

making the gap between the labels for

the levels of age3 and the axis ﬁve

times their normal size.

Uses nlsw.dta & scheme vg s2c

0 2 4 6 8 10

mean of wage

not college grad college grad

34/37 38/41 42/46 34/37 38/41 42/46

graph bar wage, over(age3) over(collgrad, label(labgap(*5)))

We use the label(labgap(*5)) option

to control the gap between the labels

for age3 and collgrad, making that

gap ﬁve times the normal size.

Uses nlsw.dta & scheme vg s2c

0 2 4 6 8 10

mean of wage

not college grad college grad

34/37 38/41 42/46 34/37 38/41 42/46

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

130 Chapter 4. Bar graphs

graph bar wage, over(age3) over(collgrad, axis(outergap(*20)))

0 2 4 6 8 10

mean of wage

not college grad college grad

34/37 38/41 42/46 34/37 38/41 42/46

The axis(outergap(*20)) option

controls the gap between the labels of

the x-axis and the outside of the graph.

As you can see, this increases the space

below the labels for collgrad and the

bottom of the graph.

Uses nlsw.dta & scheme vg s2c

graph bar wage, over(union) over(grade4) asyvars

b1title("Education Level in Four Categories")

0 2 4 6 8 10

mean of wage

Not HS HS Grad Some Coll Coll Grad

Education Level in Four Categories

nonunion union

The b1title() option adds a title to

the bottom of the graph, in eﬀect

labeling the x-axis. We can add a

second title below that using the

b2title() option. If we used graph

hbox, we could label the left axis using

the l1title() and l2title() options.

Uses nlsw.dta & scheme vg s2c

4.5 Controlling legends

This section discusses the use of legends for bar charts, emphasizing the features that

are unique to bar charts. The section Options : Legend (287) goes into great detail about

legends, as does [G]legend option. Legends can be used for multiple y-variables or when

the ﬁrst over() variable is treated as a y-variable via the asyvars option. See Bar : Y-

variables (107) for more information about the use of multiple y-variables and Bar : Over

(111) for more examples of treating the ﬁrst over() variable as a y-variable. Next, we

will consider examples that show the diﬀerent kinds of labels that you can create using the

blabel() option. You can create labels that display the name of y-variable, the name of

the ﬁrst over() group, the height of the bar, or the overall height of the bar (when used

with the stack option). These examples begin using the vg s1c scheme.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.5 Controlling legends 131

graph bar wage hours tenure ttl exp age

Consider this bar graph of ﬁve diﬀerent

y-variables. The bars for the diﬀerent

y-variables are shown with diﬀerent

colors, and a legend is used to identify

the y-variables.

Uses nlsw.dta & scheme vg s1c

0 10 20 30 40

mean of wage mean of hours

mean of tenure mean of ttl_exp

mean of age

graph bar wage, over(occ7) asyvars

This is another example of where a

legend can arise in a Stata bar graph by

specifying the asyvars option, which

treats an over() variable as though the

levels were diﬀerent y-variables.

Uses nlsw.dta & scheme vg s1c

0 2 4 6 8 10

mean of wage

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Unless otherwise mentioned, the legend options described below work the same regard-

less of whether the legend was derived from multiple y-variables or from an over() variable

that was combined with the asyvars option. These next examples use the vg s2m scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

132 Chapter 4. Bar graphs

graph hbar wage hours tenure ttl exp age, nolabel

0 10 20 30 40

wage hours

tenure ttl_exp

age

The nolabel option only works when

you have multiple y-variables. When it

is used, the variable names (not the

variable labels) are used in the legend.

For example, instead of showing the

variable label hourly wage,itshows

the variable name wage.

Uses nlsw.dta & scheme vg s2m

graph hbar wage hours tenure ttl exp age, showyvars

0 10 20 30 40

mean of age

mean of ttl_exp

mean of tenure

mean of hours

mean of wage

mean of wage mean of hours

mean of tenure mean of ttl_exp

mean of age

The showyvars option puts the labels

on the axis, beside or “under” the bars.

Uses nlsw.dta & scheme vg s2m

graph bar wage, over(occ7) asyvars showyvars

0 2 4 6 8 10

mean of wage

Prof Mgmt Sales Cler. Operat. Labor Other

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Even though the showyvars option

sounds like it would work only with

multiple y-variables, it also works when

you combine the over() and asyvars

options. As you can see, the legend is

now redundant and could be

suppressed.

Uses nlsw.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.5 Controlling legends 133

graph bar wage, over(occ7) asyvars showyvars legend(off)

This example is similar to the previous

example, but we use the legend(off)

option to suppress the display of the

legend.

Uses nlsw.dta & scheme vg s2m

0 2 4 6 8 10

mean of wage

Prof Mgmt Sales Cler. Operat. Labor Other

graph bar wage, over(occ7) asyvars legend(label(1 "Professional")

label(2 "Management"))

We can use legend(label()) to change

the labels for one or more of the bars in

the graph. Here, we change the labels

for the ﬁrst and second bars in the

legend. Note that you use a separate

label() option for each bar. This is in

contrast to the relabel() option,

where all of the label assignments were

placed in one relabel() option; see

Bar : Cat axis (123).

Uses nlsw.dta & scheme vg s2m

0 2 4 6 8 10

mean of wage

Professional Management

Sales Cler.

Operat. Labor

Other

graph bar wage, over(occ7) asyvars legend(rows(2) colfirst)

In this example, we use the rows(2)

option combined with colfirst to

display the legend in two rows and to

order the keys by column (instead of

the default, which is by row). This

yields keys that are more adjacent to

the bars that they label.

Uses nlsw.dta & scheme vg s2m

0 2 4 6 8 10

mean of wage

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

134 Chapter 4. Bar graphs

As you can see, the default placement for the legend is below the x-axis. However, Stata

gives you tremendous ﬂexibility in the placement of the legend. We now consider options

that control the placement of the legend, along with options useful for controlling the place-

ment of the items within the legend. The following examples use the vg blue scheme.

graph bar wage, over(occ7) asyvars legend(position(1))

mean of wage

Prof Mgmt

Sales Cler.

Operat. Labor

Other

We can use the legend(position(1))

option to place the legend in the top

right corner of the graph. The values

you supply for position() are like the

numbers on a clock face, where 12

o’clock is the top, 6 o’clock is the

bottom, and 0 represents the center of

the clock face. Specifying 1 o’clock

places the legend in the top right; see

Styles : Clockpos (330) for more details.

Uses nlsw.dta & scheme vg blue

graph bar wage, over(occ7) asyvars legend(position(1) ring(0))

mean of wage

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Adding the ring(0) option, we can try

to tuck the legend inside the top right

corner of the plot area. Think of the

ring() option as specifying concentric

rings around the graph, where 0 is a

position inside the plot region, 1 is just

outside the plot region, and increasing

values are farther and farther from the

center of the plot region. Unfortunately,

the legend touches one of the bars, but

we will ﬁx that in the next example.

Uses nlsw.dta & scheme vg blue

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.5 Controlling legends 135

graph bar wage, over(occ7) asyvars legend(position(1) ring(0)) exclude0

Adding exclude0 no longer forces the

y-axis to start at 0 and makes room in

the top corner of the plot region for the

legend. See Bar : Y-axis (143) for more

details about the exclude0 option.

Uses nlsw.dta & scheme vg blue

mean of wage

Prof Mgmt

Sales Cler.

Operat. Labor

Other

graph hbar wage, over(occ7) asyvars legend(cols(1) position(9))

We switch to making this a horizontal

bar chart and move the legend using

the position(9) option to place the

legend in the 9 o’clock position. We

also use the cols(1) option to display

the legend as a single column.

Uses nlsw.dta & scheme vg blue

0 2 4 6 8 10

mean of wage

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

graph hbar wage, over(occ7) asyvars legend(cols(1) position(9) textfirst)

Adding the textfirst option places

the description of the key before the

symbol in the legend.

Uses nlsw.dta & scheme vg blue

0 2 4 6 8 10

mean of wage

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

136 Chapter 4. Bar graphs

graph hbar wage, over(occ7) asyvars legend(cols(1) position(9) stack)

0 2 4 6 8 10

mean of wage

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

With the stack option, the keys and

their labels are placed on top of each

other to form an even narrower legend,

leaving more room to plot the bars.

You have considerable control over the

elements within the legend using other

options, such as rowgap(),keygap(),

symxsize(),symysize(),

textwidth(),andsymplacement().

See Options : Legend (287) and

[G]legend option for more details.

Uses nlsw.dta & scheme vg blue

graph hbar wage, over(occ7) asyvars

0 2 4 6 8 10

mean of wage

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

This example uses the vg lgndc

scheme, set scheme vg lgndc. Notice

how it positions and customizes the

legend, as in the previous example.

With this scheme, the legend defaults

to the 9 o’clock position, in a single

column, with the keys and symbols

stacked.

Uses nlsw.dta & scheme vg lgndc

Let’s now look at how we can use the blabel() (bar label) option to add labels to the

bars. These labels can show the name of the over() option, the name of y-variables, or

the height of the bar. These options are illustrated below along with other related options

you might use in conjunction with blabel() for identifying the bars. These examples begin

using the vg past scheme.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.5 Controlling legends 137

graph bar wage hours tenure, over(collgrad)

Consider this graph, where we look at

wage,hours,andtenure broken down

by the levels of collgrad. The legend

identiﬁes the bars for us. In addition to

the legend, Stata oﬀers us other ways

we can label these bars, as we shall see

in the upcoming examples.

Uses nlsw.dta & scheme vg past

0 10 20 30 40

not college grad college grad

mean of wage mean of hours

mean of tenure

graph bar wage hours tenure, over(collgrad) blabel(name)

We can add the blabel(name) (bar

label) option, and it places labels on

each of the bars with the name of

y-variables. Here, each of these labels is

preceded with “mean of” since each bar

represents the mean of y-variable.

Uses nlsw.dta & scheme vg past

mean of wage

mean of hours

mean of tenure

mean of wage

mean of hours

mean of tenure

0 10 20 30 40

not college grad college grad

mean of wage mean of hours

mean of tenure

graph bar wage hours tenure, over(collgrad) blabel(name) nolabel

If we use the nolabel option, just the

name y-variable is shown. For example,

instead of showing the variable label

hourly wage, it shows the variable

name wage.

Uses nlsw.dta & scheme vg past

wage

hours

tenure

wage

hours

tenure

0 10 20 30 40

not college grad college grad

wage hours

tenure

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

138 Chapter 4. Bar graphs

graph bar wage hours tenure, over(collgrad) blabel(name) nolabel

legend(off)

wage

hours

tenure

wage

hours

tenure

0 10 20 30 40

not college grad college grad

In this case, the legend is no longer

needed, so we can suppress the display

of the legend with the legend(off)

option. See Options : Legend (287) for

more information about legend options.

Uses nlsw.dta & scheme vg past

graph bar tenure, over(occ7) exclude0 blabel(group)

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

3 4 5 6 7

mean of tenure

Prof Mgmt Sales Cler. Operat. Labor Other

Using the blabel(group) option shows

the label for the ﬁrst over() group at

the top of each bar. In this case, the

label at the bottom of the bar becomes

unnecessary.

Uses nlsw.dta & scheme vg past

graph bar tenure, over(occ7, label(nolabels)) exclude0

blabel(group) yscale(range(7.2))

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

3 4 5 6 7

mean of tenure

We can add the label(nolabels)

option to suppress the display of the

labels below each bar. Note that we

have also used the option

yscale(range(7.2)) to provide more

room within the plot area to label the

bar for the Other category.

Uses nlsw.dta & scheme vg past

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.5 Controlling legends 139

graph bar tenure, over(occ5, label(nolabels)) exclude0 blabel(group)

yscale(range(7.2)) over(union)

Even if we add a second over() option,

the levels of the ﬁrst over() variable

are labeled at the top of each bar due to

the blabel() option, and the levels of

the second over() variable are labeled,

as usual, at the bottom of the bars.

Note that the blabel() option does

not work this way when you have three

over() options or multiple y-variables.

Uses nlsw.dta & scheme vg past

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

4 5 6 7 8 9

mean of tenure

nonunion union

graph hbar prev exp tenure ttl exp, over(grade4) blabel(bar)

Consider this graph showing previous,

current, and total work experience

broken down by education. In this

example, the blabel(bar) option is

used to display the bar height (in this

case, the mean of y-variables).

Uses nlsw.dta & scheme vg past

13.441

6.81536

6.64236

13.2673

6.21637

7.06569

12.4683

5.84232

6.64033

10.347

4.7191

5.66887

0 5 10 15

Coll Grad

Some Coll

HS Grad

Not HS

mean of prev_exp mean of tenure

mean of ttl_exp

graph bar (sum) prev exp tenure, stack over(grade4) blabel(bar)

Using the (sum) function, this graph

shows the sum of experience for all

individuals in a grade level before their

current job (prev exp) and the sum of

experience for all individuals in a grade

level in their current job (tenure)and

then uses stack to stack these two

totals. With the blabel(bar) option,

the bar labels are the sums for each

y-variables broken down by grade4.

Uses nlsw.dta & scheme vg past

1865.06

1552.58

6221.99

5474.25

3186.63

2803.58

3387.6

3475.83

0 5,000 10,000 15,000

Not HS HS Grad Some Coll Coll Grad

sum of prev_exp sum of tenure

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

140 Chapter 4. Bar graphs

graph bar (sum) prev exp tenure, stack over(grade4) blabel(total)

1865.06

3417.64

6221.99

11696.2

3186.63

5990.21

3387.6

6863.44

0 5,000 10,000 15,000

Not HS HS Grad Some Coll Coll Grad

sum of prev_exp sum of tenure

As compared with the prior example,

this example uses the blabel(total)

option to display the results as totals.

Now, the labels represent the

cumulative total height of the bar.

Uses nlsw.dta & scheme vg past

We have seen a variety of ways that you can use the blabel() option to label the bars.

In addition, Stata oﬀers a variety of options you can use to control the display of these

labels. Below, we will consider some of these options that allow you to customize the way

these labels are displayed. These example begin using the vg palec scheme.

graph hbar hours, over(occ7, label(nolabels)) blabel(group)

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

0 10 20 30 40

mean of hours

Consider this graph of the average

hours worked by occupation. We add

labels of the occupation at the top of

each bar but suppress the label at the

bottom of each bar. The label for the

second bar runs oﬀ the right of the

graph. Fortunately, Stata oﬀers us a

number of options to control where

these labels are displayed.

Uses nlsw.dta & scheme vg palec

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.5 Controlling legends 141

graph hbar hours, over(occ7, label(nolabels))

blabel(group, position(inside))

With the position(inside) option, we

can place the group label inside the

bar. By default, inside refers to the

very “top” of the bar but on the inside

of the bar. Note that, because we chose

the vg palec scheme, the bar colors are

pale, so the labels within the bars are

readable.

Uses nlsw.dta & scheme vg palec

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

0 10 20 30 40

mean of hours

graph hbar hours, over(occ7, label(nolabels))

blabel(group, position(base))

With the position(inside) option, we

can place the label inside the bar, but

at the base of the bar. You can also

specify position(center) to place the

label in the center of the bar.

Uses nlsw.dta & scheme vg palec

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

0 10 20 30 40

mean of hours

graph hbar hours, over(occ7, label(nolabels))

blabel(group, position(base) gap(*10))

The gap() option can be used to

ﬁne-tune the placement of the label.

Here, we position the label at the base

but increase the gap between the label

and the base to be 10 times its normal

size. You can also use the gap() option

with position(inside) to position the

label with respect to the top of the bar.

Uses nlsw.dta & scheme vg palec

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

0 10 20 30 40

mean of hours

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

142 Chapter 4. Bar graphs

graph bar hours, over(occ7) blabel(bar, position(outside)) exclude0

37.7603

42.9886

35.9284

34.9804

39.3862

31.9754

38.6349

30 35 40 45

mean of hours

Prof Mgmt Sales Cler. Operat. Labor Other

This graph is similar to the previous

ones, but the bars are vertical, and we

now are labeling the bars with the

height of the bar. The label is placed

just outside the bar.

Uses nlsw.dta & scheme vg palec

graph bar hours, over(occ7, axis(outergap(*5))) asyvars

blabel(bar, position(base) gap(-4))

37.7603 42.9886 35.9284 34.9804 39.3862 31.9754 38.6349

0 10 20 30 40

mean of hours

Prof Mgmt

Sales Cler.

Operat. Labor

Other

To put the labels just under the bars,

we use position(base) to put the

labels at the base but also specify

gap(-4) to move the labels below the

bars. Adding the axis(outergap(*5))

option (see Bar : Cat axis (123)), we

make enough room so the labels do not

bump into the legend.

Uses nlsw.dta & scheme vg palec

graph bar hours, over(occ7) asyvars

blabel(bar, position(base) box bfcolor(white) size(large) format(%5.2f))

37.76 42.99 35.93 34.98 39.39 31.98 38.63

0 10 20 30 40

mean of hours

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Here, we show more options that you

can use to customize the display of the

labels. We add a number of options to

place a box around the label, make the

background ﬁll color white, increase the

size of the text to be large, and display

the means with a width of 5 and 2

decimal places. See Options : Textboxes

(303) for additional examples of how to

use textbox options to control the

displayoftext.

Uses nlsw.dta & scheme vg palec

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.6 Controlling the y-axis 143

4.6 Controlling the y-axis

This section describes options you can use to control the y-axis in bar charts. To be

precise, when Stata refers to the y-axis on a bar chart, it refers to the axis with the con-

tinuous variable, whether the left axis when using graph bar or the bottom axis when

using graph hbar. This section emphasizes the features that are particularly relevant to

bar charts. For more details, see Options : Axis titles (254), Options : Axis labels (256), and

Options : Axis scales (265). Also see [G]axis title options,[G]axis label options,and

[G]axis scale options. This section uses the vg s2c scheme.

graph bar wage, over(occ5) over(married) asyvar

Consider this graph showing the mean

hourly wage broken down by

occupation and marital status.

Uses nlsw.dta & scheme vg s2c

0 5 10 15

mean of wage

single married

Prof/Mgmt Sales

Clerical Labor/Ops

Other

graph bar wage, over(occ5) over(married) asyvar

ytitle("Years of experience")

We can use the ytitle() option to add

a title to the y-axis. See

Options : Axis titles (254) and

[G]axis title options for more details,

but please disregard any references to

xtitle() since that option is not valid

when using graph bar.

Uses nlsw.dta & scheme vg s2c

0 5 10 15

Years of experience

single married

Prof/Mgmt Sales

Clerical Labor/Ops

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

144 Chapter 4. Bar graphs

graph hbar wage, over(occ5) over(married) asyvar

ytitle("Years of" "experience")

0 5 10 15

Years of

experience

married

single

Prof/Mgmt Sales

Clerical Labor/Ops

Other

Splitting the title into two separate

quoted strings displays the title on

separate lines. Note that, when using

graph hbar, the title of the y-axis now

appears at the bottom.

Uses nlsw.dta & scheme vg s2c

graph hbar wage, over(occ5) over(married) asyvar

ytitle("Years of" "experience", size(vlarge) box bexpand)

0 5 10 15

Years of

experience

married

single

Prof/Mgmt Sales

Clerical Labor/Ops

Other

Because this title is considered to be a

textbox, you can use a variety of

textbox options to control the look of

the title. In this example, the title is

made large with a box around it, and

the bexpand (box expand) makes the

box expand to ﬁll the width of the plot

area. See Options : Textboxes (303) for

additional examples of how to use

textbox options to control the display

of text.

Uses nlsw.dta & scheme vg s2c

graph hbar wage, over(occ5) over(married) asyvar

yline(8 10, lwidth(thick) lcolor(red) lpattern(dash))

0 5 10 15

mean of wage

married

single

Prof/Mgmt Sales

Clerical Labor/Ops

Other

The yline() option is used to place a

thick, red, dashed line on the graph

where yequals 8 and 10. Note that this

option is still called yline() since the

y-axis is the axis with the continuous

variable.

Uses nlsw.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.6 Controlling the y-axis 145

graph bar hours, over(occ7) asyvar ylabel(30(5)45)

We can use the ylabel() option to

label the y-axis. In this case, we label

the y-axis from 30 to 45 by increments

of 5. See Options : Axis labels (256) and

[G]axis label options for more

details. Please disregard any references

to xlabel() since that option is not

valid when using graph bar. Note that

the y-axis still begins at 0. See the

following example to see how you can

control that.

Uses nlsw.dta & scheme vg s2c

30 35 40 45

mean of hours

Prof Mgmt

Sales Cler.

Operat. Labor

Other

graph bar hours, over(occ7) asyvar ylabel(30(5)45) exclude0

By default, bar charts include 0 on the

y-axis, unless you specify the exclude0

option, as we do here.

Uses nlsw.dta & scheme vg s2c

30 35 40 45

mean of hours

Prof Mgmt

Sales Cler.

Operat. Labor

Other

graph bar hours, over(occ7) asyvar ylabel(30(5)45, angle(0)) exclude0

We can add the angle() option to

modify the angle of the y-label, making

the labels for the y-axis horizontal (zero

degrees).

Uses nlsw.dta & scheme vg s2c

mean of hours

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

146 Chapter 4. Bar graphs

graph bar hours, over(occ7) asyvar ylabel(30(5)45, nogrid) exclude0

30 35 40 45

mean of hours

Prof Mgmt

Sales Cler.

Operat. Labor

Other

The nogrid option suppresses the

display of the grid. Note that this

option is placed within the ylabel()

option, thus suppressing the grid for

the y-axis. (With bar charts, there is

never a grid with respect to the x-axis.)

If the grid were absent, and we wanted

to include it, we could add the grid

option. For more details, see

Options : Axis labels (256).

Uses nlsw.dta & scheme vg s2c

graph bar prev exp tenure, over(occ7) yscale(off)

Prof Mgmt Sales Cler. Operat. Labor Other

mean of prev_exp mean of tenure

If you want to suppress the display of

the y-axis entirely, you can use the

yscale(off) option. See

Options : Axis scales (265) and

[G]axis scale options for more

details. Please disregard any references

to xscale() since that option is not

valid when using graph bar.

Uses nlsw.dta & scheme vg s2c

graph bar prev exp tenure, over(occ7) yalternate

0 2 4 6 8

Prof Mgmt Sales Cler. Operat. Labor Other

mean of prev_exp mean of tenure

We can use the yalternate option to

put the y-axis on the opposite side, in

this case on the right side of the graph.

Uses nlsw.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.7 Changing the look of bars, lookofbar options 147

graph hbar prev exp tenure, over(occ7) xalternate yreverse

You can reverse the direction of the

y-axis with the yreverse option. We

combine this with the xalternate

option to place the labels for the bars

on the alternate (right) side of the

graph.

Uses nlsw.dta & scheme vg s2c

02468

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

4.7 Changing the look of bars, lookofbar options

This section shows how you can control the look of the bars in your bar charts: the

space between the bars, the color of the bars, and the characteristics of the line outlin-

ing the bars. For more information, see the lookofbar options table in [G]graph bar and

[G]barlook options. This section begins using the vg rose scheme.

graph bar wage hours ttl exp tenure, over(collgrad)

Consider this bar chart. It shows the

mean wages, hours worked per week,

total experience, and job tenure broken

down by whether one graduated college.

Uses nlsw.dta & scheme vg rose

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

148 Chapter 4. Bar graphs

graph bar wage hours ttl exp tenure, over(collgrad)

outergap(*15)

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

We can change the outer gap between

the bars and the edge of the plot area

with the outergap() option. Here, the

gap is ﬁfteen times its normal size. You

can also supply values less than 1 to

shrink the size of the gap.

Uses nlsw.dta & scheme vg rose

graph bar wage hours ttl exp tenure, over(collgrad)

bargap(25)

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

The bargap() option controls the size

of the gap between the bars. The

default value is 0, meaning that the

bars touch exactly. Here, we make the

gap 25% of the width of the bars.

Uses nlsw.dta & scheme vg rose

graph bar wage hours ttl exp tenure, over(collgrad)

bargap(-50)

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

The bargap() option permits negative

values to indicate that you want the

bars to overlap. Here, we make the bars

overlap by 50% of the size of the bars.

Uses nlsw.dta & scheme vg rose

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.7 Changing the look of bars, lookofbar options 149

graph bar wage hours ttl exp tenure, over(collgrad)

intensity(*.5)

The intensity option is used to

control the intensity of the color within

the bars. Here, we request that the

color be 50% as intense as it normally

would be.

Uses nlsw.dta & scheme vg rose

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

graph bar wage hours ttl exp tenure, over(collgrad)

intensity(*1.4)

In this example, we use the

intensity() option to make the colors

within the bars 1.4 times more intense

than they would normally be. Note

that Stata also has an option called

lintensity() that works the same way

but controls the intensity of the line

surrounding the bar. (This option is

not illustrated.)

Uses nlsw.dta & scheme vg rose 0

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

So far, all these options that we have examined determine the overall behavior and look

over all of the bars as a group. Using the bar() option, you can control the look of the

bars for each y-variable, as illustrated below. These graphs use the vg s2c scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

150 Chapter 4. Bar graphs

graph bar wage hours ttl exp tenure, over(collgrad)

bar(1, bcolor(dkgreen))

0 10 20 30 40

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

Here, we use the bar() option to make

the color of the ﬁrst bar dark green.

See Styles : Colors (328) for more

information about colors you can select.

Uses nlsw.dta & scheme vg s2c

graph bar wage hours ttl exp tenure, over(collgrad)

bar(1, bfcolor(ltblue) blcolor(blue) blwidth(vthick))

0 10 20 30 40

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

In this example, we make the ﬁll color

of the ﬁrst bar light blue and the

outline very thick and blue. See

Styles : Linewidth (337) for more details

on controlling the thickness of lines.

You could also use the blpattern()

option to control the pattern of the line

surrounding the bar; see

Styles : Linepatterns (336) for more

details.

Uses nlsw.dta & scheme vg s2c

graph bar wage hours ttl exp tenure, over(collgrad)

0 10 20 30 40

not college grad college grad

mean of wage mean of hours

mean of ttl_exp mean of tenure

While you can use the bar() option to

control the look of each bar, selecting a

diﬀerent scheme allows you to control

the look of all of the bars. For example,

this graph is drawn using the vg palec

scheme. See Intro : Schemes (14) for

some other schemes you could try and

Appendix : Customizing schemes (379) for

tips on customizing your own schemes.

Uses nlsw.dta & scheme vg palec

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.8 Graphing by groups 151

4.8 Graphing by groups

This section discusses the use of the by() option in combination with graph bar.Nor-

mally, you would use the over() option instead of the by() option, but there are cases

where the by() option is either necessary or more advantageous. For example, a by() op-

tion is useful if you exceed the maximum number of over() options (three if you have a

single y-variable or two if you have multiple y-variables). In such cases, the by() option

allows you to break your data down by additional categorical variables. Also, by() gives

you more ﬂexibility in the placement of the separate panels. For more information about

the by() option, see Options : By (272); for more information about the over() option, see

Bar : Over (111). These examples are shown using the vg s1c scheme.

graph bar wage, over(urban2) over(married) over(union)

Consider this bar graph that breaks

wages down by three categorical

variables. If we wanted to further break

this down by another categorical

variable, we could not use another

over() option since we can have a

maximum of three over() options with

a single y-variable.

Uses nlsw.dta & scheme vg s1c

0 2 4 6 8 10

mean of wage

nonunion union

single married single married

Rural Metro

graph bar wage, over(urban2) over(married) over(union) by(collgrad)

If we want to show the previous graph

separately by collgrad, we can use the

by() option. This gives us two graphs

side by side: one for those who are not

college graduates and one for college

graduates.

Uses nlsw.dta & scheme vg s1c

0 5 10 15

nonunion union nonunion union

single married single married single married single married

not college grad college grad

Rural Metro

mean of wage

Graphs by college graduate

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

152 Chapter 4. Bar graphs

graph bar ttl exp tenure, over(married) over(urban2)

0 5 10 15

Rural Metro

single married single married

mean of ttl_exp mean of tenure

Consider this bar graph with multiple

y-variables broken down by two

categorical variables using two over()

options. When you have multiple

y-variables, you can only have a

maximum of two over() options.

Uses nlsw.dta & scheme vg s1c

graph bar ttl exp tenure, over(married) over(urban2)

by(union)

0 5 10 15

Rural Metro Rural Metro

single married single married single married single married

nonunion union

mean of ttl_exp mean of tenure

Graphs by union worker

If we want to further show the previous

graph by another categorical variable,

say union,wecanusetheby() option.

Uses nlsw.dta & scheme vg s1c

graph bar ttl exp tenure, over(married) over(urban2)

by(union, missing)

0 5 10 150 5 10 15

Rural Metro Rural Metro

Rural Metro

single married single married single married single married

single married single married

nonunion union

(missing)

mean of ttl_exp mean of tenure

Graphs by union worker

We can add the missing option to

include a panel for the missing values of

union.

Uses nlsw.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

4.8 Graphing by groups 153

graph bar ttl exp tenure, over(married) over(urban2)

by(union, missing total)

We can add the total option to include

a panel for all observations.

Uses nlsw.dta & scheme vg s1c

0 5 10 150 5 10 15

Rural Metro Rural Metro

single married single married single married single married

nonunion union

(missing) Total

mean of ttl_exp mean of tenure

Graphs by union worker

graph hbar ttl exp tenure, over(married) over(urban2)

by(union, cols(1))

We remove the total and missing

options and ﬂip the graph to make a

horizontal bar chart. We then use the

cols(1) option to show these graphs in

one column. This makes the graph

pretty cramped. Let’s explore a number

of options we can add to this graph to

make it less cramped, adding the

options just a small number at a time.

Uses nlsw.dta & scheme vg s1c 0 5 10 15

Metro

Rural

Metro

Rural

married

single

married

single

married

single

married

single

nonunion

union

mean of ttl_exp mean of tenure

Graphs by union worker

graph hbar ttl exp tenure, over(married) over(urban2)

by(union, cols(1) note(""))

We add the note("") option within the

by() option, and that suppresses the

note in the left corner, leaving more

room for the graph.

Uses nlsw.dta & scheme vg s1c

0 5 10 15

Metro

Rural

Metro

Rural

married

single

married

single

married

single

married

single

nonunion

union

mean of ttl_exp mean of tenure

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

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published form of the book may be distributed or reproduced, either electronically or in printed form.

154 Chapter 4. Bar graphs

graph hbar ttl exp tenure, over(married) over(urban2)

by(union, cols(1) note("") legend(position(3)))

051015

Metro

Rural

Metro

Rural

married

single

married

single

married

single

married

single

nonunion

union mean of ttl_exp mean of tenure

We add the legend(position(3))

option to put the legend at the right.

Note that this is contained within the

by() option because it changes the

position of the legend. If we could make

the legend narrow (instead of wide), it

would work well in this position.

Uses nlsw.dta & scheme vg s1c

graph hbar ttl exp tenure, over(married) over(urban2)

by(union, cols(1) note("") legend(position(3)))

legend(cols(1) stack label(1 "Tot Exp") label(2 "Curr Exp"))

0 5 10 15

Metro

Rural

Metro

Rural

married

single

married

single

married

single

married

single

nonunion

union Tot Exp

Curr Exp

We add the legend(cols(1) stack)

to make the legend narrow and the

label() option to change the labels in

the legend. Note that this legend()

option appears outside of the by()

option. See Options : By (272) and

Options : Legend (287) for more

information about the interactions of

by() and legend().

Uses nlsw.dta & scheme vg s1c

graph hbar ttl exp tenure, over(married) over(urban2)

by(union, cols(1) note("") legend(position(3)))

legend(cols(1) stack label(1 "Tot Exp") label(2 "Curr Exp"))

subtitle(, position(5) ring(0) nobexpand)

0 5 10 15

Metro

Rural

Metro

Rural

married

single

married

single

married

single

married

single

nonunion

union

Tot Exp

Curr Exp

We can add the subtitle() option to

position the title for each separate

graph in the lower right corner. The

position(5) option puts the title in

the 5 o’clock position, and the ring(0)

option puts the title inside the plot

area. The nobexpand (no box expand)

option keeps the title from expanding

to ﬁll the entire plot area.

Uses nlsw.dta & scheme vg s1c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

4.8 Graphing by groups 155

graph bar ttl exp tenure, over(married) over(urban2)

by(union collgrad)

You can include multiple variables

within the by() option. Here, in

addition to breaking these variables

down by two over() variables, we

break them down by two additional

variables using the by(union

collgrad) option.

Uses nlsw.dta & scheme vg s1c

0 5 10 150 5 10 15

Rural Metro Rural Metro

single married single married single married single married

nonunion, not college grad nonunion, college grad

union, not college grad union, college grad

mean of ttl_exp mean of tenure

Graphs by union worker and college graduate

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Y-variables Over Over options Cat axis Legend Y-axis Lookofbar options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5 Box plots

A box plot displays box(es) bordered at the 25th and 75th percentiles of the y-variable

with a median line at the 50th percentile. Whiskers extend from the box to the upper and

lower adjacent values and are capped with an adjacent line. Values exceeding the upper and

lower adjacent values are called outside values and are displayed as markers. This chapter

starts by showing the use of the over() option to break box plots down by categorical

variables and then showing how you can specify multiple y-variables to display plots for

multiple variables. Next, we see further options that can be used to customize the display

of over() option, followed by options that control the display of categorical axes. Next,

we discuss options for legends, followed by options that control the display of the y-axis.

Finally, we cover options that control the look of boxes and the by() option.

5.1 Specifying variables and groups, yvars and over

This section introduces the use of box plots, illustrating the use of the over() option

for showing box plots by one or more grouping variables. Next, we give examples showing

how you can graph multiple variables at once by specifying additional y-variables, followed

by some general options for controlling the display of multiple y-variables and the behavior

of over() options. See the group options table in [G]graph box for more details. This

section begins with the vg s2c scheme.

graph box wage, over(grade4)

This is a box plot of wages broken

down by education. The over(grade4)

option breaks down wages by education

level (in four categories). By default,

the separate levels of grade4 are

graphed using the same color, and the

levels are labeled on the x-axis. The

graph shows a large number of outside

values that are displayed as markers

beyond the whiskers. The following

example shows how we can suppress the

display of the outside values.

Uses nlsw.dta & scheme vg s2c

0 10 20 30 40

hourly wage

Not HS HS Grad Some Coll Coll Grad

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

157

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published form of the book may be distributed or reproduced, either electronically or in printed form.

158 Chapter 5. Box plots

graph box wage, over(grade4) nooutsides

0 5 10 15 20

hourly wage

Not HS HS Grad Some Coll Coll Grad

excludes outside values

By adding the nooutsides option, we

suppress the display of the outside

values. Graphs using this option have a

note in the bottom left corner

indicating that the outside values have

been excluded from display in the

graph. For most of the graphs in this

chapter, there would be a large number

of outside values, which would make the

graphs very cluttered, so many of the

graphs will use the nooutsides option.

Uses nlsw.dta & scheme vg s2c

graph box wage, nooutsides over(grade4) over(union)

0 5 10 15 20

hourly wage

nonunion union

Not HSHS GradSome CollColl Grad Not HS HS GradSome CollColl Grad

excludes outside values

Here, we add the over(union) option

to show wages broken down by

education and whether one is a member

of a union. Note, however, that the

labels for grade4 overlap each other.

See the next example for one solution.

Uses nlsw.dta & scheme vg s2c

graph hbox wage, nooutsides over(grade4) over(union)

0 5 10 15 20

hourly wage

union

nonunion

Coll Grad

Some Coll

HS Grad

Not HS

Coll Grad

Some Coll

HS Grad

Not HS

excludes outside values

Here, we use graph hbox to make a

horizontal box plot. Note that this

eliminates the overlapping of the labels

for grade4. The next example will

show another possible solution.

Uses nlsw.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.1 Specifying variables and groups, yvars and over 159

graph box wage, nooutsides over(grade4) over(union) asyvars

Using the asyvars option, the ﬁrst

over() variable, grade4, is treated as

though it were multiple y-variables. As

a result, the levels of grade4 are shown

in multiple colors and labeled via a

legend. You can only use asyvars when

youhaveasingley-variable.

Uses nlsw.dta & scheme vg s2c

0 5 10 15 20

hourly wage

nonunion union

excludes outside values

Not HS HS Grad

Some Coll Coll Grad

graph box wage, nooutsides over(grade4) over(union) over(urban2)

In this example, we add a third over()

option, in this case comparing people

who live in rural and metropolitan

areas. Note that the ﬁrst over()

variable, grade4, is now treated as

though it were multiple y-variables.

Because of this, you can only specify

one y-variable when you have three

over() options.

Uses nlsw.dta & scheme vg s2c

0 5 10 15 20 25

hourly wage

Rural Metro

nonunion union nonunion union

excludes outside values

Not HS HS Grad

Some Coll Coll Grad

Now, let’s look at examples of using multiple y-variables with the over() option. We

ﬁrst consider a graph with multiple y-variables. These examples use the vg outc scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

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published form of the book may be distributed or reproduced, either electronically or in printed form.

160 Chapter 5. Box plots

graph hbox prev exp tenure, nooutsides

0 5 10 15 20

excludes outside values

Prev. work exper. Curr. work exper.

This graph shows work experience

before one’s current job and work

experience at one’s current job.

Uses nlsw.dta & scheme vg outc

graph hbox prev exp tenure, nooutsides over(married)

0 5 10 15 20

married

single

excludes outside values

Prev. work exper. Curr. work exper.

We can further break these variables

down by marital status.

Uses nlsw.dta & scheme vg outc

graph hbox prev exp tenure, nooutsides over(married) over(union)

0 5 10 15 20 25

union

nonunion

married

single

married

single

excludes outside values

Prev. work exper. Curr. work exper.

We can take the last graph and add

another over() option to even further

break these variables down by whether

one belongs to a union. Note, however,

that we cannot add a third over()

option when we have multiple

y-variables, but we could add the by()

option; see Box : By (189).

Uses nlsw.dta & scheme vg outc

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.1 Specifying variables and groups, yvars and over 161

Now, let’s consider options that may be used in combination with the over() option

to customize the behavior of the graphs. We show how you can treat the levels of the ﬁrst

over() option as though they were multiple y-variables. You can also request that missing

values for the levels of the over() variables be displayed, and you can suppress empty cat-

egories when multiple over() options are used. These examples are shown below using the

vg s2m scheme.

graph hbox wage, nooutsides over(grade4) over(union)

Consider this graph where we show

wages broken down by education level

and whether one belongs to a union.

Uses nlsw.dta & scheme vg s2m

0 5 10 15 20

hourly wage

union

nonunion

Coll Grad

Some Coll

HS Grad

Not HS

Coll Grad

Some Coll

HS Grad

Not HS

excludes outside values

graph hbox wage, nooutsides over(grade4) over(union) asyvars

If we add the asyvars option, then the

ﬁrst over() variable (grade4)is

graphed as if there were four y-variables

corresponding to each level of grade4.

Each level of grade4 is shown as a

diﬀerently colored/shaded box and

labeled using the legend.

Uses nlsw.dta & scheme vg s2m 0 5 10 15 20

hourly wage

union

nonunion

excludes outside values

Not HS HS Grad

Some Coll Coll Grad

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

162 Chapter 5. Box plots

graph hbox wage, nooutsides over(grade4) over(union) asyvars missing

0 10 20 30

hourly wage

union

nonunion

excludes outside values

Not HS HS Grad

Some Coll Coll Grad

By adding the missing option to the

previous graph, we see a category for

those who are missing on the union

variable, shown as the third group,

which is labeled with a dot to indicate

that those values are missing; see

Box : Cat axis (168) to see how you

could label this diﬀerently (e.g.,

labeling it with the word “Missing”).

Uses nlsw.dta & scheme vg s2m

graph box wage, nooutsides over(grade) over(collgrad)

0 10 20 30

hourly wage

not college grad college grad

4 5 6 7 8 9 101112131415161718 4 5 6 7 8 9 101112131415161718

excludes outside values

Consider this box chart that breaks

wages down by two variables: the last

grade that one completed and whether

one is a college graduate. By default,

Stata shows all possible combinations

for these two variables. In most cases,

all combinations are possible, but not

in this case.

Uses nlsw.dta & scheme vg s2m

graph box wage, nooutsides over(grade) over(collgrad) nofill

0 10 20 30

hourly wage

not college grad college grad

4 5 6 7 8 9 10 11 12 13 14 15 13 14 15 16 17 18

excludes outside values

If you only want to display the

combinations of the over() variables

that exist in the data, then you can use

the nofill option.

Uses nlsw.dta & scheme vg s2m

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.2 Options for groups, over options 163

5.2 Options for groups, over options

This section considers some of the options that can be used with the over() and

yvaroptions() options for customizing the display of the boxes. We will focus on con-

trolling the spacing between the boxes and the order in which the boxes are displayed.

Other options that control the display of the x-axis, such as the labels, are covered in

Box : Cat axis (168). For more information on the over() options covered in this section,

see the over subopts table in [G]graph box. We begin by considering options that control

the spacing among the boxes and use the vg past scheme.

graph hbox tenure, nooutsides over(occ5) over(collgrad)

Consider this graph that shows box

plots of tenure broken down by occ5

and collgrad.Weusethenooutsides

option to suppress the display of

outside values. For the rest of the

graphs in this section, there would be a

large number of outside values, which

would make the graphs very cluttered,

so we will include the nooutsides

option for each example.

Uses nlsw.dta & scheme vg past 0 5 10 15 20

Curr. work exper.

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

graph hbox tenure, nooutsides over(occ5, gap(*3)) over(collgrad)

We can change the gap between the

levels of occ5. Here, we make that gap

twice as large as it normally would.

This leads to narrow boxes with a

sizable gap between them.

Uses nlsw.dta & scheme vg past

0 5 10 15 20

Curr. work exper.

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

164 Chapter 5. Box plots

graph hbox tenure, nooutsides over(occ5, gap(*.2)) over(collgrad)

0 5 10 15 20

Curr. work exper.

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

Here, we shrink the gap between the

levels of collgrad, making the gaps

20% of the size they normally would.

This yields boxes that are wider than

they normally would.

Uses nlsw.dta & scheme vg past

graph hbox tenure, nooutsides over(occ5, gap(*.4)) over(collgrad, gap(*2))

0 5 10 15 20

Curr. work exper.

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

We can control the gap with respect to

each of the over() variables. In this

example, we make the gap among the

occ5 categories small (40% of their

original size) and the gap between the

levels of collgrad larger (two times the

normal size).

Uses nlsw.dta & scheme vg past

By default, the boxes formed by over() variables are ordered in ascending sequence

according to the values of the over() variable. Stata allows us to control the order of the

boxes by allowing us to put them in descending order, order them according to the values of

another variable, or sort the boxes according to their medians. These options are illustrated

in the following examples.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.2 Options for groups, over options 165

graph hbox tenure, nooutsides over(occ7, descending)

Consider this graph showing tenure

broken down by the seven levels of

occupation. The boxes would normally

be ordered by levels of occ7, going from

1to7.Thedescending option

switches the order of the boxes. They

still are ordered according to the seven

levels of occupation, but the boxes are

ordered going from 7 to 1.

Uses nlsw.dta & scheme vg past 0 5 10 15 20 25

Curr. work exper.

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

excludes outside values

graph hbox tenure, nooutsides over(occ7, sort(occ7alpha))

We might want to put these boxes in

alphabetical order, but with Other still

appearing last. We can do this by

recoding occ7 into a new variable (say

occ7alpha) such that, as occ7alpha

goes from 1 to 7, the occupations

alphabetically ordered. We recoded

occ7 with these assignments: 4 = 1,

6=2,2=3,5=4,1=5,3=6,and

7 = 7. Then, the sort(occ7alpha)

option alphabetizes the boxes (but with

Other still appearing last).

Uses nlsw.dta & scheme vg past

0 5 10 15 20 25

Curr. work exper.

Other

Sales

Prof

Operat.

Mgmt

Labor

Cler.

excludes outside values

graph hbox tenure, nooutsides over(occ7, sort(1))

Here, we sort the variables based on the

median of tenure, yielding boxes with

medians in ascending order. The

sort(1) option sorts the boxes

according to the median of the ﬁrst

y-variable, meaning to sort on the

median of tenure.

Uses nlsw.dta & scheme vg past

0 5 10 15 20 25

Curr. work exper.

Other

Prof

Mgmt

Operat.

Sales

Labor

Cler.

excludes outside values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

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published form of the book may be distributed or reproduced, either electronically or in printed form.

166 Chapter 5. Box plots

graph hbox tenure, nooutsides over(occ7, sort(1) descending)

0 5 10 15 20 25

Curr. work exper.

Cler.

Labor

Sales

Operat.

Mgmt

Prof

Other

excludes outside values

Adding the descending option yields

boxes in descending order, going from

highest median tenure to lowest

median tenure.

Uses nlsw.dta & scheme vg past

graph hbox prev exp tenure, nooutsides over(occ7)

0 5 10 15 20 25

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

excludes outside values

Prev. work exper. Curr. work exper.

Here, we plot two y-variables: the

number of years of work experience

before one’s current job and the years

in one’s current job. Since we have

removed any sort() options, the boxes

are sorted according to the values of

occ7.

Uses nlsw.dta & scheme vg past

graph hbox prev exp tenure, nooutsides over(occ7, sort(1))

0 5 10 15 20 25

Mgmt

Prof

Cler.

Sales

Labor

Operat.

Other

excludes outside values

Prev. work exper. Curr. work exper.

Adding the sort(1) option now sorts

the boxes according to the median of

prev exp since that is the ﬁrst

y-variable.

Uses nlsw.dta & scheme vg past

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.2 Options for groups, over options 167

graph hbox prev exp tenure, nooutsides over(occ7, sort(2))

Changing sort(1) to sort(2) then

sorts the boxes according to the median

of the second y-variable, tenure.

Uses nlsw.dta & scheme vg past

0 5 10 15 20 25

Other

Prof

Mgmt

Operat.

Sales

Labor

Cler.

excludes outside values

Prev. work exper. Curr. work exper.

graph hbox tenure, nooutsides over(occ7, sort(1)) over(collgrad)

We can use the sort() option when

there are additional over() variables.

Here, the boxes are ordered according

to the median of tenure across occ7

but within each level of collgrad.

Uses nlsw.dta & scheme vg past

0 5 10 15 20 25

Curr. work exper.

college grad

not college grad

Other

Mgmt

Prof

Sales

Cler.

Labor

Operat.

Prof

Mgmt

Operat.

Sales

Other

Labor

Cler.

excludes outside values

graph hbox tenure, nooutsides over(occ7, sort(1)) over(collgrad, descending)

We add the descending option to the

second over() option, and the levels of

collgrad are now shown with college

graduates appearing ﬁrst.

Uses nlsw.dta & scheme vg past

0 5 10 15 20 25

Curr. work exper.

not college grad

college grad

Prof

Mgmt

Operat.

Sales

Other

Labor

Cler.

Other

Mgmt

Prof

Sales

Cler.

Labor

Operat.

excludes outside values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

168 Chapter 5. Box plots

5.3 Controlling the categorical axis

This section describes ways that you can label categorical axes. Box plots are similar

to bar charts, but they are diﬀerent from other graphs because their x-axes are repre-

sented by categorical variables. This section describes options you can use to customize

these categorical axes. For more details on this, see [G]cat axis label options and

[G]cat axis line options.

We will start by showing examples of how you can change the labels for the x-axis for

these categorical variables. The next set of examples will use the vg teal scheme.

graph box wage, nooutsides over(south)

hourly wage

0 1

excludes outside values

This is an example of a box plot with

one over() variable graphing wages

broken down by whether one lives in

the South. The variable south is a

dummy variable that does not have any

value labels, so the x-axis is not labeled

very well. We use the nooutsides

option to suppress the display of

outside values. For the rest of the

graphs in this section, there would be a

large number of outside values, which

would make the graphs very cluttered,

so we will include the nooutsides

option for each example.

Uses nlsw.dta & scheme vg teal

graph box wage, nooutsides over(south, relabel(1 "N & W" 2 "South"))

hourly wage

N & W South

excludes outside values

We can use the relabel() option to

change the labels displayed for the

levels of south, giving the x-axis more

meaningful labels. Note that we wrote

relabel(1 "N & W"),notrelabel(0

"N & W"), since these numbers do not

represent the actual levels of south but

the ordinal position of the levels, i.e.,

ﬁrst and second.

Uses nlsw.dta & scheme vg teal

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.3 Controlling the categorical axis 169

graph box wage, nooutsides over(south, relabel(1 "N & W" 2 "South"))

over(smsa, relabel(1 "Non Metro" 2 "Metro"))

This is an example of a box plot with

two over() variables. Here, we use the

relabel() option to change the labels

displayed for the levels of south and

smsa.

Uses nlsw.dta & scheme vg teal

hourly wage

Non Metro Metro

N & W South N & W South

excludes outside values

graph box prev exp tenure ttl exp, nooutsides ascategory

This shows a box plot with multiple

y-variables but uses the ascategory

option to plot the diﬀerent y-variables

as if they were categorical variables.

The boxes for the diﬀerent variables are

the same color, and the categories are

labeled on the x-axis rather than with a

legend. The default labels on the x-axis

are not bad, but we might want to

change them.

Uses nlsw.dta & scheme vg teal 0

Prev. work exper. Curr. work exper. Tot. work exper.

excludes outside values

graph box prev exp tenure ttl exp, nooutsides ascategory

yvaroptions(relabel(1 "Prev Exp" 2 "Curr Exp" 3 "Tot Exp"))

If we had an over() option, we would

use the relabel() option to change the

labels on the x-axis. But since we had

multiple y-variables that we have

treated as categories, we then use the

yvaroptions(relabel()) option to

modify the labels on the x-axis.

Uses nlsw.dta & scheme vg teal

Prev Exp Curr Exp Tot Exp

excludes outside values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

170 Chapter 5. Box plots

graph box prev exp tenure ttl exp, nooutsides ascategory

over(south, relabel(1 "N & W" 2 "South"))

yvaroptions(relabel(1 "Prev Exp" 2 "Curr Exp" 3 "Tot Exp"))

N & W South

Prev Exp Curr Exp Tot Exp Prev Exp Curr Exp Tot Exp

excludes outside values

This example is similar to the previous

example, but we have added an over()

variable as well. As before, we use

yvaroptions(relabel()) to modify

the labels for the multiple y-variables,

and then we also use the relabel()

option within the over() option to

change the labels for south.

Uses nlsw.dta & scheme vg teal

graph box prev exp tenure ttl exp, nooutsides ascategory xalternate

over(south, relabel(1 "N & W" 2 "South"))

yvaroptions(relabel(1 "Prev Exp" 2 "Curr Exp" 3 "Tot Exp"))

N & W South

Prev Exp Curr Exp Tot Exp Prev Exp Curr Exp Tot Exp

excludes outside values

We add the xalternate option, which

moves the labels for the x-axis to the

opposite side, in this case from the

bottom to the top. You can also use

the yalternate option to move the

y-axis to its opposite side.

Uses nlsw.dta & scheme vg teal

In the examples above, we have seen that, even though the relabel() option is called

an over() option, it can be used within yvaroptions() to control the labeling of multiple

y-variables (provided that the ascategory option is used to convert the multiple y-variables

into categories). We will next explore other over() options, which also can be used with

either over() or yvaroptions(). These examples will use the vg rose scheme.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5.3 Controlling the categorical axis 171

graph box wage, nooutsides over(occ7, label(angle(45))) over(collgrad)

In this example, the levels of occ7

might overlap each other. Using the

label(angle(45)) option makes the

angle of the labels for occ7 45 degrees,

and they do not overlap.

Uses nlsw.dta & scheme vg rose

hourly wage

not college grad college grad

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

excludes outside values

graph box wage, nooutsides over(occ7, label(alternate)) over(collgrad)

Another way we can avoid overlapping

is by adding the label(alternate)

option. As you can see, the labels

alternate in height, avoiding

overlapping.

Uses nlsw.dta & scheme vg rose

hourly wage

not college grad college grad

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

excludes outside values

graph box wage, nooutsides over(occ7, label(labsize(small))) over(collgrad)

We can instead make the size of the

labels smaller to make them ﬁt without

overlapping. Here, we make the label

size small using the

label(labsize(small)) option. See

Styles : Textsize (344) for other values

you could choose for labsize().

Uses nlsw.dta & scheme vg rose

hourly wage

not college grad college grad

Prof Mgmt Sales Cler. Operat.Labor Other Prof Mgmt Sales Cler. Operat. Labor Other

excludes outside values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

172 Chapter 5. Box plots

graph hbox wage, nooutsides over(occ5, label(labcolor(maroon)))

over(collgrad)

0 5 10 15 20 25

hourly wage

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

Using the label(labcolor(maroon))

option, we change the label color for

occ5 to maroon. See Styles : Colors

(328) for more details about other

colors you could choose.

Uses nlsw.dta & scheme vg rose

graph hbox wage, nooutsides

over(occ5, label(ticks tlwidth(thick) tlength(*2) tposition(crossing)))

over(collgrad)

0 5 10 15 20 25

hourly wage

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

We can use the label(ticks) option

to place ticks under each box. We also

modify the attributes of the ticks,

making the tick thick, twice as long as

normal, and crossing the x-axis. See

[G]cat axis label options for more

details and other options for controlling

ticks.

Uses nlsw.dta & scheme vg rose

graph hbox wage, nooutsides over(occ5, label(labgap(*5))) over(collgrad)

0 5 10 15 20 25

hourly wage

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

The label(labgap(*5)) option

controls the gap between the label and

the ticks. Here, we increase the gap

between the label for the levels of occ5

and the axis line to ﬁve times its

normal size.

Uses nlsw.dta & scheme vg rose

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5.3 Controlling the categorical axis 173

graph hbox wage, nooutsides over(occ5) over(collgrad, label(labgap(*7)))

Using the label(labgap(*7)) option,

we increase the gap associated with

collgrad. This example makes the gap

between collgrad and occ5 seven

times its normal size.

Uses nlsw.dta & scheme vg rose

0 5 10 15 20 25

hourly wage

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

graph box wage, nooutsides over(occ7, axis(outergap(*20)))

We use the axis(outergap()) option

to increase the gap between the labels

of the x-axis and the outside of the

graph. As you can see, this increases

the space between the labels for occ7

and the bottom of the graph.

Uses nlsw.dta & scheme vg rose

hourly wage

Prof Mgmt Sales Cler. Operat. Labor Other

excludes outside values

So far, we have focused on labeling the values on the categorical x-axis, but we have not

yet looked at how to add a title to that axis. We might be tempted to use xtitle(), but

that option is not valid for a categorical axis. Instead, we can use other means for giving

titles to these axes, as illustrated in the examples below using the vg s1c scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

174 Chapter 5. Box plots

graph box wage, over(grade6) nooutsides

b1title("Level of Education") b2title("in six categories")

0 5 10 15 20

hourly wage

No HS Some HS HS Grad Some Coll Coll Grad Post Grad

Level of Education

in six categories

excludes outside values

In this example, the categorical axis

represents the level of education, and

we can use the b1title() and

b2title() options to add titles to the

bottom of the graph. See Standard

options : Titles (313) for more details.

Uses nlsw.dta & scheme vg s1c

graph hbox wage, over(grade6) nooutsides

l1title("Level of Education" "in six categories")

0 5 10 15 20

hourly wage

Post Grad

Coll Grad

Some Coll

HS Grad

Some HS

No HS

Level of Education

in six categories

excludes outside values

By using graph hbox, the categorical

axis is now on the left axis, so we then

use the l1title() to add a title to the

x-axis. We could also use the

l2title() to add a second title as well.

Uses nlsw.dta & scheme vg s1c

5.4 Controlling legends

This section discusses the use of legends for box charts, emphasizing the features that

are unique to box charts. The section Options : Legend (287) goes into great detail about

legends, as does [G]legend option. Legends can be used for multiple y-variables or when

the ﬁrst over() variable is treated as a y-variable via the asyvars option. See Box : Yvars

and over (157) for more information about using multiple y-variables and more examples

of treating the ﬁrst over() variable as a y-variable. These ﬁrst examples use the vg brite

scheme.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5.4 Controlling legends 175

graph box prev exp tenure ttl exp, nooutsides

Consider this box plot of three diﬀerent

variables. These variables are shown

with diﬀerent colors, and a legend is

used to identify the variables. We use

the nooutsides option to suppress the

display of outside values. For the rest

of the graphs in this section, there

would be a large number of outside

values, which would make the graphs

very cluttered, so we will include the

nooutsides option for each example.

Uses nlsw.dta & scheme vg brite

0 5 10 15 20 25

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

graph box wage, nooutsides over(occ7) asyvars

This is another example of where a

legend can arise in a Stata box plot by

using the asyvars option, which treats

an over() variable as though the levels

were diﬀerent y-variables.

Uses nlsw.dta & scheme vg brite

0 5 10 15 20 25

hourly wage

excludes outside values

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Unless otherwise mentioned, the legend() options described below work the same

whether the legend was derived from multiple y-variables or from an over() option that

was combined with the asyvars option. These examples use the vg teal scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

176 Chapter 5. Box plots

graph box prev exp tenure ttl exp, nooutsides nolabel

excludes outside values

prev_exp tenure

ttl_exp

The nolabel option only works when

you have multiple y-variables. When

this option is used, the variable names

(not the variable labels) are used in the

legend. For example, instead of showing

the variable label Prev. work exper.,

it shows the variable name prev exp.

Uses nlsw.dta & scheme vg teal

graph box prev exp tenure ttl exp, nooutsides showyvars

Prev. work exper. Curr. work exper. Tot. work exper.

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

The showyvars option puts the labels

under the boxes.

Uses nlsw.dta & scheme vg teal

graph box prev exp tenure ttl exp, nooutsides showyvars legend(off)

Prev. work exper. Curr. work exper. Tot. work exper.

excludes outside values

One instance when the showyvars

option would be useful is when you

want separately colored boxes labeled

at the bottom. Here, we use showyvars

to show the labels at the bottom of the

boxes and the legend(off) option to

suppress the display of the legend.

Uses nlsw.dta & scheme vg teal

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5.4 Controlling legends 177

graph box wage, nooutsides over(occ7) asyvars showyvars legend(off)

Even though the showyvars option

sounds like it would work only with

multiple y-variables, it also works when

you combine the over() and asyvars

options. As before, we suppress the

legend in this example using the

legend(off) option.

Uses nlsw.dta & scheme vg teal

hourly wage

Prof Mgmt Sales Cler. Operat. Labor Other

excludes outside values

graph box wage, nooutsides over(occ7) asyvars

legend(label(1 "Professional") label(2 "Management"))

We use the legend(label()) option to

change the labels for the ﬁrst and

second variables in the legend. Note

that you use a separate label() option

for each bar. This is in contrast to the

relabel() option, where all the label

assignments were placed in one

relabel() option; see Box : Cat axis

(168).

Uses nlsw.dta & scheme vg teal

hourly wage

excludes outside values

Professional Management

Sales Cler.

Operat. Labor

Other

graph box wage, nooutsides over(occ7) asyvars legend(rows(2) colfirst)

In this example, we use the

legend(rows(2) colfirst) options to

display the legend in two rows and to

order the keys by column (instead of

the default, which is by row). This

yields keys that are more adjacent to

the boxes that they label.

Uses nlsw.dta & scheme vg teal

hourly wage

excludes outside values

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

178 Chapter 5. Box plots

graph box wage, nooutsides over(occ7) asyvars

legend(position(1))

hourly wage

excludes outside values

Prof Mgmt

Sales Cler.

Operat. Labor

Other

We can put the legend up in the top

right corner of the graph with the

legend(position(1)) option. The

values you supply for position() are

like the numbers on a clock face, where

12 o’clock is the top, 6 o’clock is the

bottom, and 0 represents the center of

the clock face; see Styles : Clockpos (330)

for more details.

Uses nlsw.dta & scheme vg teal

graph hbox wage, nooutsides over(occ7) asyvars

legend(cols(1) position(9))

0 5 10 15 20 25

hourly wage

excludes outside values

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

We switch to making this a horizontal

box chart and then move the legend

using the legend(position(9))

option. The legend is now placed in the

9 o’clock position and is displayed as a

single column.

Uses nlsw.dta & scheme vg teal

graph hbox wage, nooutsides over(occ7) asyvars

legend(cols(1) position(9) textfirst)

0 5 10 15 20 25

hourly wage

excludes outside values

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

We can add the textfirst option to

put the key description before the key

in the legend.

Uses nlsw.dta & scheme vg teal

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5.5 Controlling the y-axis 179

graph hbox wage, nooutsides over(occ7) asyvars

legend(cols(1) position(9) stack)

With the stack option, we can place

the keys and their labels on top of each

other to form an even more compact

column. You have considerable control

over the elements within the legend

using other options like rowgap(),

keygap(),symxsize(),symysize(),

textwidth(),andsymplacement().

See Options : Legend (287) and

[G]legend option for more details.

Uses nlsw.dta & scheme vg teal 0 5 10 15 20 25

hourly wage

excludes outside values

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

graph hbox wage, nooutsides over(occ7) asyvars

Switching to the vg lgndc scheme, by

typing set scheme vg lgndc, positions

the legend at the left in a single

column, by default, without the need to

specify options.

Uses nlsw.dta & scheme vg lgndc

0 5 10 15 20 25

hourly wage

excludes outside values

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

5.5 Controlling the y-axis

This section describes options you can use with respect to the y-axis with box charts.

To be precise, when Stata refers to the y-axis on a box chart, it refers to the axis with the

continuous variable, whether the left axis when using graph box or the bottom axis when

using graph hbox. This section emphasizes the features that are particularly relevant to

box charts. For more details, see Options : Axis titles (254), Options : Axis labels (256), and

Options : Axis scales (265). See also [G]axis title options,[G]axis label options,and

[G]axis scale options. These examples are shown using the vg lgndc scheme, which

places the legend to the left in a single column.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

180 Chapter 5. Box plots

graph box wage, nooutside over(occ5)

0 5 10 15 20

hourly wage

Prof/Mgmt Sales Clerical Labor/Ops Other

excludes outside values

Consider this graph showing the hourly

wages broken down by occupation. We

use the nooutsides option to suppress

the display of outside values. For the

rest of the graphs in this section, there

would be a large number of outside

values, which would make the graphs

very cluttered, so we will include the

nooutsides option for each example.

Uses nlsw.dta & scheme vg lgndc

graph box prev exp tenure, nooutside over(occ5)

ytitle("Years of experience")

0 5 10 15 20

Years of experience

Prof/Mgmt Sales Clerical Labor/Ops Other

excludes outside values

Prev. work exper.

Curr. work exper.

Looking at previous and current work

experience over occupations, we can use

the ytitle() option to add a title to

the y-axis. See Options : Axis titles (254)

and [G]axis title options for more

details, but please disregard any

references to xtitle() there since that

option is not valid when using graph

box.

Uses nlsw.dta & scheme vg lgndc

graph hbox prev exp tenure, nooutside over(occ5)

ytitle("Years of" "experience")

0 5 10 15 20

Years of

experience

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

Prev. work exper.

Curr. work exper.

In this example, we place the title

across two lines by using two separate

quoted strings. Note that, even though

we have used graph hbox to place the

y-axis on the bottom axis, we still

should use ytitle() to change the title

of that axis.

Uses nlsw.dta & scheme vg lgndc

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.5 Controlling the y-axis 181

graph hbox prev exp tenure, nooutside over(occ5)

ytitle("Years of experience", size(vlarge) box bexpand)

Because this title is considered to be a

textbox, you can use a variety of

textbox options to control the look of

the title. This example makes the title

very large, surrounds it with a box, and

uses the bexpand (box expand) option

to stretch the box to ﬁll the width of

the plot area. See Options : Textboxes

(303) for additional examples of how to

use textbox options to control the

display of text.

Uses nlsw.dta & scheme vg lgndc

0 5 10 15 20

Years of experience

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

excludes outside values

Prev. work exper.

Curr. work exper.

graph box wage, nooutside over(occ5) over(collgrad) asyvar

yline(4 12, lwidth(medthick) lcolor(maroon) lpattern(dash))

In this example, we use the yline()

option to add a medium-thick, maroon,

dashed line to the points in the graph

where wages equal 4 and 12. Note that

we would still use yline(),evenifwe

used graph hbox, placing the y-axis at

the bottom.

Uses nlsw.dta & scheme vg lgndc

0 5 10 15 20 25

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

graph box wage, nooutside over(occ5) over(collgrad) asyvar

ylabel(5(10)25)

We can use the ylabel() option to

label the y-axis. In this case, we use the

labels going from 5 to 25 by increments

of 10. Note that the y-axis still starts

at 0, and we would have to supply the

exclude0 option, so 0 is not necessarily

the starting point for the y-axis. See

Options : Axis labels (256) and

[G]axis label options for more

details. Please disregard any references

to xlabel() since that option is not

valid when using graph box.

Uses nlsw.dta & scheme vg lgndc

5 15 25

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

182 Chapter 5. Box plots

graph box wage, nooutside over(occ5) over(collgrad) asyvar

ylabel(5(10)25, angle(0))

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

We can add the angle(0) option to

modify the angle of the y-labels, in this

case making them display horizontally.

Uses nlsw.dta & scheme vg lgndc

graph box wage, nooutside over(occ5) over(collgrad) asyvar

ylabel(5(10)25, nogrid)

5 15 25

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

The nogrid option suppresses the

display of the grid. Note that this

option is placed within the ylabel()

option, thus suppressing the grid for

the y-axis. (With box plots, there is

never a grid with respect to the x-axis.)

If the grid were absent and we wanted

to include it, we could add the grid

option. For more details, see

Options : Axis labels (256).

Uses nlsw.dta & scheme vg lgndc

graph box wage, nooutside over(occ5) over(collgrad) asyvar yscale(off)

not college grad college grad

excludes outside values

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

We can use yscale(off) to turn oﬀ the

y-axis. See Options : Axis scales (265)

and [G]axis scale options for more

details. Please disregard any references

to xscale(), since that option is not

valid when using graph box.

Uses nlsw.dta & scheme vg lgndc

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5.6 Changing the look of boxes, boxlook options 183

graph box wage, nooutside over(occ5) over(collgrad) asyvar yalternate

We can put the y-axis on the opposite

side, in this case on the right side of the

graph, using the yalternate option.

Uses nlsw.dta & scheme vg lgndc

0 5 10 15 20 25

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

graph box wage, nooutside over(occ5) over(collgrad) asyvar yreverse

You can reverse the direction of the

y-axis, in eﬀect turning your boxes

upside down, with the yreverse

option.

Uses nlsw.dta & scheme vg lgndc

0510152025

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt

Sales

Clerical

Labor/Ops

Other

5.6 Changing the look of boxes, boxlook options

This section shows how you can control the look of the boxes in your box charts: con-

trol the space between the boxes, the color of the boxes, and the characteristics of the line

outlining the boxes. For more information, see the boxlook options table in [G]graph box.

These examples begin with the vg blue scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

184 Chapter 5. Box plots

graph box prev exp tenure ttl exp, over(collgrad)

not college grad college grad

Prev. work exper. Curr. work exper.

Tot. work exper.

Consider this box chart, which shows

the distribution of previous work

experience, current work experience,

and total work experience. These three

variables are broken down by whether

one graduated college.

Uses nlsw.dta & scheme vg blue

graph box prev exp tenure ttl exp, nooutsides over(collgrad)

not college grad college grad

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

We add the nooutsides option to

suppress the display of outside values.

We will use this option for most of the

graphs in this section.

Uses nlsw.dta & scheme vg blue

graph box prev exp tenure ttl exp, nooutsides over(collgrad)

outergap(*5)

not college grad college grad

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

We can change the outer gap between

the boxes and the edge of the plot area

with the outergap() option. Here, the

gap is ﬁve times its normal size. You

could also supply a value less than 1 to

shrink the size of the outer gap.

Uses nlsw.dta & scheme vg blue

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5.6 Changing the look of boxes, boxlook options 185

graph box prev exp tenure ttl exp, nooutsides over(collgrad)

boxgap(10)

The boxgap() option controls the size

of the gap among the boxes formed by

the multiple y-variables. The default

value is 33, meaning that the distance

between the boxes is 33% of the width

of the boxes. Here, we make the gap

smaller, making the boxes for the

y-variables closer to each other.

Uses nlsw.dta & scheme vg blue 0

not college grad college grad

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

graph box prev exp tenure ttl exp, nooutsides over(collgrad, gap(*3))

Here, we use the gap() option to

control the gap between the college

graduate group and the noncollege

graduate group. Here, we make the gap

three times the width of a box. See

Box : Over options (163) for more

information about controlling the gap

among boxes created by the over()

option.

Uses nlsw.dta & scheme vg blue

not college grad college grad

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

Let’s now look at options that allow us to control the color of the boxes. We will ﬁrst

look at options that control the overall intensity of the color for all the boxes and then show

how you can control the color of each box. We will use the vg s2c scheme for the following

examples.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

186 Chapter 5. Box plots

graph box wage, over(occ5) over(collgrad) asyvars nooutsides intensity(*.5)

0 5 10 15 20 25

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt Sales

Clerical Labor/Ops

Other

The intensity option controls the

intensity of the color within the boxes.

Here, we request that the color be 50%

as intense as it normally would be.

Uses nlsw.dta & scheme vg s2c

graph box wage, over(occ5) over(collgrad) asyvars nooutsides intensity(*1.5)

0 5 10 15 20 25

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt Sales

Clerical Labor/Ops

Other

In this example, we use the intensity

option to make the colors within the

boxes 1.5 times more intense than they

would normally.

Uses nlsw.dta & scheme vg s2c

graph box wage, over(occ5) over(collgrad) asyvars nooutsides

box(1, bcolor(sand))

0 5 10 15 20 25

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt Sales

Clerical Labor/Ops

Other

Here, we add box(1, bcolor(sand))

to make the box color for the ﬁrst bar a

sand color. See Styles : Colors (328) for

more information about colors you can

select.

Uses nlsw.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.6 Changing the look of boxes, boxlook options 187

graph box wage, over(occ5) over(collgrad) asyvars nooutsides

box(1, bcolor(sand) blcolor(brown) blwidth(thick))

We add the blcolor() (box line color)

and blwidth() (box line width) options

to make the outline for the ﬁrst box

brown and thick. Note that, while you

can control the color of the boxes and

outline characteristics via the box()

option, if you want to extensively

change these characteristics for many

graphs, you might consider making

your own scheme. See Intro : Schemes

(14) and Appendix : Customizing schemes

(379).

Uses nlsw.dta & scheme vg s2c

0 5 10 15 20 25

hourly wage

not college grad college grad

excludes outside values

Prof/Mgmt Sales

Clerical Labor/Ops

Other

Now, let’s consider options that allow us to control the display of the median, whiskers,

caps, and outside markers. These examples use the vg s1m scheme.

graph box prev exp tenure ttl exp, nooutsides

medtype(cline) medline(lwidth(thick) lcolor(black))

The medtype(cline) option sets the

median type to be a custom line. We

then customize the median line using

the medline() option to specify that

the line width be thick and the line

color be black.

Uses nlsw.dta & scheme vg s1m

0 5 10 15 20 25

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

188 Chapter 5. Box plots

graph box prev exp tenure ttl exp, nooutsides

medtype(marker) medmarker(msymbol(+) msize(large))

0 5 10 15 20 25

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

We can use the medtype(marker)

option to tell Stata that we want to use

a marker symbol to label the median

and then use the medmarker() option

to control the display of the median

marker. In this case, we make the

marker symbol a plus sign and make

the marker size large.

Uses nlsw.dta & scheme vg s1m

graph box prev exp tenure ttl exp, nooutsides

cwhiskers lines(lwidth(thick) lcolor(black))

0 5 10 15 20 25

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

To customize the whiskers, we need to

specify the cwhiskers (customize

whiskers) option, and then we can add

the lines() option to specify how we

want the whiskers customized. In this

case, we make the whiskers thick and

black.

Uses nlsw.dta & scheme vg s1m

graph box prev exp tenure ttl exp, nooutsides alsize(20)

0 5 10 15 20 25

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

The alsize() (adjacent line size)

option allows you to control the size

(width) of the adjacent line. By

default, the adjacent line is 67% of the

width of the box. Here, we make the

adjacent line much smaller, 20% of the

width of the box.

Uses nlsw.dta & scheme vg s1m

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published form of the book may be distributed or reproduced, either electronically or in printed form.

5.7 Graphing by groups 189

graph box prev exp tenure ttl exp, nooutsides capsize(5)

The capsize() option allows you to

specify the size of the caps (if any) on

the adjacent line. The default value is

0, meaning that no cap is displayed.

Here, we add a small cap to the

adjacent line.

Uses nlsw.dta & scheme vg s1m

0 5 10 15 20 25

excludes outside values

Prev. work exper. Curr. work exper.

Tot. work exper.

graph box prev exp tenure ttl exp, marker(2, msymbol(Oh) msize(vlarge))

The marker() option allows you to

control the markers used to display the

outside values. You can control this

separately for each y-variable. Here, we

make the outside value for tenure

display as large, hollow circles.

Uses nlsw.dta & scheme vg s1m

0 10 20 30

Prev. work exper. Curr. work exper.

Tot. work exper.

5.7 Graphing by groups

This section discusses the use of the by() option in combination with graph box.Nor-

mally, you would use the over() option instead of the by() option, but in some cases the

by() option is either necessary or more advantageous. For example, a by() option is useful

if you exceed the maximum number of over() options (three if you have a single y-variable

or two if you have multiple y-variables). In such cases, the by() option allows you to break

your data down by additional categorical variables. Also, by() gives you more ﬂexibility

in the placement of the separate panels. For more information about the by() option, see

Options : By (272); for more information about the over() option, see Box : Yvars and over

(157).

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

190 Chapter 5. Box plots

graph hbox wage, nooutsides note("")

over(collgrad) over(urban2) over(married)

0 5 10 15 20

hourly wage

married

single

Metro

Rural

Metro

Rural

not college grad college grad

Consider this box graph, which breaks

wages down by three categorical

variables. If we wanted to further break

this down by another categorical

variable, we could not use another

over() option since we can have a

maximum of three over() options with

a single y-variable. We use the

nooutsides option to suppress the

display of outside values for this graph

and the rest of the graphs in this

section.

Uses nlsw.dta & scheme vg s1m

graph hbox wage, nooutsides note("")

over(collgrad) over(urban2) over(married) by(union)

0 5 10 15 20 0 5 10 15 20

married

single

married

single

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

nonunion union

not college grad college grad

hourly wage

Graphs by union worker

If we want to further break prev exp

down by union, we can use the

by(union) option to do this. We also

add the note("") option to suppress

the note saying that the outside values

have been omitted.

Uses nlsw.dta & scheme vg s1m

graph hbox prev exp tenure, nooutsides note("")

over(urban2) over(married)

0 5 10 15 20 25

married

single

Metro

Rural

Metro

Rural

Prev. work exper. Curr. work exper.

Consider this box graph with multiple

y-variables breaking them down by two

categorical variables using two over()

options. When you have multiple

y-variables, you can only have a

maximum of two over() options.

Uses nlsw.dta & scheme vg s1m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

5.7 Graphing by groups 191

graph box prev exp tenure, nooutsides note("")

over(urban2) over(married) by(union)

If we want to further break prev exp

down by another categorical variable,

say union,wecanusetheby(union)

option. We can include multiple

variables within by(), although this can

make some very small graphs.

Uses nlsw.dta & scheme vg s1m

0 5 10 15 20 25

single married single married

Rural Metro Rural Metro Rural Metro Rural Metro

nonunion union

Prev. work exper. Curr. work exper.

Graphs by union worker

graph hbox ttl exp tenure, nooutsides note("")

over(urban2) over(married) by(union, missing)

We can use the missing option to

include a panel for the missing values of

union.

Uses nlsw.dta & scheme vg s1m

0 5 10 15 20 25

married

single

married

single

married

single

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

nonunion union

(missing)

Tot. work exper. Curr. work exper.

Graphs by union worker

graph hbox ttl exp tenure, nooutsides note("")

over(urban2) over(married) by(union, total)

We can add the total option to include

a panel for all observations.

Uses nlsw.dta & scheme vg s1m

0 5 10 15 20 25

married

single

married

single

married

single

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

nonunion union

Total

Tot. work exper. Curr. work exper.

Graphs by union worker

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Boxlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

192 Chapter 5. Box plots

graph hbox ttl exp tenure, nooutsides note("")

over(urban2) over(married) by(union, total row(1))

0510152025 0510152025 0510152025

married

single

married

single

married

single

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

nonunion union Total

Tot. work exper. Curr. work exper.

Graphs by union worker

Switching to a vertical box chart, we

can use the rows(1) option to show the

multiple graphs in one row.

Uses nlsw.dta & scheme vg s1m

graph hbox ttl exp tenure, nooutsides note("")

over(urban2) over(married) by(union, cols(1))

0 5 10 15 20 25

married

single

married

single

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

nonunion

union

Tot. work exper. Curr. work exper.

Graphs by union worker

Here, we ﬂip the graph back to a

horizontal box chart and use the

cols(1) option to show both graphs in

one column.

Uses nlsw.dta & scheme vg s1m

graph hbox ttl exp tenure, nooutsides note("")

over(urban2) over(married) by(union, cols(1) legend(position(9)))

legend(cols(1) stack)

0 5 10 15 20 25

married

single

married

single

Metro

Rural

Metro

Rural

Metro

Rural

Metro

Rural

nonunion

union

Tot. work exper.

Curr. work exper.

Graphs by union worker

To make the last graph more readable,

we can add the legend(pos(9)) within

the by() option to put the legend at 9

o’clock and legend(cols(1) stack) to

make the legend one stacked column.

Adding note("") suppresses the note

about outside values being omitted.

Uses nlsw.dta & scheme vg s1m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

6 Dot plots

This chapter discusses the use of dot plots in Stata. We start by showing how you can

specify multiple y-variables to display plots for multiple variables and how you can use the

over() option to break dot plots down by categorical variables. Then, we discuss over()

options that can be used to customize the display of these categorical variables, followed by

options concerning the display of display of categorical axes. Next, we cover options that

control legends, followed by options that control the y-axis. Finally, we discuss options that

control the look of the lines and dots that form the dot plot and, lastly, the by() option.

6.1 Specifying variables and groups, yvars and over

This section introduces the use of dot plots. It shows how you can use the over()

option for displaying dot plots by one or more grouping variables. It then shows how you

can specify one or more y-variables in a plot and control the summary statistic used for

collapsing the y-variable(s). See the group options table in [G]graph dot for more details.

The graphs in this section begin using the vg s1c scheme.

graph dot tenure, over(occ7)

Here, we use the over() option to show

the average current work experience

broken down by occupation. By

default, the y-variable (tenure)is

placed on the bottom axis and is

considered to be the y-axis. Likewise,

the levels of occ7 are placed on the left

axis and are considered to form the

x-axis, or categorical axis.

Uses nlsw.dta & scheme vg s1c

0 2 4 6 8

mean of tenure

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

193

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

194 Chapter 6. Dot plots

graph dot tenure, over(occ7) over(collgrad)

0 2 4 6 8

mean of tenure

college grad

not college grad

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Here, we use a second over() option to

show the mean of work experience

broken down by occupation and

whether one graduated college.

Uses nlsw.dta & scheme vg s1c

graph dot tenure, over(occ7) over(collgrad) over(married)

0 2 4 6 8 10

mean of tenure

married

single

college grad

not college grad

college grad

not college grad

Prof Mgmt

Sales Cler.

Operat. Labor

Other

We can add a third over() option, in

this case further breaking the tenure

down by whether one is married. Note

that the ﬁrst over() variable (occ7)is

now treated as multiple y-variables.

When you use three over() options,

the ﬁrst variable is then treated as

multiple y-variables, as though you had

speciﬁed the asyvars option. This

graph can be diﬃcult to read with occ7

forming the multiple y-variables.

Uses nlsw.dta & scheme vg s1c

graph dot tenure, over(married) over(occ7) over(collgrad)

0 2 4 6 8 10

mean of tenure

college grad

not college grad

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

single married

This graph shows the same data as the

last one, except we have switched the

order of the over() options, making

over(married) come ﬁrst and thus

forming the multiple y-variables. This

might be easier to read than the

previous graph.

Uses nlsw.dta & scheme vg s1c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.1 Specifying variables and groups, yvars and over 195

Let’s now consider examples with multiple y-variables. These examples are shown using

the vg outc scheme.

graph dot prev exp tenure, over(occ7)

This graph shows the average previous

experience and average current tenure

broken down by occupation. While you

do not need to use the over() option,

omitting it may make a fairly boring

graph.

Uses nlsw.dta & scheme vg outc

0 2 4 6 8

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

graph dot prev exp tenure, over(occ7) over(collgrad)

This graph adds whether one is a

college graduate as an additional

grouping level. Because the command

has multiple y-variables, we cannot

include another over() option since

dot plots support three levels of nesting

and the multiple y-variables account for

a level.

Uses nlsw.dta & scheme vg outc

0 2 4 6 8 10

college grad

not college grad

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

196 Chapter 6. Dot plots

graph dot (median) prev exp tenure, over(occ7) over(collgrad)

0 2 4 6 8 10

college grad

not college grad

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

p 50 of prev_exp p 50 of tenure

So far, all the examples we have seen

have graphed the mean of y-variable(s).

Here, we preface the y-variables with

(median), plotting the median for each

y-variable.

Uses nlsw.dta & scheme vg outc

graph dot (p10) wage p10=wage (p25) wage p25=wage

(p50) wage p50=wage (p75) wage p75=wage (p90) wage p90=wage,

over(occ7)

0 5 10 15 20

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

p 10 of wage p 25 of wage

p 50 of wage p 75 of wage

p 90 of wage

You can request diﬀerent statistics for

the same variable, such as in this

example, which shows the 10th, 25th,

50th, 75th, and 90th percentiles of

wages broken down by occupation.

Uses nlsw.dta & scheme vg outc

Now, let’s consider options that can be used in combination with the over() option to

customize the behavior of the graphs. We show how you can treat the levels of the ﬁrst

over() option as though they were multiple y-variables. You can also request that missing

values for the levels of the over() variables be displayed, and you can suppress empty cat-

egories when multiple over() options are used. These examples are shown below using the

vg s2m scheme.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.1 Specifying variables and groups, yvars and over 197

graph dot tenure, over(collgrad) over(occ7)

Consider this graph, which shows the

average current work experience broken

down by whether one is a college

graduate and by occupation.

Uses nlsw.dta & scheme vg s2m

0 2 4 6 8

mean of tenure

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

college grad

not college grad

college grad

not college grad

college grad

not college grad

college grad

not college grad

college grad

not college grad

college grad

not college grad

college grad

not college grad

graph dot tenure, over(collgrad) over(occ7) asyvars

If we add the asyvars option, the ﬁrst

over() variable (collgrad) is graphed

as if there were two y-variables. The

two levels of collgrad are shown as

diﬀerent markers on the same line, and

they are labeled using the legend.

Uses nlsw.dta & scheme vg s2m

0 2 4 6 8

mean of tenure

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

not college grad college grad

graph dot tenure, over(occ5) over(union) missing

Consider this graph in which we use the

over() option to show tenure broken

down by occ5 and union. By including

the missing option, we then see the

category for those who are missing on

the union variable, shown as the third

group labeled with a dot. See

Dot : Cat axis (202) for examples

showing how you could change the label

(.) to something more meaningful, e.g.,

“Missing”.

Uses nlsw.dta & scheme vg s2m

0 2 4 6 8 10

mean of tenure

union

nonunion

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

198 Chapter 6. Dot plots

graph dot tenure, over(grade) over(collgrad)

0 2 4 6 8

mean of tenure

college grad

not college grad

4Consider this dot plot, which breaks

tenure down by two variables: the last

grade that one completed and whether

one is a college graduate. By default,

Stata shows all possible combinations

for these two variables. In most cases,

all combinations are possible, but not

in this case, and including them has

caused the labels for grade to overlap.

Uses nlsw.dta & scheme vg s2m

graph dot tenure, over(grade) over(collgrad) nofill

0 2 4 6 8

mean of tenure

college grad

not college grad

If you only want to display only the

combinations of the over() variables

that exist in the data, you can use the

nofill option.

Uses nlsw.dta & scheme vg s2m

6.2 Options for groups, over options

This section considers some of the options that can be used with the over() and

yvaroptions() options for customizing the display of the markers. We will focus on con-

trolling the spacing between the markers and the order in which the markers are displayed.

Other options that control the display of the x-axis (such as the labels) are covered in

Dot : Cat axis (202). For more information on the over() options covered in this section, see

the over subopts table in [G]graph dot. We ﬁrst consider options that control the spacing

among the markers and then options that change the order in which the markers are sorted.

These examples begin with the vg blue scheme.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.2 Options for groups, over options 199

graph dot tenure, over(occ5) over(collgrad)

Consider this graph in which we show a

dot plot of tenure broken down by

occ5 and collgrad.

Uses nlsw.dta & scheme vg blue

0 2 4 6 8

mean of tenure

college grad

not college grad

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

graph dot tenure, over(occ7) over(collgrad, gap(*5))

Suppose that we wanted to make the

gap between the levels of collgrad

larger. Here, we use the gap(*5) option

to make this gap ﬁve times as large as

it normally would be.

Uses nlsw.dta & scheme vg blue

0 2 4 6 8

mean of tenure

college grad

not college grad

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

graph dot tenure, over(occ7)

Consider this graph showing tenure

broken down by the seven levels of

occupation. The markers are ordered

by levels of occ7, going from 1 to 7.

Uses nlsw.dta & scheme vg blue

0 2 4 6 8

mean of tenure

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

200 Chapter 6. Dot plots

graph dot tenure, over(occ7, descending)

0 2 4 6 8

mean of tenure

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

The descending option switches the

order of the markers. They still are

ordered according to the seven levels of

occupation, but the markers are

ordered from 7 to 1.

Uses nlsw.dta & scheme vg blue

graph dot tenure, over(occ7, sort(occ7alpha))

0 2 4 6 8

mean of tenure

Other

Sales

Prof

Operat.

Mgmt

Labor

Cler.

We might want to put these markers in

alphabetical order (but with Other

appearing last). We can do this by

recoding occ7 into a new variable (say

occ7alpha), such that, as occ7alpha

goes from 1 to 7, the occupations are

alphabetical. We recoded occ7 with

these assignments: 4 = 1, 6 = 2, 2 = 3,

5=4,1=5,3=6,and7=7;see

[R]recode. Then, the

sort(occ7alpha) option alphabetizes

the markers (but with Other still

appearing last).

Uses nlsw.dta & scheme vg blue

graph dot tenure, over(occ7, sort(1))

0 2 4 6 8

mean of tenure

Other

Prof

Mgmt

Sales

Operat.

Labor

Cler.

Here, we sort the variables based on the

mean of tenure, yielding markers with

means in ascending order. The sort(1)

option sorts the markers according to

the mean of the ﬁrst y-variable, the

mean of tenure. In this case, there is

only one variable.

Uses nlsw.dta & scheme vg blue

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

6.2 Options for groups, over options 201

graph dot tenure, over(occ7, sort(1) descending)

Adding the descending option yields

markers in descending order, going

from highest mean tenure to lowest

mean tenure.

Uses nlsw.dta & scheme vg blue

0 2 4 6 8

mean of tenure

Cler.

Labor

Operat.

Sales

Mgmt

Prof

Other

graph dot tenure prev exp, over(occ7, sort(2))

Adding a second y-variable and

changing sort(1) to sort(2) sorts the

markers according to the second

y-variable, the mean of prev exp.

Uses nlsw.dta & scheme vg blue

0 2 4 6 8

Mgmt

Cler.

Prof

Sales

Labor

Operat.

Other

mean of tenure mean of prev_exp

graph dot tenure prev exp, over(occ7, sort(1)) over(collgrad)

We can use the sort() option when

there are additional over() variables.

Here, the markers are ordered

according to the mean of tenure within

each level of collgrad.

Uses nlsw.dta & scheme vg blue

0 2 4 6 8 10

college grad

not college grad

Other

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Prof

Mgmt

Sales

Operat.

Other

Labor

Cler.

mean of tenure mean of prev_exp

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

202 Chapter 6. Dot plots

graph dot tenure prev exp, over(occ7, sort(1)) over(collgrad, descending)

0 2 4 6 8 10

not college grad

college grad

Prof

Mgmt

Sales

Operat.

Other

Labor

Cler.

Other

Prof

Mgmt

Sales

Cler.

Operat.

Labor

mean of tenure mean of prev_exp

We add the descending option to the

second over() option, and the levels of

collgrad are now shown with college

graduates appearing ﬁrst.

Uses nlsw.dta & scheme vg blue

6.3 Controlling the categorical axis

This section describes ways that you can label the categorical axis in dot plots. Dot plots,

like bar and box plots, are diﬀerent from other plots since their x-axis is formed by categori-

cal variables. (Remember that Stata calls the axis with the categorical variable(s) the x-axis,

even though it may be placed on the left axis.) This section describes options you can use

to customize the categorical axis. For more details on this, see [G]cat axis label options

and [G]cat axis line options. We will start by showing how you can change the labels

used for the categorical axis. These examples use the vg past scheme.

graph dot tenure, over(occ7) over(south)

0 2 4 6 8

mean of tenure

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

This is an example of a dot plot with

two over() variables graphing the

average tenure broken down by

occupation and whether one lives in the

South. The variable south is a dummy

variable that does not have any value

labels, so the x-axis is not labeled very

well.

Uses nlsw.dta & scheme vg past

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.3 Controlling the categorical axis 203

graph dot wage, over(occ7) over(south, relabel(1 "N & W" 2 "South"))

We can use the relabel() option to

change the labels displayed for the

levels of south, giving the x-axis more

meaningful labels. Note that we wrote

relabel(1 "N & W"),notrelabel(0

"N & W"), since these numbers do not

represent the actual levels of south but

the ordinal position of the levels, i.e.,

ﬁrst and second.

Uses nlsw.dta & scheme vg past

0 5 10 15

mean of wage

South

N & W

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

graph dot prev exp tenure ttl exp, over(occ5) ascategory

This graph dot command has multiple

y-variables but uses the ascategory

option to plot the diﬀerent y-variables

as if they were categorical variables.

The dots for the diﬀerent y-variables

are plotted on diﬀerent lines using the

same symbol, and each line is labeled

on the x-axis rather than using a

legend. The default labels on the x-axis

are not bad, but we might want to

change them.

Uses nlsw.dta & scheme vg past 0 5 10 15

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

mean of ttl_exp

mean of tenure

mean of prev_exp

mean of ttl_exp

mean of tenure

mean of prev_exp

mean of ttl_exp

mean of tenure

mean of prev_exp

mean of ttl_exp

mean of tenure

mean of prev_exp

mean of ttl_exp

mean of tenure

mean of prev_exp

graph dot prev exp tenure ttl exp, over(occ5) ascategory

yvaroptions(relabel(1 "Previous" 2 "Current" 3 "Total"))

If we had an over() option, we could

use the relabel() option to change the

labels on the x-axis. But since we have

multiple y-variables that we have

treated as categories, we then use the

yvaroptions(relabel()) option to

modify the labels on the x-axis.

Uses nlsw.dta & scheme vg past

0 5 10 15

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Total

Current

Total

Current

Total

Current

Total

Current

Total

Current

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

204 Chapter 6. Dot plots

graph dot prev exp tenure ttl exp, ascategory

over(south, relabel(1 "N & W" 2 "South"))

yvaroptions(relabel(1 "Previous" 2 "Current" 3 "Total"))

0 5 10 15

South

N & W

Total

Current

Total

Current

In this example, we have multiple

y-variables that are converted into

categorical variables via the

ascategory option, and an over()

variable, as well. The relabel() option

within the over() option changes the

labels for south, and the relabel()

option within yvaroptions() changes

the labels for the multiple y-variables.

Uses nlsw.dta & scheme vg past

graph dot prev exp tenure ttl exp, ascategory xalternate

over(south, relabel(1 "N & W" 2 "South"))

yvaroptions(relabel(1 "Previous" 2 "Current" 3 "Total"))

0 5 10 15

South

N & W

Total

Current

Total

Current

We add the xalternate option, which

moves the labels for the x-axis to the

opposite side, in this case, from the left

to the right. We could also use the

yalternate option to move the y-axis

to its opposite side.

Uses nlsw.dta & scheme vg past

graph dot wage, over(occ7)

l1title("Occupations recoded" "into seven categories")

0 2 4 6 8 10

mean of wage

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Occupations recoded

into seven categories

In this example, the categorical axis

represents the occupation after recoding

it into seven categories. We can use the

l1title() option to add a title to the

left of the graph labeling this axis. Note

that we broke the title into two quoted

strings that appear on the graph as two

lines. We could also add a second title

to the left with l2title();seeStandard

options : Titles (313) for more details.

Uses nlsw.dta & scheme vg past

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.4 Controlling legends 205

6.4 Controlling legends

This section discusses the use of legends for dot plots, emphasizing the features that are

unique to dot plots. The section Options : Legend (287) goes into great detail about legends,

as does [G]legend option. Legends can be used for multiple y-variables or when the ﬁrst

over() variable is treated as a y-variable via the asyvars option. See Dot : Yvars and over

(193) for more information about using multiple y-variables and more examples of treating

the ﬁrst over() variable as a y-variable. These following examples use the vg rose scheme.

graph dot prev exp tenure ttl exp, over(occ7)

Consider this dot plot of three diﬀerent

variables. These variables are shown

with diﬀerent markers, and a legend is

used to identify the variables.

Uses nlsw.dta & scheme vg rose

0 5 10 15

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

mean of ttl_exp

graph dot wage, over(collgrad) over(occ7) asyvars

This is another example of how a

legend can arise in a Stata dot plot if

you use the over() variable with the

asyvars option. Stata treats the levels

of the over() variable as if they were

really multiple y-variables.

Uses nlsw.dta & scheme vg rose

0 5 10 15

mean of wage

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

not college grad college grad

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

206 Chapter 6. Dot plots

Unless otherwise mentioned, the legend options described below work the same whether

the legend was derived from multiple y-variables or from an over() variable that was com-

bined with the asyvars option.

graph dot prev exp tenure ttl exp, over(occ7) nolabel

0 5 10 15

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

prev_exp tenure

ttl_exp

The nolabel option only works when

you have multiple y-variables. When

this option is used, the variable names

(not the variable labels) are used in the

legend. For example, instead of showing

the variable label Prev. work exper.,

this option shows the variable name

prev exp.

Uses nlsw.dta & scheme vg rose

graph dot prev exp tenure ttl exp, over(occ7)

legend(label(1 "Previous") label(2 "Current") label(3 "Total")

title("Work Experience"))

0 5 10 15

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Previous Current

Total

Work Experience

We use the legend(label()) option to

change the labels for the variables in

the legend and the title() option to

add a title to the legend.

Uses nlsw.dta & scheme vg rose

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.5 Controlling the y-axis 207

graph dot prev exp tenure ttl exp, over(occ7)

legend(position(12) rows(1))

We can put the legend at the top of the

graph with the legend(position(12))

option. The values you supply for

position() are similar to the numbers

on a clock face, where 12 o’clock is the

top, 6 o’clock is the bottom, and 0

represents the center of the clock face;

see Styles : Clockpos (330) for more

details. We also add the rows(1)

option to make the legend display as

one row.

Uses nlsw.dta & scheme vg rose 0 5 10 15

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure mean of ttl_exp

graph dot prev exp tenure ttl exp, over(occ7)

legend(cols(1) position(9))

Here, the legend is moved to the left

and displayed in a single column using

the legend(cols(1) position(9))

options.

Uses nlsw.dta & scheme vg rose

0 5 10 15

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp

mean of tenure

mean of ttl_exp

6.5 Controlling the y-axis

This section describes options to customize the y-axis with dot plots. To be precise,

when Stata refers to the y-axis on a dot plot, it refers to the axis with the continuous vari-

able, which is placed on the bottom (where the x-axis would traditionally be placed). This

section emphasizes the features that are particularly relevant to dot plots. For more details,

see Options : Axis titles (254), Options : Axis labels (256), and Options : Axis scales (265). Also,

see [G]axis title options,[G]axis label options, and [G]axis scale options. These

examples use the vg teal scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

208 Chapter 6. Dot plots

graph dot hours, over(occ7) ytitle("Hours Worked" "Per Week")

0 10 20 30 40

Hours Worked

Per Week

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Consider this graph showing the mean

hourly wage broken down by

occupation. We use the ytitle()

option to add a title to the y-axis. We

place the title across two lines by using

two separate, quoted strings. See

Options : Axis titles (254) and

[G]axis title options for more details,

but please disregard any references to

xtitle(), since that option is not valid

when using graph dot.

Uses nlsw.dta & scheme vg teal

graph dot hours, over(occ7)

ytitle("Hours Worked" "Per Week", bfcolor(eggshell) box bexpand)

0 10 20 30 40

Hours Worked

Per Week

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Because the title is considered to be a

textbox, you can use textbox options as

illustrated here to control the look of

the title. See Options : Textboxes (303)

for additional examples of how to use

textbox options to control the display

of text.

Uses nlsw.dta & scheme vg teal

graph dot hours, over(occ7)

yline(35 40, lwidth(thin) lcolor(navy) lpattern(dash))

0 10 20 30 40

mean of hours

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

This example uses the yline() option

to add a thin, navy, dashed line to the

graph where the hours worked equal 35

and 40.

Uses nlsw.dta & scheme vg teal

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.5 Controlling the y-axis 209

graph dot hours, over(occ7) ylabel(30(5)45)

We use the ylabel() option to label

the y-axis from 30 to 45 by increments

of 5. See Options : Axis labels (256) and

[G]axis label options for more

details. Please disregard any references

to xlabel() since that option is not

valid when using graph dot. Note that

the y-axis still begins at 0, but see the

next example for how you can override

this.

Uses nlsw.dta & scheme vg teal 30 35 40 45

mean of hours

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

graph dot hours, over(occ7) ylabel(30(5)45) exclude0

When we add the exclude0 option, the

dot plot does not automatically begin

at 0. In this case, it starts at 30 since

that is the value we speciﬁed as the

starting point on the ylabel() option.

Uses nlsw.dta & scheme vg teal

30 35 40 45

mean of hours

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

graph dot hours, over(occ7) yscale(off)

We can use the yscale(off) option to

turn oﬀ the y-axis. See

Options : Axis scales (265) and

[G]axis scale options for more

details. Please disregard any references

to xscale() since that option is not

valid when using graph dot.

Uses nlsw.dta & scheme vg teal

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

210 Chapter 6. Dot plots

graph dot hours, over(occ7) yalternate

0 10 20 30 40

mean of hours

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

The yalternate option puts the y-axis

on the opposite side, in this case on the

top side of the graph.

Uses nlsw.dta & scheme vg teal

graph dot hours, over(occ7) yreverse

010203040

mean of hours

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

You can reverse the direction of the

y-axis with the yreverse option.

Uses nlsw.dta & scheme vg teal

6.6 Changing the look of dot rulers, dotlook options

This section shows how you can control the look of the lines in your dot plots. We

show how you can control the space between the lines, the color of the lines, and other

characteristics of the line. For more information, see the linelook options table in [G]graph

dot. These graphs are shown using the vg s2c scheme.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.6 Changing the look of dot rulers, dotlook options 211

graph dot prev exp tenure, over(occ7)

Consider this dot plot showing previous

and current work experience broken

down by occupation. Each dot plot has

a series of small dots that forms a line

on which the symbols are plotted.

Uses nlsw.dta & scheme vg s2c

0 2 4 6 8

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

graph dot prev exp tenure, over(occ7)

ndots(50) dots(msymbol(Oh) msize(medium) mcolor(dkgreen))

By default, each line would be

composed of 100 small dots, but here

we use the ndots(50) option to display

50 small dots. Further, using the

dots() option, the small dots are

displayed as medium-sized, dark green,

hollow circles. See Styles : Symbols

(342), Styles : Markersize (340), and

Styles : Colors (328) for more

information.

Uses nlsw.dta & scheme vg s2c 0 2 4 6 8

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

graph dot prev exp tenure, over(occ7) linetype(line)

lines(lwidth(thick) lcolor(erose))

Using the linetype(line) option, the

dots are instead displayed as lines.

Further, we use the lines() option to

make the line width thick and the line

color rose. We could also add the

lpattern() option to control the line

pattern. See Styles : Linewidth (337),

Styles : Colors (328), and

Styles : Linepatterns (336) for more

information.

Uses nlsw.dta & scheme vg s2c 0 2 4 6 8

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

212 Chapter 6. Dot plots

graph dot prev exp tenure, over(occ7) linetype(rectangle)

rwidth(3) rectangles(fcolor(erose) lcolor(maroon))

0 2 4 6 8

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

Here, we change the linetype() to be

a rectangle. The rwidth(3) sets the

rectangle width to be three times its

normal width. In addition, the

rectangle() option is used to

customize it, using the fcolor() (ﬁll

color) and lcolor() (line color)

options to make the rectangle rose on

the inside with a maroon outline.

Uses nlsw.dta & scheme vg s2c

Let’s now look at options that allow us to control the markers and whether the markers

are displayed on the same line.

graph dot prev exp tenure, over(occ7)

marker(1, msymbol(D) mcolor(teal) msize(large))

0 2 4 6 8

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

Here, we use the marker() option to

control the marker used for the ﬁrst

y-variable, making it a large

teal-colored diamond. See

Options : Markers (235) for more details

on how you can control markers.

Uses nlsw.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.6 Changing the look of dot rulers, dotlook options 213

graph dot prev exp tenure, over(occ7)

marker(1, msymbol(d) mfcolor(teal) mlcolor(dkgreen) mlwidth(thick))

marker(2, msymbol(S) mfcolor(ltblue) mlcolor(blue) mlwidth(thick))

In this example, we use two marker()

options, so we can control both

markers. The ﬁrst marker is now a

diamond with a teal ﬁll and a thick,

dark green outline. The second marker

is a square, light blue on the inside with

a thick blue outline. The section

Options : Markers (235) has more details

on controlling markers.

Uses nlsw.dta & scheme vg s2c 0 2 4 6 8

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

graph dot prev exp tenure, over(occ7) linegap(45)

We can use the linegap() option to

display the y-variables on diﬀerent lines

and specify the gap between these lines.

The default value is 0, meaning that all

y-variables are displayed on the same

line.

Uses nlsw.dta & scheme vg s2c

0 2 4 6 8

Other

Labor

Operat.

Cler.

Sales

Mgmt

Prof

mean of prev_exp mean of tenure

graph dot tenure, over(occ5) over(collgrad) over(married)

Consider this graph. Since we have

used three over() options, the levels of

the ﬁrst over() variable are displayed

as though they were diﬀerent

y-variables. We may want to use the

linegap() option to display the

diﬀerent y-variables on diﬀerent lines to

make the graph more readable; see the

next example.

Uses nlsw.dta & scheme vg s2c

0 2 4 6 8 10

mean of tenure

married

single

college grad

not college grad

college grad

not college grad

Prof/Mgmt Sales

Clerical Labor/Ops

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

214 Chapter 6. Dot plots

graph dot tenure, over(occ5) over(collgrad) over(married) linegap(30)

legend(rows(1) span)

0 2 4 6 8 10

mean of tenure

married

single

college grad

not college grad

college grad

not college grad

Prof/Mgmt Sales Clerical Labor/Ops Other

This example is the similar to the

previous one, but we have added the

linegap(30) option to make the levels

of occ5 display on separate lines,

making the results more readable. We

have also added a legend() option to

make the legend display in one line and

span the width of the graph.

Uses nlsw.dta & scheme vg s2c

6.7 Graphing by groups

This section discusses the use of the by() option in combination with graph dot.Nor-

mally, you would use the over() option instead of the by() option, but in some cases, the

by() option is either necessary or more advantageous. For example, a by() option is useful

if you exceed the maximum number of over() options (three if you have a single y-variable

or two if you have multiple y-variables). In such cases, the by() option allows you to break

your data down by additional categorical variables. by() also gives you more ﬂexibility in

the placement of the separate panels. For more information about the by() option, see

Options : By (272), and for more information about the over() option, see Dot : Yvars and

over (193). The examples in this section use the vg s1m scheme.

graph dot wage, over(collgrad) over(occ5) over(urban2)

0 5 10 15

mean of wage

Metro

Rural

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

not college grad college grad

Consider this dot graph breaking wages

down by three categorical variables. If

we wanted to break this down further

by another categorical variable, we

could not use another over() option

since we can have a maximum of three

over() options with a single y-variable.

Uses nlsw.dta & scheme vg s1m

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published form of the book may be distributed or reproduced, either electronically or in printed form.

6.7 Graphing by groups 215

graph dot wage, over(collgrad) over(occ5) over(urban2) by(union)

If we want to break wage down further

by union, we can use the by(union)

option.

Uses nlsw.dta & scheme vg s1m

0 5 10 15 0 5 10 15

Metro

Rural

Metro

Rural

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

nonunion union

not college grad college grad

mean of wage

Graphs by union worker

graph dot tenure ttl exp, over(occ5) over(urban2)

Consider this dot graph with multiple

y-variables breaking them down by two

categorical variables using two over()

options. When you have multiple

y-variables, you can have a maximum

of two over() options.

Uses nlsw.dta & scheme vg s1m

0 5 10 15

Metro

Rural

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

mean of tenure mean of ttl_exp

graph dot tenure ttl exp, over(occ5) over(urban2)

by(union)

If we want to break tenure down

further by another categorical variable,

say union,wecanusetheby(union)

option. Although this example shows

only a single variable in the by()

option, you can specify multiple

variables.

Uses nlsw.dta & scheme vg s1m

0 5 10 15 0 5 10 15

Metro

Rural

Metro

Rural

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

Other

Labor/Ops

Clerical

Sales

Prof/Mgmt

nonunion union

mean of tenure mean of ttl_exp

Graphs by union worker

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Yvars and over Over options Cat axis Legend Y-axis Dotlook options By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

216 Chapter 6. Dot plots

graph dot ttl exp tenure, over(married) over(urban2)

by(union, missing)

0 5 10 15

Metro

Rural

Metro

Rural

Metro

Rural

married

single

married

single

married

single

married

single

married

single

married

single

nonunion union

(missing)

mean of ttl_exp mean of tenure

Graphs by union worker

We can use the missing option to

include a panel for the missing values of

union. Note that we changed the ﬁrst

over() variable to be over(married)

to make an example that was more

readable.

Uses nlsw.dta & scheme vg s1m

graph dot ttl exp tenure, over(married) over(collgrad)

by(union, total)

0 5 10 15

college grad

not college grad

college grad

not college grad

college grad

not college grad

married

single

married

single

married

single

married

single

married

single

married

single

nonunion union

Total

mean of ttl_exp mean of tenure

Graphs by union worker

We can add the total option to include

a panel for all observations.

Uses nlsw.dta & scheme vg s1m

graph dot ttl exp tenure, over(married) over(collgrad)

by(union, total cols(1))

0 5 10 15

college grad

not college grad

college grad

not college grad

college grad

not college grad

married

single

married

single

married

single

married

single

married

single

married

single

nonunion

union

Total

mean of ttl_exp mean of tenure

Graphs by union worker

We can use the cols(1) option to show

the graphs in one column.

Uses nlsw.dta & scheme vg s1m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

7 Pie graphs

This chapter discusses the use of pie charts in Stata. We start by illustrating the diﬀerent

kinds of ways that you can create pie charts in Stata, followed by showing how you can sort

the slices in your pie charts. Next, we show how you can customize the display of individual

slices, as well as control the colors of the pie chart. Then, we demonstrate diﬀerent ways

you can label the pie slices and then how you can control the legends for pie charts. Finally,

we discuss how to use the by() option.

7.1 Types of pie graphs

This section describes diﬀerent ways to produce pie charts using Stata. Stata allows you

to produce pie charts based on multiple y-variables, with each y-variable corresponding to

a slice. You can also create a pie chart based on a single y-variable broken down by a single

over() variable. Finally, you can create a pie chart with no y-variables broken down by

an over() variable, which counts the number of observations by each level of the over()

variable. For more details, see [G]graph pie. This section uses the vg s1c scheme.

graph pie poplt5 pop5 17 pop18 64 pop65p

In this syntax, you supply multiple

y-variables, and each y-variable

corresponds to a slice in the pie. The

ﬁrst y-variable is the population in the

state that is younger than 5 years old,

the next the population 5 to 17 years

old, the next 18 to 64 years old, and the

last 65 years and older. The entire pie

would correspond to the sum of all of

these variables across all states. The

ﬁrst slice then corresponds to the

percentage of the total population that

is younger than 5 years old.

Uses allstates.dta & scheme vg s1c

Pop, < 5 year Pop, 5 to 17 years

Pop, 18 to 64 years Pop, 65 and older

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

217

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

218 Chapter 7. Pie graphs

graph pie pop, over(division)

N. Eng. Mid Atl

E.N.C. W.N.C.

S. Atl. E.S.C.

W.S.C. Mountain

Pacific

In this syntax, you supply a single

y-variable and an over() option. In

this case, the y-variable corresponds to

the population of the state, the entire

pie corresponds to the entire

population, and each slice corresponds

to the percentage of the population for

each level of division.

Uses allstates.dta & scheme vg s1c

graph pie, over(occ7)

Prof Mgmt

Sales Cler.

Operat. Labor

Other

For this third example, we switch to the

nlsw data ﬁle. In this syntax, an

over() option is supplied, but no

y-variable is supplied (in a sense, the

observation itself serves as the

y-variable). This pie chart is much like

a visual frequency distribution of occ7,

where the size of each slice corresponds

to the proportion of women in each

occupation.

Uses nlsw.dta & scheme vg s1c

graph pie, over(union) missing

nonunion union

This example shows the proportion of

women in union and nonunion jobs. We

add the missing option, and another

pie slice is added for the observations in

which union is missing.

Uses nlsw.dta & scheme vg s1c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

7.2 Sorting pie slices 219

7.2 Sorting pie slices

This section describes how you can sort and arrange slices in pie charts. For more de-

tails, see [G]graph pie. This section uses the vg lgndc scheme, which places the legend

at the left in a single column.

graph pie, over(occ7)

Consider this pie chart showing the

number of women who work in these

seven diﬀerent occupations. The slices

are ordered according to the levels of

occ7 from 1 to 7, rotating clockwise,

starting with the ﬁrst slice, which is

positioned at 90 degrees.

Uses nlsw.dta & scheme vg lgndc

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

graph pie, over(occ7) noclockwise

With the noclockwise option, you can

display the slices in counterclockwise

order.

Uses nlsw.dta & scheme vg lgndc

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

220 Chapter 7. Pie graphs

graph pie, over(occ7) angle0(0)

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

With the angle0() option, you can set

the angle of the line that begins the

ﬁrst pie slice. Here, we make the ﬁrst

pie slice begin at 0 degrees.

Uses nlsw.dta & scheme vg lgndc

graph pie, over(occ7) sort

Cler.

Operat.

Mgmt

Labor

Other

Prof

Sales

The sort option sorts the slices

according to their size, from smallest to

largest.

Uses nlsw.dta & scheme vg lgndc

graph pie, over(occ7) sort descending

Sales

Prof

Other

Labor

Mgmt

Operat.

Cler.

Adding the descending option to the

sort option orders the slices from

largest to smallest.

Uses nlsw.dta & scheme vg lgndc

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

7.3 Changing the look of pie slices, colors, and exploding 221

graph pie, over(occ7) sort(occ7alpha)

Say that we wanted to sort the slices

(alphabetically) by occupation name.

We have created a new variable,

occ7alpha, that is a recoded version of

occ7. It is recoded such that, as

occ7alpha goesfrom1to7,the

occupations are alphabetized (except

for Other, which is placed last). We

add sort(occ7alpha), and the slices

are ordered alphabetically.

Uses nlsw.dta & scheme vg lgndc

Cler.

Labor

Mgmt

Operat.

Prof

Sales

Other

7.3 Changing the look of pie slices, colors, and exploding

This section describes how to change the color of pie slices, explode pie slices, control

the overall intensity of colors, and control the characteristics of lines surrounding the pie

slices. For more details, see [G]graph pie. This section uses the vg rose scheme.

graph pie, over(occ7)

Consider this pie chart showing the

number of women who work in these

seven diﬀerent occupations. The slices

are colored using the colors indicated

by the scheme. None of the slices are

exploded, and no lines surround the

slices.

Uses nlsw.dta & scheme vg rose

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

222 Chapter 7. Pie graphs

graph pie, over(occ7) pie(3, explode)

Prof Mgmt

Sales Cler.

Operat. Labor

Other

In this example, we use the pie()

option to explode the third pie slice,

calling attention to this slice. By

default, it is exploded by 3.8 units.

Uses nlsw.dta & scheme vg rose

graph pie, over(occ7) pie(3, explode(5) color(cyan))

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Here, we specify explode(5) to

increase the distance this slice is

exploded to 5 units. We also make the

third slice cyan to make it more

noticeable. See Styles : Colors (328) for

other colors you could choose.

Uses nlsw.dta & scheme vg rose

graph pie, over(occ7) pie(3, color(cyan) explode(5))

pie(1, color(gold) explode(2.5))

Prof Mgmt

Sales Cler.

Operat. Labor

Other

You can use the pie() option

repeatedly. Here, we change the color

and explode slices 1 and 3.

Uses nlsw.dta & scheme vg rose

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

7.3 Changing the look of pie slices, colors, and exploding 223

graph pie, over(occ7) intensity(*1.5)

Using the intensity() option, we

make the colors of all of the slices 1.5

times their normal intensity.

Uses nlsw.dta & scheme vg rose

Prof Mgmt

Sales Cler.

Operat. Labor

Other

graph pie, over(occ7) intensity(*.6)

In this example, we make the intensity

of the colors 60% of the normal color.

Uses nlsw.dta & scheme vg rose

Prof Mgmt

Sales Cler.

Operat. Labor

Other

graph pie, over(occ7) line(lcolor(sienna) lwidth(thick))

The line() option can be used to

change the characteristics of the lines

surrounding the pie slices. Here, we add

the lcolor() (line color) and lwidth()

(line width) options to make the line

sienna and thick.

Uses nlsw.dta & scheme vg rose

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

224 Chapter 7. Pie graphs

7.4 Slice labels

This section describes how you can label the pie slices. For more details, see [G]graph

pie. For this section, we will use the economist scheme.

graph pie, over(occ7) plabel( all sum)

317

264

726

102

246

286

305

Prof Mgmt Sales

Cler. Operat. Labor

Other

Consider this pie chart showing the

number of women who work in these

seven diﬀerent occupations. Here, we

use the plabel() (pie label) option to

label all slices with the sum, in this

case the frequency of women who work

in each occupation. Notice how

readable the labels are because of the

pale colors of the pie slices selected by

the vg past scheme. Other schemes

with more intense colors would have

made these labels hard to read.

Uses nlsw.dta & scheme economist

graph pie, over(occ7) plabel( all percent)

14.11%

11.75%

32.32%

4.541%

10.95%

12.73%

13.58%

Prof Mgmt Sales

Cler. Operat. Labor

Other

Using the percent option, we can show

the percent of women who work in each

occupation.

Uses nlsw.dta & scheme economist

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

7.4 Slice labels 225

graph pie, over(occ7) plabel( all name)

The name option adds a label that is

the name of the occupation.

Uses nlsw.dta & scheme economist

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Prof Mgmt Sales

Cler. Operat. Labor

Other

graph pie, over(occ7) plabel( all name) legend(off)

When the name option is used, the

legend is not as necessary and can be

suppressed using the legend(off)

option.

Uses nlsw.dta & scheme economist

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

graph pie, over(occ7) plabel(1 "Prof=14.11")

plabel(3 "Sales=32.32%")

The plabel() option can also be used

to put any text that you want into all

slices or into individual slices. Here, we

add text to the ﬁrst and third slices.

Uses nlsw.dta & scheme economist Prof=14.11

Sales=32.32%

Prof Mgmt Sales

Cler. Operat. Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

226 Chapter 7. Pie graphs

graph pie, over(occ7) plabel( all percent, format("%2.0f"))

14%

12%

32%

11%

13%

14%

Prof Mgmt Sales

Cler. Operat. Labor

Other

When you use plabel to label slices

with a sum or percent, you can use the

format() option to control the format

of the numeric values displayed. Here,

we display the percentages as whole

numbers.

Uses nlsw.dta & scheme economist

graph pie, over(occ7) plabel( all percent, gap(-5))

14.11%

11.75%

32.32%

4.541%

10.95%

12.73%

13.58%

Prof Mgmt Sales

Cler. Operat. Labor

Other

You can use the gap() option to adjust

the position of the label with respect to

the center of the pie. A positive number

pushes the label away from the center

of the pie, and a negative value pushes

the label closer to the center of the pie.

Uses nlsw.dta & scheme economist

graph pie, over(occ7) plabel( all percent, size(large) color(maroon))

14.11%

11.75%

32.32%

4.541%

10.95%

12.73%

13.58%

Prof Mgmt Sales

Cler. Operat. Labor

Other

You can use textbox options to modify

the display of the text labeling the pie

slices. Here, we increase the size of the

text and change its color to maroon.

See Options : Textboxes (303) for more

options you can use.

Uses nlsw.dta & scheme economist

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

7.4 Slice labels 227

graph pie, over(occ7) plabel( all name, gap(-5))

plabel(1 "32%", gap(5)) legend(off)

We can include multiple plabel()

options. In this example, the ﬁrst

plabel() option assigns the occupation

names to all the slices and moves the

names 5 units inward. The second

plabel() option assigns text to the

second slice and displays it 5 more

units from the center. Since the legend

was not needed, we suppressed it with

the legend(off) option.

Uses nlsw.dta & scheme economist

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

32%

graph pie, over(occ7) plabel( all name, gap(-5))

plabel( all percent, gap(5) format("%2.0f"))

legend(off)

Here, we use the plabel() option twice

to label the slices with the occupation

name and with the percentage. We use

the gap() option to move the names

closer to the center by 5 extra units and

move the percentage 5 extra units from

the center.

Uses nlsw.dta & scheme economist

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

14%

12%

32%

11%

13%

14%

graph pie, over(occ7) ptext(0 30 "This is some text")

The ptext() (pie text) option can be

used to add text to the pie chart. Polar

coordinates are used to determine the

location of the text by specifying the

angle and distance from the center.

Here, the angle is 0, and the distance

from the center is 30.

Uses nlsw.dta & scheme economist This is some text

Prof Mgmt Sales

Cler. Operat. Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

228 Chapter 7. Pie graphs

graph pie, over(occ7) ptext(-10 10 "This is some text")

This is some text

Prof Mgmt Sales

Cler. Operat. Labor

Other

Here, we choose an angle of −10

(putting it 10 degrees below 0) and a

distance of 10 units from the center.

Note that the angle determines only the

position of the text but not its actual

angle of display, which is controlled

with the orientation() option. See

the next example for more details.

Uses nlsw.dta & scheme economist

graph pie, over(occ7)

ptext(-10 10 "This is some text", orientation(rvertical)

placement(s) box margin(medsmall) bfcolor(sand))

This is some text

Prof Mgmt Sales

Cler. Operat. Labor

Other

Here, we choose an angle of −10

degrees and a distance of 10 units. We

also add a number of textbox options to

make the text reverse vertical, meaning

that it is placed to the south of the

given coordinates, within a box that

has with a medium-small margin and is

ﬁlled with a sand color. For more

information on these kinds of textbox

options, see Options : Textboxes (303).

Uses nlsw.dta & scheme economist

7.5 Controlling legends

This section illustrates some of the options that you can use to control the display of

legends with pie charts. While this section illustrates the use of legends, it emphasizes

options that may be particularly useful with pie charts. See Options : Legend (287) for more

details about legends; those details apply well to pie charts, even if the examples use other

kinds of graphs. Also, see [G]legend option for more details. We begin this section using

the vg brite scheme.

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published form of the book may be distributed or reproduced, either electronically or in printed form.

7.5 Controlling legends 229

graph pie, over(occ7)

Consider this pie graph showing the

frequencies of women in these seven

occupational categories.

Uses nlsw.dta & scheme vg brite

Prof Mgmt

Sales Cler.

Operat. Labor

Other

graph pie, over(occ7) legend(label(1 "Professional"))

We can use the legend(label())

option to change the label for the ﬁrst

occupation.

Uses nlsw.dta & scheme vg brite

Professional Mgmt

Sales Cler.

Operat. Labor

Other

graph pie, over(occ7) legend(title(Occupation))

We can add the title() option to the

legend() option to add a title to the

legend. In fact, we can also use

subtitle(),note(),andcaption()

options as well, much as we would for

adding titles to a graph; see Standard

options : Titles (313) for more details.

Uses nlsw.dta & scheme vg brite

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Occupation

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

230 Chapter 7. Pie graphs

graph pie, over(occ7) legend(title(Occupation, position(6)))

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Occupation

We can use the position() option

within the title() option to control

the position of the title. Here, we put

the title in the 6 o’clock position,

placing it at the bottom of the legend.

Uses nlsw.dta & scheme vg brite

graph pie, over(occ7) legend(colfirst)

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

We can use the legend(colfirst)

option to order the items in the legend

by columns instead of rows.

Uses nlsw.dta & scheme vg brite

graph pie, over(occ7) legend(colfirst order(7 651234)holes(1))

Other

Labor

Operat.

Prof

Mgmt

Sales

Cler.

The pie wedges rotate clockwise, and

here we make the items within the

legend rotate in a similar clockwise

fashion, starting from the top right.

The order() option puts the items in

the legend in a clockwise order, and the

holes(1) option leaves the ﬁrst

position empty.

Uses nlsw.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

7.5 Controlling legends 231

graph pie, over(occ7) legend(position(12) rows(2))

We can use the position() option to

control the position of the legend,

indicating its position like the numbers

on a clock face; see Styles : Clockpos

(330). Here, we put the legend at the

12 o’clock position, placing it at the top

of the chart, and also add the rows(2)

option to make the legend display in

two rows.

Uses nlsw.dta & scheme vg brite

Prof Mgmt Sales Cler.

Operat. Labor Other

graph pie, over(occ7) legend(position(9) cols(1) stack)

Here, we use the same options as those

in the last example but use them to

place the legend to the left of the graph

(in the 9 o’clock position) and make the

legend display in a single column. We

also add the stack option to the

previous example to stack the symbol

and descriptive text above each other.

This makes an even narrower column,

leaving more room for the pie chart.

Uses nlsw.dta & scheme vg brite

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

graph pie, over(occ7)

Here, we use the vg lgndc scheme.

Using this scheme places the legend to

the left in a single column with the

symbol stacked above the description.

Uses nlsw.dta & scheme vg lgndc

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

232 Chapter 7. Pie graphs

7.6 Graphing by groups

This section describes the use of the by() option with pie charts, focusing on features

that are speciﬁcally relevant to pie charts. For more details, see Options : By (272) and

[G]by option.

graph pie, over(occ7)

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Here, we see a basic pie chart showing

the distribution of occupations.

Uses nlsw.dta & scheme vg s2c

graph pie, over(occ7) by(union)

nonunion union

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Graphs by union worker

In this graph, the occupations are

broken down by whether one belongs to

a union.

Uses nlsw.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

7.6 Graphing by groups 233

graph pie, over(occ7) by(union) pie(2, explode)

If we add the pie(2, explode) option,

the second slice is exploded in both

graphs.

Uses nlsw.dta & scheme vg s2c

nonunion union

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Graphs by union worker

graph pie, over(occ7) by(union) sort

Here, we sort the slices from least

frequent to most frequent. Note that

separate legends are shown for each

chart. This is because the slices can be

ordered diﬀerently in the two diﬀerent

graphs when sorted. Thus, when you

use the sort option for pie charts,

Stata shows two separate legends to

assure proper labeling of the slices.

Uses nlsw.dta & scheme vg s2c

Cler. Operat.

Other Labor

Mgmt Prof

Sales

Cler. Mgmt

Labor Prof

Operat. Other

Sales

nonunion union

Graphs by union worker

graph pie, over(occ7) by(union, legend(off))

plabel( all name)

Here, we add the plabel() option to

label the inside of each slice with the

name of the slice, so the legend is no

longer needed. We suppress the legend

with the legend(off) option, which is

placed within the by() option because

it, in a way, determines the placement

of the legend by turning it oﬀ.

Uses nlsw.dta & scheme vg s2c

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

nonunion union

Graphs by union worker

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Types of pie graphs Sorting Colors and exploding Labels Legend By

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

234 Chapter 7. Pie graphs

graph pie, over(occ7) by(union, legend(pos(3)))legend(cols(1) stack)

nonunion union

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

Graphs by union worker

Here, we place the legend to the right

using the legend(pos(3)) option.

Note that this option is contained

within the by() option because it alters

the position of the legend. We also

make the legend a single column with

the legend symbols and labels stacked

with the legend(cols(1) stack)

option. Note this option is outside of

the by() option since it does not

determine the position of the legend.

Uses nlsw.dta & scheme vg s2c

graph pie, over(occ7) by(union) legend(pos(3) cols(1) stack) sort

Cler.

Operat.

Other

Labor

Mgmt

Prof

Sales

Cler.

Mgmt

Labor

Prof

Operat.

Other

Sales

nonunion union

Graphs by union worker

This example is similar to the previous

example, but we have added the sort

option. Note that, when we add the

sort option, we need to move the

pos() option from within the by()

option to outside of the by() option.

This is an exception to the general rule

that legend options that control the

position of the legend are placed within

the by() option. Here, we get the

legends that we desire, each to the right

of the pie.

Uses nlsw.dta & scheme vg s2c

graph pie, over(occ7) by(urban3, legend(at(4)))

Rural Suburb

Urban

Prof Mgmt

Sales Cler.

Operat. Labor

Other

Graphs by Rural vs. Suburb vs. Urban

Here, we break down occupation by a

three-level variable, leaving a fourth

position open. We can specify the

legend(at(4)) option within the by()

option to place the legend in the space

in the fourth position, conserving space

on the graph.

Uses nlsw.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8 Options available for most graphs

This chapter discusses options that are used in many, but not all, kinds of graphs in

Stata, as compared with the Standard options (313) chapter, which covers options that are

standard in all Stata graphs. This chapter goes into greater detail about how to use these

options to customize your graphs. As you can see from the Visual Table of Contents at

the right, this chapter covers markers, connecting, axis titles, labels, scales, selection, using

the by() option, legends, added text, and textboxes. For further details, the examples will

frequently refer to sections of Styles (327) and to [G]graph.

8.1 Changing the look of markers

This section looks at options that we can use for controlling markers. While the ex-

amples in this section focus on twoway scatter, these options apply to any graph where

you have markers and can control them. This section will show how to change the marker

symbol, marker size, and color (both ﬁll and outline color). For more information, see

[G]marker options. We will start this section using the vg s2c scheme.

twoway scatter ownhome borninstate

Consider this scatterplot showing the

relationship between the percentage of

people in a state who own their home

and the percentage of people born in

their state of residence. The markers

used in this plot are ﬁlled circles.

Uses allstates.dta & scheme vg s2c

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

235

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

236 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, msymbol(S)

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

We can control the shape of the marker

with the msymbol() (marker symbol)

option. Here, we make the symbols

large squares.

Uses allstates.dta & scheme vg s2c

twoway scatter ownhome borninstate, msymbol(s)

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

Specifying msymbol(s), which uses a

lowercase s, displays smaller squares.

Uses allstates.dta & scheme vg s2c

twoway scatter ownhome borninstate, msymbol(sh)

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

We can append an h(i.e.,

msymbol(sh)) to yield hollow squares.

In addition to choosing Sfor larger

squares and sfor small squares, we can

specify D(large diamond), T(large

triangle), and O(large circles). We can

specify lowercase letters to get smaller

versions of these symbols and append

the hfor hollow versions.

Uses allstates.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.1 Changing the look of markers 237

twoway scatter ownhome borninstate, msymbol(X)

We can also specify msymbol(X) to use

a large X shape for the markers. We

could also use a lowercase xfor smaller

markers. We cannot append an hsince

we cannot make a hollow X.

Uses allstates.dta & scheme vg s2c

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

twoway scatter ownhome borninstate, msymbol(+)

Specifying msymbol(+) yields a plus

sign shape for the markers. As with the

X, we cannot make these hollow, nor is

there a symbol for a smaller version of

plus signs.

Uses allstates.dta & scheme vg s2c

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

twoway scatter heatdd cooldd, msymbol(p)

Here, we switch to the citytemp data

ﬁle to illustrate the use of the

msymbol(p) option to plot very small

points. Although each point is hard to

see because they are so small, we can

see the overall pattern of the data

because of the large number of points

and the strong trend in the data. See

Styles : Symbols (342) for more

information about symbols.

Uses citytemp.dta & scheme vg s2c

0 2000 4000 6000 8000 10000

Heating degree days

0 1000 2000 3000 4000

Cooling degree days

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

238 Chapter 8. Options available for most graphs

twoway scatter ownhome propval100 borninstate

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

Aside from aesthetics, choosing

diﬀerent marker symbols is useful to

diﬀerentiate multiple markers displayed

in the same plot. In this example, we

plot two y-variables, and Stata displays

both as solid circles, diﬀering in color.

Uses allstates.dta & scheme vg s2c

twoway scatter ownhome propval100 borninstate,

msymbol(t Oh)

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

To further diﬀerentiate the symbols, we

add the msymbol(t Oh) option to

control both markers. Here, we make

the ﬁrst marker a small triangle and the

second a larger hollow circle.

Uses allstates.dta & scheme vg s2c

twoway scatter ownhome propval100 borninstate,

msymbol(. Oh)

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

Using the msymbol(. Oh) option, we

can leave the ﬁrst symbol unchanged

(as indicated by the dot) and change

the second symbol to a hollow circle.

We might think that the dot indicates a

small point, but that is indicated by the

poption.

Uses allstates.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.1 Changing the look of markers 239

twoway scatter ownhome borninstate, mlabel(stateab) mlabpos(center)

One last marker symbol is ifor

invisible, allowing us to hide the marker

symbol. In this example, we use the

mlabel(stateab) (marker label)

option to display a marker label with

the state abbreviation for each

observation and the mlabpos(center)

(marker label position) option to center

the marker label. However, the marker

symbol (the circle) and the marker

label (the abbreviation) are right on

top of each other.

Uses allstates.dta & scheme vg s2c

FL GA

IN IA

KS KYLA

MT NE

NC ND

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

twoway scatter ownhome borninstate, mlabel(stateab) mlabpos(center)

msymbol(i)

If we use msymbol(i) to make the

marker symbol invisible, the marker

label (the state abbreviation) can be

displayed without being obscured by

the marker symbol. See Styles : Symbols

(342) for more information about

symbols.

Uses allstates.dta & scheme vg s2c

FL GA

IN IA

KS KYLA

MT NE

NC ND

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

So far, we have seen that the msymbol() option can be used to control the marker sym-

bol and, to a certain extent, can be used to control the marker size (e.g., using Oyields

large circles, and using oyields small circles). As the following examples show, the msize()

option can be used to exert more ﬂexible control over the size of the markers. The following

examples will use the vg s1m scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

240 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, msymbol(+) msize(small)

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

Previously, we saw that the size of the

symbols created using O,D,S,andT

could be modiﬁed using an uppercase

or lowercase letter. Here, we use the

msize() (marker size) option to control

the size of the marker symbol, making

the marker symbol small. Other values

we could have chosen include vtiny,

tiny,vsmall,small,medsmall,

medium,medlarge,large,vlarge,

huge,vhuge,andehuge.

Uses allstates.dta & scheme vg s1m

twoway scatter ownhome borninstate, msymbol(Oh) msize(*2)

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

We can specify the sizes as multiples of

the original size of the marker. In this

example, we make the markers twice

their original size by specifying

msize(*2). Specifying a value less than

one reduces the marker size; e.g.,

msize(*.5), would make the marker

half its normal size. See

Styles : Markersize (340) for more details.

Uses allstates.dta & scheme vg s1m

twoway scatter ownhome borninstate [aweight=propval100],

msymbol(oh)

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

Stata even allows us to size the symbols

based on the values of another variable

in your data ﬁle. This allows us, in a

sense, to graph three variables at once.

Here, we look at the relationship

between borninstate and ownhome and

then size the markers based on

propval100 using

[aweight=propval100], weighting the

markers by propval100.

Uses allstates.dta & scheme vg s1m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.1 Changing the look of markers 241

twoway scatter ownhome borninstate [aweight=propval100],

msymbol(oh) msize(small)

Even if we weight the size of the

markers using aweight, we can still

control the general size of the markers.

Here, we make all markers smaller

using the msize(small) option. The

markers are smaller than they were

previously but are still sized according

to the value of propval100.

Uses allstates.dta & scheme vg s1m

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

twoway scatter ownhome borninstate [aweight=propval100],

msymbol(oh) msize(large) mlabel(stateab)

We might try to even graph a fourth

variable in the plot by using the

mlabel() (marker label) option. Here,

we try to use the mlabel(stateab)

option to label each marker with the

abbreviation of the state. However,

note that when we add the mlabel()

option, the weights no longer aﬀect the

size of the markers. See the following

example for a solution to this.

Uses allstates.dta & scheme vg s1m

FL GA

IN IA

KS KYLA

MT NE

NC ND

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

twoway (scatter ownhome borninstate [aweight=propval100],

msymbol(oh) msize(large))

(scatter ownhome borninstate, mlabel(stateab) msymbol(i) mlabpos(center))

We can solve the problem from the

previous example by overlaying a

scatterplot that has the symbols

weighted by propval100 with a

scatterplot that shows just the marker

labels. The second scatterplot uses the

mlabel(stateab) msymbol(i)

mlabpos(center) options to label the

markers with the state abbreviation.

See Options : Marker labels (247) for

more details.

Uses allstates.dta & scheme vg s1m

FL GA

ID IL

IN IA

KS KYLA

MT NE

NC ND

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

% who own home % who own home

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

242 Chapter 8. Options available for most graphs

Stata also allows us to control the color of the markers. We can control the overall color

of the marker, create a solid color, or make the inner part of the marker one color (called a

ﬁll color) and the outline of the marker a diﬀerent color. We can also vary the thickness of

the outline of the marker. These next examples will use the vg rose scheme.

twoway scatter ownhome borninstate, mcolor(navy)

% who own home

20 40 60 80

% born in state of residence

The mcolor() (marker color) option

can be used to control the color of the

markers. Here, we make the markers

navy blue using the mcolor(navy)

option. See Styles : Colors (328) for more

information about specifying colors

Uses allstates.dta & scheme vg rose

twoway scatter ownhome borninstate, mfcolor(ltblue) mlcolor(navy)

% who own home

20 40 60 80

% born in state of residence

We can separately control the ﬁll color

(inside color) and outline color with the

mfcolor() (marker ﬁll color) and

mlcolor() (marker line color) options,

respectively. Here, we make the ﬁll

color light blue by specifying

mfcolor(ltblue) and the line color

navy by specifying mlcolor(navy).

Uses allstates.dta & scheme vg rose

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.1 Changing the look of markers 243

twoway scatter ownhome borninstate, mlcolor(black)

We can change the line color

surrounding the marker with the

mlcolor() option. Here, we specify

mlcolor(black) to make the line

surrounding the markers black.

Uses allstates.dta & scheme vg rose

% who own home

20 40 60 80

% born in state of residence

twoway scatter ownhome borninstate, mfcolor(ltblue)

We can also separately control the ﬁll

color using the mfcolor() option. If we

choose mfcolor(ltblue), the ﬁll color

is light blue.

Uses allstates.dta & scheme vg rose

% who own home

20 40 60 80

% born in state of residence

twoway scatter ownhome borninstate, mfcolor(eltgreen)

mlcolor(dkgreen) mlwidth(vthick)

We can control the width of the line

that surrounds the marker using the

mlwidth() option. Here, we make the

width very thick by specifying the

mlwidth(vthick) (marker line width)

option. We can also indicate the

thickness as a multiple of the original

thickness; e.g., mlwidth(*3) indicates

the line should be three times as thick

as it would normally be. See

Styles : Linewidth (337) for more details.

Uses allstates.dta & scheme vg rose

% who own home

20 40 60 80

% born in state of residence

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

244 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, mlwidth(medthick)

% who own home

20 40 60 80

% born in state of residence

If we do not specify a diﬀerent color for

the line that outlines the marker (e.g.,

via the mlcolor() option), we may not

see any eﬀect in specifying the

mlwidth() option. This is because the

color of the line surrounding the marker

is the same as the ﬁll color, so we

cannot see the eﬀect of modifying the

width of the line surrounding the

marker, as illustrated here.

Uses allstates.dta & scheme vg rose

So far, we have focused on controlling the individual elements of markers, the marker

symbol, color, size, ﬁll color, line color, and so forth. There is another way to change the

appearance of a marker, and that is by specifying a marker style. The marker style controls

all these attributes at once, and in some situations, it can be more eﬃcient to use a marker

style to control the elements individually, as we will see in the following examples. The next

examples will use the vg s2m scheme.

twoway scatter ownhome borninstate

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

The marker styles are named/numbered

p1 to p15. The markers in this example

are displayed using the p1 style because

we are plotting only one y-variable and

have not speciﬁed a marker style.

Uses allstates.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.1 Changing the look of markers 245

twoway scatter ownhome borninstate, mstyle(p1)

Here, we explicitly select the default

marker style using the mstyle(p1)

(marker style) option, and the markers

look identical to the previous graph.

Uses allstates.dta & scheme vg s2m

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

twoway scatter ownhome borninstate, mstyle(p2)

Here, we use mstyle(p2) to explicitly

select the p2 style for displaying the

markers, and now the markers are

diﬀerent in size, shape, and color. The

markers are now larger diamonds that

are a middle-level gray color.

Uses allstates.dta & scheme vg s2m

40 50 60 70 80

% who own home

20 40 60 80

% born in state of residence

twoway scatter ownhome propval100 borninstate

Now, if we plot two variables, notice

how the ﬁrst variable is plotted using

the p1 style and the second variable is

plotted using the p2 style. We would

have gotten the same result if we had

speciﬁed the option mstyle(p1 p2).

Uses allstates.dta & scheme vg s2m

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

246 Chapter 8. Options available for most graphs

twoway scatter ownhome propval100 borninstate, mstyle(p1 p10)

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

In this graph, we use the mstyle(p1

p10) option to request that the ﬁrst

variable be plotted with the p1 style

and the second be plotted with the p10

style. A style is just a starting point,

and we can use additional options to

modify the markers to suit our taste.

Uses allstates.dta & scheme vg s2m

twoway scatter ownhome propval100 borninstate, mstyle(p1 p10)

msize(. medium)

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

Say that in the previous graph you

wanted medium-sized triangles. We can

add the msize(. medium) option to

control the size of the second marker,

leaving the ﬁrst unchanged. So, even

though a style chooses a number of

characteristics for the markers, we can

override them.

Uses allstates.dta & scheme vg s2m

twoway scatter ownhome propval100 borninstate, mstyle(p1 p1)

mfcolor(. white)

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

In this example, we use the p1 style for

both the ﬁrst and second markers,

which are small, dark gray, ﬁlled circles.

If no other options are speciﬁed, the

markers for the ﬁrst variable will be

identical to those for the second. But

adding the mfcolor(. white) option,

the ﬁll color for the ﬁrst variable was

left alone, and the second was changed

to white. This easily gave us solid and

white-ﬁlled circles for the two markers.

Uses allstates.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.2 Creating and controlling marker labels 247

twoway scatter ownhome propval100 borninstate, msymbol(. Sh)

Another strategy for controlling the

marker symbols is choosing or creating

a scheme. The vg samem scheme makes

all markers the same size, shape, color,

etc., allowing you to customize them all

from a common base. Here, we use the

vg samem scheme, making all markers

solid, dark gray circles, but use the

msymbol(. Sh) option to make the

second symbol hollow squares.

Uses allstates.dta & scheme vg samem

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

twoway scatter ownhome propval100 borninstate

Say that you wanted the markers to be

displayed as outlines ﬁlled with white.

Rather than specifying the mfcolor()

option, you could use the vg outm

scheme, as shown here. Even if you

overlaid multiple commands, using this

scheme would display the markers, by

default, as white-ﬁlled outlines.

Uses allstates.dta & scheme vg outm

0 20 40 60 80 100

20 40 60 80

% born in state of residence

% who own home % homes cost $100K+

8.2 Creating and controlling marker labels

This section looks at the details of using marker labels. Marker labels can be used to

identify the markers with graph twoway but also can be used with other types of graphs,

such as graph matrix and graph box, aﬀecting the outside values. You can even use

marker labels in lieu of markers. For more information, see [G]marker label options.

For this section, we will use the vg s2c scheme and the allstates3 ﬁle, which keeps the

states that are in the South, i.e., if region is equal to 3.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

248 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, mlabel(stateab)

OK SC

65 70 75

% who own home

30 40 50 60 70 80

% born in state of residence

Consider this scatterplot showing the

relationship between the percentage of

people in a state who own their home

and the percentage of people born in

their state of residence. We might want

to be able to identify some of the

observations, and we can use the

mlabel() (marker label) option to label

the markers with the two-letter

abbreviation of the state.

Uses allstates3.dta & scheme vg s2c

twoway scatter ownhome borninstate, mlabel(stateab) mlabpos(12)

OK SC

65 70 75

% who own home

30 40 50 60 70 80

% born in state of residence

In the previous graph, the marker labels

were all at the 3 o’clock position with

respect to the markers. We can use the

mlabpos() (marker label position)

option to give the marker labels a

diﬀerent position. In this example, we

place the marker labels in the 12

o’clock position above the markers.

Uses allstates3.dta & scheme vg s2c

twoway scatter ownhome borninstate, mlabel(stateab) mlabvpos(pos)

OK SC

65 70 75

% who own home

30 40 50 60 70 80

% born in state of residence

There are a few markers whose

corresponding marker labels overlap

each other. The mlabvpos() (marker

label variable position) option allows us

to assign a diﬀerent marker label

position for each observation via a

variable in the data ﬁle. The variable

pos has a value of 3, except for states

AL,MS,AR,andLA, where pos is 9, 12,

12, and 6, respectively. Note how the

markers are in the 3 o’clock position,

except for AL,MS,AR,andLA,which

are in the 9, 12, 12, and 6 o’clock

positions, respectively.

Uses allstates3.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.2 Creating and controlling marker labels 249

twoway scatter ownhome borninstate, mlabel(stateab) mlabvpos(pos)

mlabsize(small)

We can use the mlabsize() (marker

label size) option to control the size of

the markers. In this example, we make

the markers small. Some of the sizes

you could choose include small,

medsmall,medium,medlarge,large,

and vlarge;seeStyles : Textsize (344)

for more options.

Uses allstates3.dta & scheme vg s2c

65 70 75

% who own home

30 40 50 60 70 80

% born in state of residence

twoway scatter ownhome borninstate, mlabel(stateab) mlabvpos(pos)

mlabsize(*.6)

We can also specify the mlabsize() as

a relative size, a multiple of the original

size. In this example, the labels are .6

times their normal size.

Uses allstates3.dta & scheme vg s2c

OK SC

65 70 75

% who own home

30 40 50 60 70 80

% born in state of residence

twoway scatter ownhome borninstate, mlabel(stateab) mlabangle(45)

The mlabangle() (marker label angle)

option can be used to control the angle

of the marker label. 0 degrees indicates

horizontal text, 90 degrees vertical text,

180 degrees reverse horizontal text, and

270 degrees reverse vertical text. You

can also specify negative degrees (for

example, −90 degrees is the same as

270 degrees). See Styles : Angles (327)

for more details.

Uses allstates3.dta & scheme vg s2c

GA KY

VA WV

65 70 75

% who own home

30 40 50 60 70 80

% born in state of residence

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

250 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, mlabel(stateab) mlabpos(7)

mlabcolor(red)

OK SC

65 70 75

% who own home

30 40 50 60 70 80

% born in state of residence

The mlabcolor() (marker label color)

option controls the color of the marker

labels. In this example, we make the

marker labels red. See Styles : Colors

(328) for more details.

Uses allstates3.dta & scheme vg s2c

twoway scatter ownhome borninstate, mlabel(stateab) mlabpos(7)

mlabgap(*3)

OK SC

65 70 75

% who own home

30 40 50 60 70 80

% born in state of residence

The mlabgap() (marker label gap)

option controls the gap between the

marker and the marker label. In this

example, we make the gap three times

the size that it would normally. You

can also specify a value less than 1 to

place the marker label closer to the

marker.

Uses allstates3.dta & scheme vg s2c

8.3 Connecting points and markers

Stata supports a variety of methods for connecting points using diﬀerent values for the

connectstyle. These include lto connect with a straight line, Lto connect with a straight

line only if the current x-value is greater than the prior x-value, Jfor stairstep, stepstair

for step then stair, and ifor invisible connections. For the next few examples, let’s switch

to using the spjanfeb2001 data ﬁle, keeping just the data for January and February of

2001. These examples of connect styles do not demonstrate how you would normally use

these styles but illustrate the diﬀerent ways you can connect points. See [G]connectstyle

for more information. For this section, we will use the vg blue scheme.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.3 Connecting points and markers 251

twoway scatter close tradeday

Consider this graph, which shows the

closing price of the S&P 500 index for

January and February of 2001 by

tradeday, the trading day numbered

from 1 to 40.

Uses spjanfeb2001.dta & scheme

vg blue

1250

1300

1350

1400

Closing price

0 10 20 30 40

Trading day number

twoway scatter close tradeday, connect(l)

We use connect(l) to connect the

points, but this does not lead to the

kind of graph we really wanted to

create. This is because the observations

in the data ﬁle are not sorted according

to tradeday, yet the observations are

connected based on the order in which

they appear in the data ﬁle.

Uses spjanfeb2001.dta & scheme

vg blue 1250

1300

1350

1400

Closing price

0 10 20 30 40

Trading day number

twoway scatter close tradeday, connect(l) sort

If we add the sort option, the

observations are connected after sorting

them by tradeday, which leads to the

kind of graph we wanted to create.

Alternatively, we could have typed

sort tradeday, and all ensuing graphs

would have been ordered on tradeday,

even without the sort option.

Uses spjanfeb2001.dta & scheme

vg blue 1250

1300

1350

1400

Closing price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

252 Chapter 8. Options available for most graphs

twoway scatter close tradeday, connect(J) sort

1250

1300

1350

1400

Closing price

0 10 20 30 40

Trading day number

You would not normally connect

observations for this kind of graph

using a stairstep pattern. This

connection method, obtained by using

the connect(J) option, would more

normally be used in a graph showing a

survival function over time.

Uses spjanfeb2001.dta & scheme

vg blue

twoway scatter close tradeday, connect(stepstair) sort

1250

1300

1350

1400

Closing price

0 10 20 30 40

Trading day number

A connection method related to the one

above can be obtained using the

connect(stepstair) option.

Uses spjanfeb2001.dta & scheme

vg blue

twoway scatter close dom, connect(l) sort

1250

1300

1350

1400

Closing price

0 10 20 30

Day of month

Say that we wanted to show the closing

price as a function of the day of the

month for the two months for which we

have data. In this example, we have the

variable dom (day of the month) on the

x-axis. If we include the sort option,

the data are shown as one continuous

line, as opposed to having one line for

January and a second line for February.

Uses spjanfeb2001.dta & scheme

vg blue

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.3 Connecting points and markers 253

twoway scatter close dom, connect(l) sort(tradeday)

We need to sort the observations by

tradeday, using the sort(tradeday)

option. This graph is almost what we

want, but the observation for January

31 is connected to the observation for

February 1.

Uses spjanfeb2001.dta & scheme

vg blue

1250

1300

1350

1400

Closing price

0 10 20 30

Day of month

twoway scatter close dom, connect(L) sort(tradeday)

This graph is what we wanted to create.

The connect(L) option avoids the line

connecting January 31 and February 1

because it connects points only as long

as dom is increasing. When dom

decreases from 31 to 1, the connect(L)

option does not connect those two

points. See Styles : Connect (332) for

more details on connect() options.

Uses spjanfeb2001.dta & scheme

vg blue

1250

1300

1350

1400

Closing price

0 10 20 30

Day of month

twoway scatter close tradeday, connect(l) sort

clcolor(green) clwidth(thick) clpattern(dash)

The connect() option determines how

the markers are connected but not the

color, width, or pattern of the line.

Here, we use the clcolor() (connect

line color), clwidth() (connect line

width), and clpattern() (connect line

pattern) options to make the line green,

thick, and dashed. See Styles : Colors

(328), Styles : Linewidth (337), and

Styles : Linepatterns (336) for more

information.

Uses spjanfeb2001.dta & scheme

vg blue

1250

1300

1350

1400

Closing price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

254 Chapter 8. Options available for most graphs

8.4 Setting and controlling axis titles

This section provides more details about the use of axis title options for providing titles

for axes. For more information, see [G]axis title options. For this section, we will use

the vg past scheme.

twoway scatter ownhome propval100

55 60 65 70 75 80

% who own home

0 20 40 60 80 100

% homes cost $100K+

Consider this graph of the percentage

of home owners by the percentage of

homes that cost over one hundred

thousand dollars. The titles of the x-

and y-axes are the names of the

variables, unless the variables are

labeled, in which case the default title

is the variable label. In this example,

the axes are labeled with the variable

labels.

Uses allstatesdc.dta & scheme vg past

twoway scatter ownhome propval100,

ytitle("Percent of households that own their homes")

xtitle("Percent of homes that cost over $100,000")

55 60 65 70 75 80

Percent of households that own their homes

0 20 40 60 80 100

Percent of homes that cost over $100,000

We can use the xtitle() and ytitle()

options to supply our own titles.

Uses allstatesdc.dta & scheme vg past

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.4 Setting and controlling axis titles 255

twoway scatter ownhome propval100,

ytitle("Percent of households that own their homes")

xtitle("Percent of homes that cost over $100,000", size(small) box)

Because an axis title is considered a

textbox, you can use textbox options,

as illustrated here, to control the look

of the axis title. Here, we add the

size() and box options to xtitle() to

make the x-axis title small with a box

around it. See Options : Textboxes (303)

for additional examples of how to use

textbox options to control the display

of text.

Uses allstatesdc.dta & scheme vg past

55 60 65 70 75 80

Percent of households that own their homes

0 20 40 60 80 100

Percent of homes that cost over $100,000

twoway scatter ownhome propval100,

ytitle("Percent of households" "that own their homes")

xtitle("Percent of homes" "that cost over $100,000")

In this example, we supply the same

titles but divide them into two separate

quoted strings, which then are

displayed on separate lines.

Uses allstatesdc.dta & scheme vg past

55 60 65 70 75 80

Percent of households

that own their homes

0 20 40 60 80 100

Percent of homes

that cost over $100,000

twoway scatter ownhome propval100,

ytitle("1990 Census Data", suffix)

xtitle("In 1990 dollars", prefix)

We can use the prefix and suffix

options to add information before or

after the existing title, respectively.

Uses allstatesdc.dta & scheme vg past

55 60 65 70 75 80

% who own home

1990 Census Data

0 20 40 60 80 100

In 1990 dollars

% homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

256 Chapter 8. Options available for most graphs

twoway (scatter rent700 ownhome )

(scatter propval100 ownhome, yaxis(2))

0 20 40 60 80 100

% homes cost $100K+

0 10 20 30 40

% rents $700+/mo

55 60 65 70 75 80

% who own home...

% rents $700+/mo % homes cost $100K+

Consider this overlaid twoway graph.

The two y-variables are both scaled in

percentages, but they have diﬀerent

ranges. We use the yaxis(2) option on

the second scatter command to place

that axis on the second y-axis, which is

then placed on the right axis.

Uses allstatesdc.dta & scheme vg past

twoway (scatter rent700 ownhome) (scatter propval100 ownhome, yaxis(2)),

ytitle("Percent rents over $700", axis(1))

ytitle("Percent homes over $100,000", axis(2))

0 20 40 60 80 100

Percent homes over $100,000

0 10 20 30 40

Percent rents over $700

55 60 65 70 75 80

% who own home...

% rents $700+/mo % homes cost $100K+

Now that we have two y-axes, the

ytitle() option would change the

y-title for the ﬁrst y-axis, unless we

specify otherwise. In this example, we

supply a ytitle() option with the

axis(1) option to indicate that the

title belongs to the ﬁrst y-axis, and a

second ytitle() option using the

axis(2) option to indicate that the

second title belongs to the second

y-axis.

Uses allstatesdc.dta & scheme vg past

8.5 Setting and controlling axis labels

This section describes more details about axis labels, including major and minor (nu-

meric) labels, major and minor tick marks, and grid lines. This section also shows how

to control the appearance of these objects (e.g., size, color, thickness, or angle). For more

information, see [G]axis label options. For this section, we will use the vg s1c scheme.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.5 Setting and controlling axis labels 257

twoway scatter propval100 faminc

Let’s start with a basic graph showing

the percent of homes costing over

$100,000 by the median family income.

Uses allstatesdc.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

twoway scatter propval100 faminc, xlabel(#10) ylabel(#10)

Using the xlabel(#10) and

ylabel(#10) options, we ask for about

10 values to be labeled on each axis.

Stata chose to use 10 values for the

y-axis, labeling it from 0 to 90,

incrementing by 10, and 8 values for

the x-axis going from 14,000 to 28,000,

incrementing by 2,000. As you can see

from this example, sometimes Stata

follows your suggestion exactly, and

sometimes it chooses a diﬀerent number

of values to make more logical labels.

Uses allstatesdc.dta & scheme vg s1c

0 10 20 30 40 50 60 70 80 90

% homes cost $100K+

14000 16000 18000 20000 22000 24000 26000 28000

1979 Median Family Inc.

twoway scatter propval100 faminc, ylabel(0(10)100)

We can change the major labels for the

y-variable to range from 0 to 100,

incrementing by 10, using the

ylabel(0(10)100) option.

Uses allstatesdc.dta & scheme vg s1c

0 10 20 30 40 50 60 70 80 90 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

258 Chapter 8. Options available for most graphs

twoway scatter propval100 faminc, xlabel(minmax) ylabel(none)

% homes cost $100K+

14591 28395

1979 Median Family Inc.

Here, we use the xlabel(minmax)

option to label the x-axis only with the

minimum and maximum and use

ylabel(none),sothatthey-axis will

have no major labels or ticks.

Uses allstatesdc.dta & scheme vg s1c

twoway scatter propval100 faminc, ymlabel(10(20)90)

10 30 50 70 90

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

The default graph had major labels for

the y-axis at 0, 20, 40, 60, 80, and 100.

We could add minor labels for the

y-variable at 10, 30, 50, 70, and 90

using the ymlabel(10(20)90) option.

The min ymlabel() stands for minor.

Uses allstatesdc.dta & scheme vg s1c

twoway scatter propval100 faminc, ytick(10(10)90)

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

The default graph had major ticks for

the y-axis at 0, 20, 40, 60, 80, and 100.

We can add major ticks ranging from

10 to 90, incrementing by 10, using the

ytick(10(10)90) option.

Uses allstatesdc.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.5 Setting and controlling axis labels 259

twoway scatter propval100 faminc, ymtick(10(20)90)

We can use the ymtick() option to add

minor ticks to the graph. For example,

here we add minor ticks at 10, 30, 50,

70, and 90. The min ymtick() stands

for minor.

Uses allstatesdc.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

twoway scatter propval100 faminc, ymtick(##10)

The default graph had major labels for

the y-axis at 0, 20, 40, 60, 80, and 100.

We can place 9 minor ticks between

major ticks with the ymtick(##10)

option. Note that the value of 10

includes the 9 minor ticks plus the 10th

major tick.

Uses allstatesdc.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

twoway scatter propval100 faminc, ylabel(0(10)100, noticks)

If we wanted to label the y-axis using

values ranging from 0 to 100,

incrementing by 10 but suppressing the

display of ticks, we could use the

noticks option.

Uses allstatesdc.dta & scheme vg s1c

0 10 20 30 40 50 60 70 80 90 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

260 Chapter 8. Options available for most graphs

twoway scatter propval100 faminc, ylabel(, nolabel)

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

We could suppress the labels using the

nolabel option, and only the ticks

would be shown.

Uses allstatesdc.dta & scheme vg s1c

twoway scatter propval100 region

0 20 40 60 80 100

% homes cost $100K+

1 2 3 4

Census region

If a variable has meaningful value

labels, we can display the value labels

in place of the values. For example, we

can look at the propval100 broken

down by census region, but we do not

know which regions correspond to the

values1to4.

Uses allstatesdc.dta & scheme vg s1c

twoway scatter propval100 region, xlabel(, valuelabels)

0 20 40 60 80 100

% homes cost $100K+

NE N Cntrl South West

Census region

If we include the xlabel(,

valuelabels) option, the value labels

are displayed instead, making the graph

much easier to understand.

Uses allstatesdc.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.5 Setting and controlling axis labels 261

twoway scatter propval100 region,

xlabel(1 "NorthEast" 2 "NorthCentral" 3 "South" 4 "West")

If region were not labeled, or if we

wanted diﬀerent labels, we could

indicate those labels using the

xlabel() option, as illustrated here.

Uses allstatesdc.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

NorthEast NorthCentral South West

Census region

twoway scatter propval100 faminc, xlabel(, format(%8.0gc))

We can change the formatting of the

labels using the format() option, just

as we would using a format statement.

In this example, we format income

using a comma format to make the

larger numbers more readable.

Uses allstatesdc.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

15,000 20,000 25,000 30,000

1979 Median Family Inc.

twoway scatter propval100 faminc, ylabel(, angle(0))

We can change the angles of the labels

from their default orientation. By

default, the values on the y-axis are

shown at a 90-degree angle, but we can

use the ylabel(, angle(0)) to display

the labels without rotation.

Uses allstatesdc.dta & scheme vg s1c

100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

262 Chapter 8. Options available for most graphs

twoway scatter propval100 faminc, xlabel(15000(1000)30000, angle(45))

0 20 40 60 80 100

% homes cost $100K+

15000

16000

17000

18000

19000

20000

21000

22000

23000

24000

25000

26000

27000

28000

29000

30000

1979 Median Family Inc.

If we label an axis with a large number

of values (and especially with wide

values), the labels may crowd each

other and overlap. Here, we label the

x-axis from 15000 to 30000 in

increments of 1000. To avoid

overlapping, we add the angle(45)

option to show the labels at a 45-degree

angle.

Uses allstatesdc.dta & scheme vg s1c

twoway scatter propval100 faminc, xlabel(15000(1000)30000, alternate)

0 20 40 60 80 100

% homes cost $100K+

15000

16000

17000

18000

19000

20000

21000

22000

23000

24000

25000

26000

27000

28000

29000

30000

1979 Median Family Inc.

We can also avoid overlapping the axis

labels by adding the alternate option

to xlabel(). The labels are now

displayed in two rows in alternating

rows, so they are not crowded or

overlapped.

Uses allstatesdc.dta & scheme vg s1c

twoway scatter propval100 faminc, ylabel(0(5)90, labsize(vsmall))

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

We can control the size of labels with

the labsize() option. For example, we

might want to label our y-axis from 0

to 90, incrementing by 5. The labels

would ordinarily overlap, but if we add

the labsize(vsmall) option, the very

small labels no longer overlap.

Uses allstatesdc.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.5 Setting and controlling axis labels 263

twoway scatter propval100 faminc, ylabel(, labgap(*5))

We can control the gap between the

label and the tick with the labgap()

option. In this example, we increase the

gap between the y-labels and the

y-ticks to ﬁve times the original size.

Uses allstatesdc.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

twoway scatter propval100 faminc,

ylabel(, tlength(*1.5) tlwidth(*3) tposition(crossing))

You can control the tick length with

the tlength() option, the tick line

width with the tlwidth() option, and

the tick position with the tposition()

option. In this example, we make the

tick length 1.5 times normal and the

width three times normal, with the

ticks crossing the y-axis.

Uses allstatesdc.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

twoway scatter propval100 faminc,

ytick(0(10)100, tposition(outside))

ymtick(5(10)95, tposition(inside))

In this example, we place major ticks

from 0 to 100, incrementing by 10,

locating the ticks on the outside of the

plot, and place minor ticks from 5 to

95, incrementing by 10, placing the

ticks on the inside of the plot region.

Uses allstatesdc.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

264 Chapter 8. Options available for most graphs

twoway scatter propval100 faminc, ylabel(, nogrid)

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

We can use the grid and nogrid

options to display or suppress the

display of grid lines corresponding to

the labels and ticks associated with the

ylabel(),ymlabel(),ytick(),or

ymtick() options (this also applies to

xlabel(),xmlabel(),xtick(),or

xmtick()). Say that we want to

suppress the grid on the y-axis. We can

do this with the ylabel(, nogrid)

option.

Uses allstatesdc.dta & scheme vg s1c

twoway scatter propval100 faminc, ylabel(, grid) xlabel(, grid)

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

If we want a grid to be displayed for

the values that correspond to the

ylabel() and the xlabel() options,

we can specify the grid option, as

shown in this example. Depending on

the scheme you choose, grids may be

included or omitted by default.

Uses allstatesdc.dta & scheme vg s1c

twoway scatter propval100 faminc,

ylabel(, grid glwidth(vthin) glcolor(gs10) glpattern(shortdash))

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

You can control the grid line width,

grid line color, and grid line pattern

with the glwidth(),glcolor(),and

glpattern() options. In this example,

we make the grid line very thin, the

color gray (gs10), and the pattern of

the lines short dashes. See

Styles : Linewidth (337), Styles : Colors

(328), and Styles : Linepatterns (336) for

additional details.

Uses allstatesdc.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.6 Controlling axis scales 265

twoway scatter propval100 faminc,

ylabel(0(20)100, grid glcolor(gs8) glpattern(solid))

ymlabel(10(20)90, grid glcolor(gs11) glpattern(shortdash))

We can use diﬀerent kinds of grid lines

for the major and minor axis labels. In

this example, we have a solid, darker

gray line for the major axis labels and a

lighter gray, short, dashed line for the

minor axis labels. We include the grid

option to ensure that the grid is

displayed.

Uses allstatesdc.dta & scheme vg s1c

10 30 50 70 90

0 20 40 60 80 100

% homes cost $100K+

15000 20000 25000 30000

1979 Median Family Inc.

8.6 Controlling axis scales

This section provides more details about axis scale options, which allow us to control

whether an axis is displayed, where it is displayed, the direction it is displayed, and the scale

of the axis. For more information about these options, see [G]axis scale options. This

section begins by using data on the S&P 500 from January 2, 2001, to December 31, 2001,

stored in the ﬁle sp2001. For simplicity, we will use tradeday on the x-axis, representing

the trading day of the year. For this section, we will use the vg s2m scheme.

twoway rspike high low tradeday

First, consider this rspike graph,

which shows the high and low prices

across 248 trading days.

Uses sp2001.dta & scheme vg s2m

900 1000 1100 1200 1300 1400

High price/Low price

0 50 100 150 200 250

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

266 Chapter 8. Options available for most graphs

twoway rspike high low tradeday, xscale(off)

900 1000 1100 1200 1300 1400

High price/Low price

If we wish, we could remove the display

of the x-axis entirely with the

xscale(off) option. Although it is not

shown, the same could be done for the

y-axis if we were to use the

yscale(off) option. This is not

normally an option we would use, but it

can be useful for combining multiple

graphs on the same scale without

having to show the scale on some of the

graphs.

Uses sp2001.dta & scheme vg s2m

twoway rspike high low tradeday, xscale(alt)

900 1000 1100 1200 1300 1400

High price/Low price

0 50 100 150 200 250

Trading day number We could shift the display of the x-axis

from the bottom of the graph to the

top of the graph with the xscale(alt)

option. Likewise, we could have chosen

to supply the yscale(alt) option to

shift the y-axis from the left to the

right.

Uses sp2001.dta & scheme vg s2m

twoway rspike high low tradeday, xscale(reverse)

900 1000 1100 1200 1300 1400

High price/Low price

050100150200250

Trading day number

We can reverse the scale of the x-axis

by specifying the xscale(reverse)

option, as illustrated here. We can

reverse the y-axis by indicating the

yscale(reverse) option.

Uses sp2001.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.6 Controlling axis scales 267

twoway scatter educ popden, xscale(log)

We brieﬂy return to the allstates ﬁle

to illustrate the xscale(log) option.

The xscale(log) option indicates that

the x-axis should be displayed on a log

scale. Note that the labels for the

x-axis overlap each other.

Uses allstates.dta & scheme vg s2m

9.5 10 10.5 11

average education level

2000 40006000800010000

Pop/10 sq. miles

twoway scatter educ popden, xscale(log)

xlabel(1 10 100 1000 10000)

Here, we use the xlabel() option to

change the labels for the x-axis using

the values 1, 10, 100, 1000, and 10,000,

and you can see how these powers of 10

are more equally spaced, reﬂecting the

log scale of the x-axis.

Uses allstates.dta & scheme vg s2m

9.5 10 10.5 11

average education level

1 10 100 1000 10000

Pop/10 sq. miles

twoway rspike high low tradeday, xscale(lwidth(thick))

We now return to the sp2001 data.

You can use the xscale() and

yscale() options to control the axis

lines. In this example, we make the

x-axis line thick by specifying

xscale(lwidth(thick)).

Uses sp2001.dta & scheme vg s2m

900 1000 1100 1200 1300 1400

High price/Low price

0 50 100 150 200 250

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

268 Chapter 8. Options available for most graphs

twoway rspike high low tradeday, xscale(off noline)

900 1000 1100 1200 1300 1400

High price/Low price

We could suppress the display of the

x-axis line completely by using the

xscale(noline) option.

Uses sp2001.dta & scheme vg s2m

twoway rspike high low tradeday, yscale(range(700 1400))

900 1000 1100 1200 1300 1400

High price/Low price

0 50 100 150 200 250

Trading day number

The yscale(range()) option can be

used to expand the scale of the y-axis

without needing to expand the labels

for the axis (as the ylabel() option

would). In this example, we have

expanded the range of the y-axis from

700 to 1400. However, this example

does not show the real utility of this

option. Note that range() can only be

used to expand the scale, not contract

it.

Uses sp2001.dta & scheme vg s2m

twoway (rspike high low tradeday)

(line volmil tradeday, sort yaxis(2))

.5 1 1.5 2 2.5

Volume (millions)

900 1000 1100 1200 1300 1400

High price/Low price

0 50 100 150 200 250

Trading day number...

High price/Low price Volume (millions)

Consider that, in addition to the spike

graph that shows the high and low

values for a given trading day, we wish

to see the volume for a given trading

day. We can combine the plots into a

single graph, but this is diﬃcult to read

because the two plots overlap.

Uses sp2001.dta & scheme vg s2m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.7 Selecting an axis 269

twoway (rspike high low tradeday)

(line volmil tradeday, sort yaxis(2)),

yscale(range(700 1400) axis(1)) yscale(range(0 10) axis(2))

This example shows the utility of the

yscale(range()) option. The

yscale(range(700 1400) axis(1))

option sets the range of price to be

from 700 to 1400, shifting that series up

to the upper third of the graph. The

yscale(range(0 10) axis(2)) option

sets the range of volume to occupy the

lower third of the graph.

Uses sp2001.dta & scheme vg s2m

.511.522.5

Volume (millions)

900 100011001200 13001400

High price/Low price

0 50 100 150 200 250

Trading day number...

High price/Low price Volume (millions)

twoway (rspike high low tradeday)

(line volmil tradeday, sort yaxis(2)),

yscale(range(700 1400) axis(1)) yscale(range(0 10) axis(2))

ylabel(1000 1200 1400, axis(1)) ylabel(0 1 2, axis(2))

Because we manipulated the scale of

the y-axes, the labels were pushed

together. We can add the ylabel(1000

1200 1400, axis(1)) and ylabel(0 1

2, axis(2)) options to the previous

example to make the labels for the

y-axes more readable.

Uses sp2001.dta & scheme vg s2m

012

Volume (millions)

1000 1200 1400

High price/Low price

0 50 100 150 200 250

Trading day number...

High price/Low price Volume (millions)

8.7 Selecting an axis

This section provides more details about how to select diﬀerent axes and modify them.

By default, any modiﬁcations you make to an axis are applied to the ﬁrst axis, so you need

to take extra action to modify other axes that you may create. For more information about

these options, see [G]axis selection options. For this section, we will use the vg outc

scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

270 Chapter 8. Options available for most graphs

twoway scatter faminc educ

15000 20000 25000 30000

1979 Median Family Inc.

9.5 10 10.5 11

average education level

This section focuses on the options that

we can use to select axes and shows

examples of graphing multiple variables

in a single graph. This graph shows the

relationship between one x-variable,

educ, and one y-variable, faminc.

Uses allstatesdc.dta & scheme vg outc

twoway (scatter faminc educ, xaxis(1) yaxis(1))

15000 20000 25000 30000

1979 Median Family Inc.

9.5 10 10.5 11

average education level

By default, the x-variable is placed on

the ﬁrst x-axis, and the y-variable is

placed on the ﬁrst y-axis. It is as

though you had added the options

xaxis(1) and yaxis(1), as illustrated

here. Note that we add parentheses to

emphasize that the options xaxis(1)

and yaxis(1) belong to the scatter

command and are not general options

for the overall graph, which would

appear after the parentheses.

Uses allstatesdc.dta & scheme vg outc

twoway (scatter faminc educ)

(scatter workers2 educ)

0 10000 20000 30000

9.5 10 10.5 11

average education level

1979 Median Family Inc. % HHs with 2+ workers

Now let’s overlay a second scatterplot

showing workers2 by educ, which has

the eﬀect of adding a second variable to

the y-axis. Stata assumes that all

variables are on the ﬁrst (and thus, the

same) axis, unless we specify otherwise.

As a result, this graph is hard to read

because faminc is scaled very

diﬀerently from workers2 but scaled on

the same axis.

Uses allstatesdc.dta & scheme vg outc

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.7 Selecting an axis 271

twoway (scatter faminc educ, yaxis(1))

(scatter workers2 educ, yaxis(2))

Stata permits you to have multiple axes

for the x-variables and the y-variables.

In this example, we use the yaxis(1)

option to place faminc on the ﬁrst

y-axis and the yaxis(2) option to

place workers2 on the second y-axis.

To make the graph more readable,

Stata moved the second y-axis over to

the right side. Note that the yaxis(1)

option was not needed but was included

for clarity.

Uses allstatesdc.dta & scheme vg outc

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

9.5 10 10.5 11

average education level...

1979 Median Family Inc. % HHs with 2+ workers

twoway (scatter faminc educ)

(scatter workers2 educ, yaxis(2)), ylabel(40(5)80, axis(2))

Say that you wished to label workers2

starting at 40, incrementing by 5 until

80. Since workers2 is on the second

y-axis, you would specify

ylabel(40(5)80, axis(2)). Without

the axis(2) option, Stata would

assume that you are referring to the

ﬁrst y-axis and would change the

scaling of faminc.

Uses allstatesdc.dta & scheme vg outc

40 45 50 55 60 65 70 75 80

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

9.5 10 10.5 11

average education level...

1979 Median Family Inc. % HHs with 2+ workers

twoway (scatter faminc educ)

(scatter workers2 educ, yaxis(2) ylabel(40(5)80))

You might be tempted to enter the

ylabel() option as an option of the

second scatter statement and expect

the ylabel() to modify the scaling of

workers2. However, we can see in this

example that this does not work.

Uses allstatesdc.dta & scheme vg outc

45 50 55 60 65 70

% HHs with 2+ workers

404550556065707580

1979 Median Family Inc.

9.5 10 10.5 11

average education level...

1979 Median Family Inc. % HHs with 2+ workers

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

272 Chapter 8. Options available for most graphs

twoway (scatter faminc educ)

(scatter workers2 educ, yaxis(2)), ylabel(40(5)80, axis(1))

45 50 55 60 65 70

% HHs with 2+ workers

404550556065707580

1979 Median Family Inc.

9.5 10 10.5 11

average education level...

1979 Median Family Inc. % HHs with 2+ workers

ylabel() is really an overall option,

but Stata is willing to pretend that you

speciﬁed this option globally, as though

you had typed ylabel() as a global

option as speciﬁed in this example. To

make this clearer, we have added the

default axis(1) to ylabel() to

illustrate why this usage does not

change the second y-axis.

Uses allstatesdc.dta & scheme vg outc

twoway (scatter faminc educ)

(scatter workers2 educ, yaxis(2)),

ytitle("Family income", axis(1)) ytitle("Two+ workers", axis(2))

45 50 55 60 65 70

Two+ workers

15000 20000 25000 30000

Family income

9.5 10 10.5 11

average education level...

1979 Median Family Inc. % HHs with 2+ workers

These same rules apply to modifying

the axis titles and labeling. In this

example, we use the ytitle() option

to change the titles for the ﬁrst and

second y-axes.

Uses allstatesdc.dta & scheme vg outc

8.8 Graphing by groups

This section provides more details about repeating graphs using the by() option to

show separate graphs for each by-group. For more information, see [G]by option. For this

section, we will use the vg brite scheme.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.8 Graphing by groups 273

twoway scatter ownhome borninstate

We start by looking at a scatterplot of

ownhome and borninstate, and we see

a general positive relationship such that

the higher the percentage of those who

were born in the state, the higher the

percentage of home owners in the state.

Uses allstatesdc.dta & scheme vg brite

55 60 65 70 75 80

% who own home

20 40 60 80

% born in state of residence

twoway scatter ownhome borninstate, by(north)

We can use the by(north) option to

look at this relationship broken down

by whether the state is considered to be

in the North.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north, total)

We can use the total option to see the

overall relationship for all 50 states, as

well as the two plots separately, by the

levels of north.

Uses allstatesdc.dta & scheme vg brite

50 60 70 8050 60 70 80

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

274 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, by(north, total colfirst)

50 60 70 8050 60 70 80

20 40 60 80

S & W

North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

We can add the colfirst option to

show the graphs going down columns

ﬁrst rather than going across rows ﬁrst,

which is the default.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north, total holes(2))

50 60 70 8050 60 70 80

20 40 60 80 20 40 60 80

S & W

North Total

% who own home

% born in state of residence

Graphs by Region North or Not

The holes(2) option leaves the second

position empty. Here, we specify a

single position to leave empty, but you

can specify multiple positions within

the holes() option.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north, total rows(1))

50 60 70 80

20 40 60 80 20 40 60 80 20 40 60 80

S & W North Total

% who own home

% born in state of residence

Graphs by Region North or Not

The rows(1) option indicates that the

graph should be displayed in one row.

Uses allstatesdc.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.8 Graphing by groups 275

twoway scatter ownhome borninstate, by(north, total cols(1))

The cols(1) option shows the graph in

a single column.

Uses allstatesdc.dta & scheme vg brite

50 60 70 8050 60 70 8050 60 70 80

20 40 60 80

S & W

North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north, total iscale(*1.5))

Sometimes when you use the by()

option, the graph can become small,

making the text and symbols diﬃcult to

see. You can use the iscale() option

to magnify the size of these elements.

In this example, we increase the size of

these elements by a factor of 1.5.

Uses allstatesdc.dta & scheme vg brite

5060708050607080

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north, total compact)

The compact option displays the graph

using a compact style, pushing the

graphs tightly together. This is almost

the same as specifying

style(compact).

Uses allstatesdc.dta & scheme vg brite

50 60 70 8050 60 70 80

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

276 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, by(north, total noedgelabel)

50 60 70 8050 60 70 80

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

The noedgelabel option suppresses the

displayofthex-axis for the graphs that

do not appear on the bottom row, in

this case the graph for the North.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north, yrescale)

55 60 65 70 75

60 65 70 75 80

20 40 60 80 20 40 60 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

The yrescale option allows the

y-variables to be scaled independently

for each by-group.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north, xrescale)

50 60 70 80

20 40 60 80 40 50 60 70 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

Likewise, the xrescale option allows

the x-variable to be scaled diﬀerently

across all the by-groups.

Uses allstatesdc.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.8 Graphing by groups 277

twoway scatter ownhome borninstate, by(north, rescale)

If you want both the x-variable and

y-variable to be scaled diﬀerently across

the by-groups, you can use the rescale

option, and both axes are separately

rescaled.

Uses allstatesdc.dta & scheme vg brite

55 60 65 70 75

60 65 70 75 80

20 40 60 80 40 50 60 70 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north, iyaxes)

You can use the iyaxes option so the

y-axes for each individual graph will be

displayed.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north, cols(1))

Likewise, the ixaxes option will display

the x-axis for all graphs. In this graph,

we omit this option. If we display two

graphs in a single column, Stata

displays the top graph, omitting the

x-axis.

Uses allstatesdc.dta & scheme vg brite

50 60 70 8050 60 70 80

20 40 60 80

S & W

North

% who own home

% born in state of residence

Graphs by Region North or Not

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

278 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, by(north, ixaxes cols(1))

50 60 70 8050 60 70 80

20 40 60 80

S & W

North

% who own home

% born in state of residence

Graphs by Region North or Not

We now include the ixaxes option and

see that the x-axis is now displayed on

the top graph.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north, total iytitle)

50 60 70 80

% who own home

50 60 70 80

% who own home

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

We can display the title for each y-axis

using the iytitle option.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north, total iyaxes iytitle)

50 60 70 80

% who own home

50 60 70 80

% who own home

50 60 70 80

% who own home

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

Note that the y-title is not displayed for

the North since the y-axis is omitted for

that graph. If we include the iyaxes

and iytitle options, the y-axis and

y-title are displayed for that graph as

well.

Uses allstatesdc.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.8 Graphing by groups 279

twoway scatter ownhome borninstate, by(north, total ixaxes ixtitle)

Likewise, we can display the x-title on

each graph using the ixaxes and

ixtitle options.

Uses allstatesdc.dta & scheme vg brite

50 60 70 8050 60 70 80

20 40 60 80

% born in state of residence

20 40 60 80

% born in state of residence

20 40 60 80

% born in state of residence

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north) title("My title")

If we include a title() option with

by(), Stata creates each graph

separately using the title we specify.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

My title My title

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north, title("My title"))

If we make the title() an option

within the by() option, Stata will make

this an overall title for the graph.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

My title

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

280 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, by(north, title("By title"))

title("Regular title")

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

Regular title Regular title

% who own home

% born in state of residence

Graphs by Region North or Not

By title This example should help you to

understand how these two types of

titles work. When the title() is used

overall, it applies to all graphs that are

created because it is repeated via the

by() option. The by(title()) is

applied after all smaller graphs are

created, providing an overall title for

the graph.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north)

caption("Regular caption")

50 60 70 80

20 40 60 80 20 40 60 80

Regular caption Regular caption

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

Stata treats the caption() option the

same way that it treats titles. Here, we

include an overall caption, which is

displayed with each graph.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north, caption("By caption"))

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

By caption

When we include the caption() inside

the by() option, it is displayed as a

caption for the full graph.

Uses allstatesdc.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.8 Graphing by groups 281

twoway scatter ownhome borninstate, by(north)

subtitle("This is a subtitle")

Stata treats the subtitle() option

diﬀerently than the title() and

caption() options. Here, we include a

subtitle() option, and we see that it

has replaced the title above each graph

that represented the names of the

by-group.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

This is a subtitle This is a subtitle

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north)

subtitle("Region of state", prefix)

We can use the subtitle() option to

add more labeling to the by-group

names. Here, we use the prefix option

to insert text that appears in the

subtitle before the name of the

by-group.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

Region of state

S & W

Region of state

North

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter ownhome borninstate, by(north)

subtitle("Region of state", suffix)

We can use the suffix option to insert

text that appears in the subtitle after

the name of the by-group.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

S & W

Region of state

North

Region of state

% who own home

% born in state of residence

Graphs by Region North or Not

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

282 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate, by(north)

subtitle("State’s location", prefix)

subtitle("Based on Region", suffix)

50 60 70 80

20 40 60 80 20 40 60 80

State’s location

S & W

Based on Region

State’s location

North

Based on Region

% who own home

% born in state of residence

Graphs by Region North or Not

We can even combine the prefix and

suffix option to insert text before and

after the label of the by-group.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate,

by(north, subtitle("This is a subtitle"))

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

This is a subtitle When used as an option within the

by() option, the subtitle() option

works just like the title() and

caption() options, placing a subtitle

on the overall graph.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate, by(north) note("Regular note")

50 60 70 80

20 40 60 80 20 40 60 80

Regular note Regular note

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

Stata treats the note() option much as

it does the title(),caption(),and

subtitle() options. Here, we include a

note() option and see that it is shown

beneath both graphs.

Uses allstatesdc.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.8 Graphing by groups 283

twoway scatter ownhome borninstate, by(north, note("By note"))

If we include the note() option within

the by() option, we see that our note

overrides the note that Stata provided

to indicate that the graphs were

separated by the variable north.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

% who own home

% born in state of residence

By note

twoway scatter ownhome borninstate,

by(north, note("North N=21, Not North N=29", suffix))

As with the subtitle() option, we can

use the prefix or suffix option to add

our own text before or after the existing

note.

Uses allstatesdc.dta & scheme vg brite

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

% who own home

% born in state of residence

Graphs by Region North or Not

North N=21, Not North N=29

twoway scatter ownhome borninstate,

by(north, total) subtitle(, position(11))

Previously, we saw that the

subtitle() option could be used to

modify the by-group names above each

graph. We can also use the subtitle(,

position()) option to modify the

placement of this text. Here, we move

the text to appear in the 11 o’clock

position.

Uses allstatesdc.dta & scheme vg brite

50 60 70 8050 60 70 80

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

284 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate,

by(north, total) subtitle(, pos(5) ring(0) nobexpand)

50 60 70 8050 60 70 80

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

We can place the name of the by-group

in the bottom right corner of each

graph using the subtitle() option.

The options pos(5) and ring(0) move

the subtitle to the 5 o’clock position

and inside the plot region. The

nobexpand (no box expand) option

prevents the by-group name from

expanding to consume the entire plot

region.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate,

by(north, total title("My title", ring(0) position(5)))

50 60 70 8050 60 70 80

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

My title

We can also use the ring() and pos()

options with title(),note(),and

caption() to alter their placement.

Here, we use position(5) to put the

title in the bottom right corner and

ring(0) to locate it inside the plot

region.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate,

by(north, total title("My title", position(5)))

50 60 70 8050 60 70 80

20 40 60 80

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

My title

The previous graph is repeated with

the position(5) option but not the

ring(0) option to illustrate the impact

of ring(0). Without ring(0), the title

is placed outside the plot region.

Uses allstatesdc.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.8 Graphing by groups 285

twoway scatter ownhome borninstate,

by(north, total) l1title("left title") b1title("bottom title")

Including the l1title() option adds a

title to the left (on the y-axis) of each

of the graphs. Likewise, the b1title()

option adds a title to the bottom (on

the x-axis) of each of the graphs.

Uses allstatesdc.dta & scheme vg brite

50 60 70 8050 60 70 80

20 40 60 80

left title

bottom title bottom title

bottom title

S & W North

Total

% who own home

% born in state of residence

Graphs by Region North or Not

twoway scatter (borninstate propval100 ownhome), by(nsw)

legend(label(1 "Born in state") label(2 "% > 100K"))

Here, we use the legend() option to

change the labels associated with the

ﬁrst two keys. These options modify the

contents of the legend, so they should

appear outside of the by() option.

Uses allstatesdc.dta & scheme vg brite

0 50 1000 50 100

50 60 70 80

North South

West

Born in state % > 100K

% who own home

Graphs by Region North, South, or West

twoway scatter (borninstate propval100 ownhome),

by(nsw, legend(position(12)))

In this graph, we use the position()

option to modify the position of the

legend. Such options that modify the

position of the legend must be placed as

an option within the by() option.

Uses allstatesdc.dta & scheme vg brite

0 50 1000 50 100

50 60 70 80

North South

West

% born in state of residence % homes cost $100K+

% who own home

Graphs by Region North, South, or West

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

286 Chapter 8. Options available for most graphs

twoway scatter (borninstate propval100 ownhome),

by(nsw, legend(pos(12)))

legend(label(1 "Born in state") label(2 "% > 100K"))

0 50 1000 50 100

50 60 70 80

North South

West

Born in state % > 100K

% who own home

Graphs by Region North, South, or West

Here, we use both of the options from

the previous two graphs, and the

legend() option is used twice: inside

the by() option to modify the position

and outside the by() option to modify

its contents. The use of legend() with

the by() option is covered more

thoroughly in Options : Legend (287).

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate,

by(north, title("% own home" "by % born in state"))

title("Region of state")

50 60 70 80

20 40 60 80 20 40 60 80

S & W North

Region of state Region of state

% who own home

% born in state of residence

Graphs by Region North or Not

% own home

by % born in state

We can use the title() option on its

own to make a title that is displayed

with each graph, and the title()

option within the by() option to make

an overall title.

Uses allstatesdc.dta & scheme vg brite

twoway scatter ownhome borninstate,

by(north, total rescale ixtitle iytitle b1title("") l1title(""))

55 60 65 70 75

% who own home

60 65 70 75 80

% who own home

50 60 70 80

% who own home

20 40 60 80

% born in state of residence

40 50 60 70 80

% born in state of residence

20 40 60 80

% born in state of residence

S & W North

Total

Graphs by Region North or Not

Here, we obtain separate graphs for the

three groups, using rescale to obtain

diﬀerent x-andy-axis labels and scales,

ixtitle and iytitle to title the

graphs separately, and b1title() and

l1title() to suppress the overall titles

for the x-andy-axes.

Uses allstatesdc.dta & scheme vg brite

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.9 Controlling legends 287

8.9 Controlling legends

This section describes more details about using legends. Legends can be useful in a

number of situations, and this section shows how to customize them. For more information

about legend options, see [G]legend option. Also, for controlling the text and textbox of

the legend, see Options : Textboxes (303) and Options : Adding text (299). We will use the

vg s2c scheme.

twoway scatter ownhome propval100 urban

Legends can be created in a variety of

ways. For example, here we have two

y-variables, ownhome and propval100,

on the same plot, and Stata creates a

legend labeling the diﬀerent points.

The default legend, in this case, is quite

useful.

Uses allstatesdc.dta & scheme vg s2c

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% who own home % homes cost $100K+

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban)

Legends are also created when you

overlay plots. Here, Stata adds a legend

entry for each of the overlaid plots. The

default legend, in this case, is less useful

since it does not help us diﬀerentiate

between the kinds of ﬁt values.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% who own home Fitted values

Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

288 Chapter 8. Options available for most graphs

twoway (scatter ownhome urban if north==0)

(scatter ownhome urban if north==1)

55 60 65 70 75 80

% who own home

20 40 60 80 100

Percent urban 1990

% who own home % who own home

A third example is when you overlay

two plots using if to display the same

variables but for diﬀerent observations.

Here, we show the same scatterplot

separately for states in the North and

for those not in the North. Here, the

legend does not help us at all to

diﬀerentiate the kinds of values.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban)

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% who own home Fitted values

Fitted values

Regardless of the graph command(s)

that generated the legend, it can be

customized the same way. For many of

the examples, we will use this graph for

customizing the legend.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban),

legend(label(1 "% Own home") label(2 "Lin. Fit") label(3 "Quad. Fit"))

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% Own home Lin. Fit

Quad. Fit

You can use the label() option to

assign labels for the keys. Note that

you use a separate label() option for

each key that you wish to modify.

Uses allstatesdc.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.9 Controlling legends 289

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban),

legend(label(2 "Lin. Fit") label(3 "Quad. Fit"))

You can use the label() option to

modify just some of the keys; for

example, here we just modify the

second and third key.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% who own home Lin. Fit

Quad. Fit

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban),

legend(label(1 "%own" "home") label(2 "Lin" "Fit") label(3 "Qd" "Fit"))

You can put the label on multiple lines

by including multiple quoted strings.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

%own

home

Lin

Fit

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban), legend(order(2 3 1))

You can use the order() option to

change the order of the keys in the

legend.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Fitted values Fitted values

% who own home

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

290 Chapter 8. Options available for most graphs

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban), legend(order(2 3))

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Fitted values Fitted values

We can also omit keys from the

order() option to suppress their

display in the legend. Here, we suppress

the display of the ﬁrst key.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban), legend(order(2 "Lin. fit" 3 "Quad. fit" 1))

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Lin. fit Quad. fit

% who own home

You can also insert and replace text for

the keys when using the order()

option. Here, we order the keys 2, 3,

and 1, and at the same time, replace

the text for keys 2 and 3.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban)

(qfit ownhome urban),

legend(order(- "Fitted" 2 "Lin. fit" 3 "Quad. fit" - "Observed" 1))

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Fitted Lin. fit

Quad. fit Observed

% who own home

We use - "Fitted" to insert the word

Fitted and - "Observed" to insert the

word Observed. Due to the

organization of the keys in the legend,

this is hard to follow.

Uses allstatesdc.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.9 Controlling legends 291

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(- "Fitted" 2 "Lin. fit" 3 "Quad. fit" - "Observed" 1)

cols(1))

We can use the cols() option to

display the legend in a single column.

Here, the added text makes more sense,

but the legend uses quite a bit of space.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Fitted

Lin. fit

Quad. fit

Observed

% who own home

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(- "Fitted" 2 "Lin. fit" 3 "Quad. fit" - "Observed" 1)

rows(3))

We can use the rows() option to

display the legend in three rows. If we

want to display the ﬁtted keys in the

left column and the observed keys in

the right column, we can order the keys

according to columns instead of

according to rows. See the next

example.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Fitted Lin. fit

Quad. fit Observed

% who own home

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(- "Fitted" 2 "Lin. fit" 3 "Quad. fit" - "Observed" 1)

rows(3) colfirst)

Adding the colfirst option displays

the keys in column order instead of row

order, with the Fitted keys in the left

column and the Observed keys in the

right column.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Fitted

Lin. fit

Quad. fit

Observed

% who own home

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

292 Chapter 8. Options available for most graphs

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(- "Observed" 1 - "Fitted" 2 "Lin. fit" 3 "Quad. fit")

rows(3) holes(3) colfirst)

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Observed

% who own home

Fitted

Lin. fit

Quad. fit

This legend is the same as the one in

the previous example but places the

Observed keys in the left column and

the Fitted keys in the right column. To

do this, we changed the order of the

keys but also added the holes(3)

option so that Fitted would be in the

fourth position at the top of the second

column.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(- "Observed" 1 -""- "Fitted" 2 "Lin fit" 3 "Qd fit")

rows(3) colfirst)

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Observed

% who own home

Fitted

Lin fit

Qd fit

Referring to the last graph, instead of

using holes(), we can put in a blank

key, -"",intheorder() option,

which pushes the word Fitted over to

the next column.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(- "Observed" 1 - " " - "Fitted" 2 "Lin fit" 3 "Qd fit")

rows(3) colfirst textfirst)

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Observed

% who own home

Fitted

Lin fit

Qd fit

Using the textfirst option, we can

make the text for the key appear ﬁrst,

followed by the symbol.

Uses allstatesdc.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.9 Controlling legends 293

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(2 "Linear" "Fit" 3 "Quadratic" "Fit")

stack cols(1))

Using the stack option, we can stack

the symbols above the labels. We use

this here to make a tall, narrow legend.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Linear

Fit

Quadratic

Fit

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(2 "Linear" "Fit" 3 "Quadratic" "Fit")

stack cols(1) position(3))

We can use the position() option to

change where the legend is displayed.

Here, we take the narrow legend from

the previous graph and put it to the

right of the graph, making good use of

space.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Linear

Fit

Quadratic

Fit

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(2 "Linear" "Fit" 3 "Quadratic" "Fit")

stack cols(1) ring(0) position(7))

We can use the ring(0) option to place

the legend inside the plot area and use

position(7) to put it in the bottom

left corner, using the empty space in

the plot for the legend.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Linear

Fit

Quadratic

Fit

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

294 Chapter 8. Options available for most graphs

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(1 "% Own Home" 2 "Linear" 3 "Quad")

rows(1) position(12))

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% Own Home Linear Quad

Here, we make the legend a thin row

using the rows(1) option and then use

the position(12) option to put it at

the top of the graph.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(1 "% Own Home" 2 "Linear" 3 "Quad")

rows(1) position(12) bexpand)

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% Own Home Linear Quad

We can expand the width of the legend

to the width of the plot area using the

bexpand (box expand) option.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(2 "Linear Fit" 3 "Quadratic Fit")

rows(1) position(12) bexpand span)

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Linear Fit Quadratic Fit

If we wanted to expand the legend to

the entire width of the graph area (not

just the plot area), we would add the

span option.

Uses allstatesdc.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.9 Controlling legends 295

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(2 "Linear Fit" 3 "Quadratic Fit")

rows(1) pos(5) title("Legend", position(11)))

We can add a title, subtitle, note, or

caption to the legend using all the

features described in Standard

options : Titles (313). Here, we add a

title() and use the position()

option to position it in the top left

corner. A simple way to get a smaller

title is to use the subtitle() option

instead.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Linear Fit Quadratic Fit

Legend

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(2 "Linear Fit" 3 "Quadratic Fit")

rows(1) pos(5) subtitle("Legend", box bexpand))

To emphasize all the control we have,

we could put the subtitle for the legend

in a box and use bexpand to make it

expand to the width of the legend.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Linear Fit Quadratic Fit

Legend

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(order(2 "Linear Fit" 3 "Quadratic Fit")

rows(1) pos(5) note("Fit obtained with lfit and qfit"))

Here, we use the note() option,

showing that we can even add a note to

the legend.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

Linear Fit Quadratic Fit

Fit obtained with lfit and qfit

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

296 Chapter 8. Options available for most graphs

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(size(medium) color(maroon) bfcolor(eggshell) box)

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% who own home Fitted values

Fitted values

The legend() option permits us to

supply options that control the display

of the labels for the keys. Here, we

request that those labels be maroon,

medium in size, displayed with an

eggshell background, and surrounded

by a box.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(region(fcolor(dimgray) lcolor(gs8) lwidth(thick) margin(medium)))

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% who own home Fitted values

Fitted values

The region() option can be used to

control the overall box in which the

legend is placed. Here, we specify the

ﬁll color to be a dim gray, the line color

to be a medium gray

(gs8 = gray scale 8), the line to be

thick, and the margin between the text

and the box to be medium.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(rows(1) bmargin(t=10))

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% who own home Fitted values Fitted values

We can adjust the margin around the

box of the legend with the bmargin()

option. Here, we use t=10 to make the

margin 10 at the top, increasing the

gap between the legend and the title of

the x-axis.

Uses allstatesdc.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.9 Controlling legends 297

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(symxsize(30) symysize(20))

We can control the width allocated to

symbols with the symxsize() option

and the height with the symysize()

option.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% who own home Fitted values

Fitted values

twoway (scatter ownhome urban) (lfit ownhome urban) (qfit ownhome urban),

legend(colgap(20) rowgap(20))

We can control the space between

columns of the legend with the

colgap() option and the space between

the rows with the rowgap() option.

Note that the rowgap() option does

not aﬀect the border between the top

row and the box or the border between

the bottom row and the box.

Uses allstatesdc.dta & scheme vg s2c

55 60 65 70 75 80

20 40 60 80 100

Percent urban 1990

% who own home Fitted values

Fitted values

twoway (scatter ownhome urban) (qfit ownhome urban),

by(nsw)

Consider this graph, which shows two

overlaid scatterplots shown separately

by the location of the state. We will

explore how to modify the legend for

this kind of graph.

Uses allstatesdc.dta & scheme vg s2c

50 60 70 8050 60 70 80

40 60 80 100

North South

West

% who own home Fitted values

Percent urban 1990

Graphs by Region North, South, or West

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

298 Chapter 8. Options available for most graphs

twoway (scatter ownhome urban) (qfit ownhome urban),

by(nsw) legend(position(12) label(2 "Quadratic Fit"))

50 60 70 8050 60 70 80

40 60 80 100

North South

West

% who own home Quadratic Fit

Percent urban 1990

Graphs by Region North, South, or West

Here, we add a legend() option, but

the position() option does not seem

to have any eﬀect since it does not

move the position of the legend.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (qfit ownhome urban),

by(nsw, legend(position(12))) legend(label(2 "Quadratic Fit"))

50 60 70 8050 60 70 80

40 60 80 100

North South

West

% who own home Quadratic Fit

Percent urban 1990

Graphs by Region North, South, or West

The graph command from the last

example did not change the position of

the legend because options for

positioning the legend must be placed

within the by() option. Here, we place

the legend(position()) option within

the by() option, and the legend is now

placed above the graph.

Uses allstatesdc.dta & scheme vg s2c

twoway (scatter ownhome urban) (qfit ownhome urban), by(nsw, legend(off))

50 60 70 8050 60 70 80

40 60 80 100

North South

West

Percent urban 1990

Graphs by Region North, South, or West

Likewise, if we wish to turn the legend

oﬀ, we must place legend(off) within

the by() option.

Uses allstatesdc.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.10 Adding text to markers and positions 299

twoway (scatter ownhome urban) (qfit ownhome urban),

by(nsw, legend(at(4))) legend(cols(1))

To place the legend in one of the holes,

we can use the at() option within the

by() option. Here, the legend is placed

inside the fourth position. To display

the legend in one column, we use the

legend(cols(1)) option outside of the

by() option since this does not control

the position of the legend.

Uses allstatesdc.dta & scheme vg s2c

50 60 70 8050 60 70 80

40 60 80 100

North South

West

% who own home

Fitted values

Percent urban 1990

Graphs by Region North, South, or West

twoway (scatter ownhome urban) (qfit ownhome urban),

by(nsw, legend(position(center) at(4))) legend(cols(1))

To position the legend, we can add the

position(center) option within the

by() option to make the legend appear

in the center of the fourth position.

Uses allstatesdc.dta & scheme vg s2c

50 60 70 8050 60 70 80

40 60 80 100

North South

West

% who own home

Fitted values

Percent urban 1990

Graphs by Region North, South, or West

8.10 Adding text to markers and positions

This section provides more details about the text() option for adding text to a graph.

Although added text can be used in a wide variety of situations, we will focus on how it can

be used to label points and lines and to add descriptive text to your graph. For more infor-

mation about this option, see [G]added text option. To learn more about how the text

can be customized, see Options : Textboxes (303). For this section, we will use the vg teal

scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

300 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate

% who own home

40 50 60 70 80

% born in state of residence

In this scatterplot, one point appears to

be an outlier from the rest, but since it

is not labeled, we cannot tell from

which state it originates.

Uses allstatesn.dta & scheme vg teal

scatter ownhome borninstate, mlabel(stateab)

IN IA

MONE

VT WI

% who own home

40 50 60 70 80

% born in state of residence

We can use the mlabel(stateab) to

label all points, which helps us see that

the outlying point comes from

Washington, DC. However, this plot is

rather cluttered by all the labels.

Uses allstatesn.dta & scheme vg teal

twoway (scatter ownhome borninstate)

(scatter ownhome borninstate if stateab == "DC", mlabel(stateab))

% who own home

40 50 60 70 80

% born in state of residence

% who own home % who own home

We could repeat a second scatterplot

just to label DC, but this is a bit

cumbersome.

Uses allstatesn.dta & scheme vg teal

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.10 Adding text to markers and positions 301

twoway scatter ownhome borninstate, text(43 40 "DC")

Instead, we can use the text() option

to add text to our graph. Looking at

the values of ownhome and borninstate

for DC, we see that their values are

about 43 and 40, respectively. We use

these as coordinates to label the point,

but the text() option places the label

at the center of the speciﬁed yx

coordinate, sitting right over the point.

Uses allstatesn.dta & scheme vg teal DC

% who own home

40 50 60 70 80

% born in state of residence

twoway scatter ownhome borninstate, text(43 40 "DC", placement(ne))

Adding the placement(ne) option

places the label above and to the right

(northeast) of the point. Other options

you could choose include n,ne,e,se,s,

sw,w,nw,andc(center); see

Styles : Compassdir (331) for more

details.

Uses allstatesn.dta & scheme vg teal

% who own home

40 50 60 70 80

% born in state of residence

twoway (scatter ownhome borninstate, text(43 40 "DC", placement(e)))

(lfit ownhome borninstate) (lfit ownhome borninstate if stateab !="DC")

Consider this scatterplot showing a

linear ﬁt between the two variables: one

including Washington, DC, and one

omitting Washington, DC. See the next

graph, which uses the text() option to

label the graph instead of the legend.

Uses allstatesn.dta & scheme vg teal

40 50 60 70 80

% born in state of residence

% who own home Fitted values

Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

302 Chapter 8. Options available for most graphs

twoway (scatter ownhome borninstate, text(43 40 "DC", placement(ne)))

(lfit ownhome borninstate) (lfit ownhome borninstate if stateab !="DC",

text(72 50 "Without DC") text(60 50 "With DC")), legend(off)

Without DC

With DC

40 50 60 70 80

% born in state of residence

This graph turns the legend oﬀ and

uses the text() option to label each

regression line to indicate which

regression line includes DC and which

excludes DC.

Uses allstatesn.dta & scheme vg teal

twoway (scatter ownhome borninstate, text(43 40 "DC", placement(ne)))

(lfit ownhome borninstate) (lfit ownhome borninstate if stateab !="DC",

text(71 50 "Without DC") text(60 50 "With DC")

text(50 70 "Coef with DC .16" "Coef without DC .44")), legend(off)

Without DC

With DC

Coef with DC .16

Coef without DC .44

40 50 60 70 80

% born in state of residence

This graph adds explanatory text

showing the regression coeﬃcient with

and without DC.

Uses allstatesn.dta & scheme vg teal

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.11 More options for text and textboxes 303

twoway (scatter ownhome propval100, xaxis(1) mlabel(stateab))

(scatter ownhome borninstate, xaxis(2) mlabel(stateab))

Consider this graph in which we overlay

two scatterplots. We place propval100

on the ﬁrst x-axis and borninstate on

the second x-axis.

Uses allstatesn.dta & scheme vg teal CT

NE NH NJ

ND OH

SD VT

WI CT

IN IA

MONE

NH NJ

VT WI

40 50 60 70 80

% born in state of residence

0 20 40 60 80 100

% homes cost $100K+

% who own home % who own home

twoway (scatter ownhome propval100, xaxis(1))

(scatter ownhome borninstate, xaxis(2)),

text(43 66 "DC") text(43 42 "DC", xaxis(2))

Rather than labeling all the points, we

can label just the point for DC.We

have to be very careful because we have

two diﬀerent x-axes. The ﬁrst text()

option uses the ﬁrst x-axis, so no

special option is required. The second

text() option uses the second x-axis,

so we must specify the xaxis(2)

option.

Uses allstatesn.dta & scheme vg teal

DCDC

40 50 60 70 80

% born in state of residence

0 20 40 60 80 100

% homes cost $100K+

% who own home % who own home

8.11 More options for text and textboxes

This section describes more options for modifying textbox elements: titles, captions,

notes, added text, and legends. Technically, all text in a graph is displayed within a textbox.

We can modify the box’s attributes, such as its size and color, the margin around the box,

and the outline; and we can modify the attributes of the text within the box, such as its

size, color, justiﬁcation, and margin. We sometimes use the box option to see how both

the textbox and its text are being displayed. This helps us to see if we should modify the

attributes of the box containing the text or the text within the box. For more information,

see [G]textbox options and Options : Adding text (299). In this section, we will begin by

showing examples illustrating how to control the placement, size, color, and orientation of

text. We will begin this section using the vg s1m scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

304 Chapter 8. Options available for most graphs

twoway scatter ownhome borninstate,

text(43 40 "Washington, DC", placement(ne))

Washington, DC

40 50 60 70 80

% who own home

40 50 60 70 80

% born in state of residence

Consider this scatterplot, which has a

dramatic outlying point. We have used

the text() option to label that point,

but, perhaps, we might want to control

the size of the text for this label. See

the next example for an illustration of

howtodothis.

Uses allstatesn.dta & scheme vg s1m

twoway scatter ownhome borninstate,

text(43 40 "Washington, DC", placement(ne) size(vlarge))

Washington, DC

40 50 60 70 80

% who own home

40 50 60 70 80

% born in state of residence

We can alter the size of the text using

the size() option. Here, we make the

text large. Other values we could use

with the size() option include zero,

miniscule,quarter tiny,third tiny,

half tiny,tiny,vsmall,small,

medsmall,medium,medlarge,large,

vlarge,huge,andvhuge;see

Styles : Textsize (344) for more details.

Uses allstatesn.dta & scheme vg s1m

twoway scatter ownhome borninstate,

text(43 40 "Washington, DC", placement(ne) color(gs9))

Washington, DC

40 50 60 70 80

% who own home

40 50 60 70 80

% born in state of residence

We can alter the color of the text using

the color() option. Here, we make the

text a middle-level gray. See

Styles : Colors (328) for other colors you

could select.

Uses allstatesn.dta & scheme vg s1m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.11 More options for text and textboxes 305

twoway scatter ownhome borninstate,

text(43 40 "Washington, DC", placement(ne) orientation(vertical))

We can use the orientation() option

to change the direction of the text.

Other values you can choose are

horizontal for 0 degrees, vertical for

90 degrees, rhorizontal for 180

degrees, and rvertical for 270

degrees, see Styles : Orientation (341) for

more details.

Uses allstatesn.dta & scheme vg s1m

Washington, DC

40 50 60 70 80

% who own home

40 50 60 70 80

% born in state of residence

This next set of examples considers options for justifying text within a box, sizing the

box, and creating margins around the box. This is followed by options that control margins

within the textbox. This next set of graphs use the vg rose scheme

twoway (scatter ownhome borninstate),

title("% who own home by" "% that reside in state of birth", box)

Consider this example where we place a

title on our graph. To help show how

the options work, we will put a box

around the title.

Uses allstatesn.dta & scheme vg rose

% who own home

40 50 60 70 80

% born in state of residence

% who own home by

% that reside in state of birth

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

306 Chapter 8. Options available for most graphs

twoway (scatter ownhome borninstate),

title("% who own home by" "% that reside in state of birth", box

justification(left))

% who own home

40 50 60 70 80

% born in state of residence

% who own home by

% that reside in state of birth

We can left-justify the text using the

justification() option. Note that

the title is justiﬁed within the textbox,

not with respect to the entire graph

area.

Uses allstatesn.dta & scheme vg rose

twoway (scatter ownhome borninstate),

title("% who own home by" "% that reside in state of birth", box

bexpand)

% who own home

40 50 60 70 80

% born in state of residence

% who own home by

% that reside in state of birth

If we use the bexpand (box expand)

option, the textbox containing the title

expands to ﬁll the width of the plot

area.

Uses allstatesn.dta & scheme vg rose

twoway (scatter ownhome borninstate),

title("% who own home by" "% that reside in state of birth", box

bexpand justification(left))

% who own home

40 50 60 70 80

% born in state of residence

% who own home by

% that reside in state of birth

With the box expanded, the

justification(left) option now

makes the title ﬂush left with the plot

area.

Uses allstatesn.dta & scheme vg rose

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.11 More options for text and textboxes 307

twoway (scatter ownhome borninstate),

title("% who own home by" "% that reside in state of birth",

box bexpand justification(left) bmargin(medium))

We can change the size of the margin

around the outside of the box using the

bmargin(medium) (box margin) option,

making the margin a medium size at all

four edges: left, right, top, and bottom.

Uses allstatesn.dta & scheme vg rose

% who own home

40 50 60 70 80

% born in state of residence

% who own home by

% that reside in state of birth

twoway (scatter ownhome borninstate),

title("% who own home by" "% that reside in state of birth",

box bexpand justification(left) bmargin(0 0 3 3))

If we wanted the margin for the left and

right to be 0 and for the top and

bottom to be 3, we could use the

bmargin(0 0 3 3) option. The order

of the margins is bmargin(#left #right

#top #bottom).

Uses allstatesn.dta & scheme vg rose

% who own home

40 50 60 70 80

% born in state of residence

% who own home by

% that reside in state of birth

twoway (scatter ownhome borninstate),

title("% who own home by" "% that reside in state of birth",

box bexpand justification(left) bmargin(b=3))

To make only the bottom margin 3, we

could specify bmargin(b=3), where b=3

means to change the bottom margin to

3. The top, left, bottom, and top

margins can be changed individually

using t=,l=,b=,andt=, respectively.

Uses allstatesn.dta & scheme vg rose

% who own home

40 50 60 70 80

% born in state of residence

% who own home by

% that reside in state of birth

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

308 Chapter 8. Options available for most graphs

twoway (scatter ownhome borninstate) (lfit ownhome borninstate)

(lfit ownhome borninstate if stateab !="DC",

text(45 70 "Coef with DC .16" "Coef without DC .44", box))

Coef with DC .16

Coef without DC .44

40 50 60 70 80

% born in state of residence

% who own home Fitted values

Fitted values

Consider this graph, which uses the

text() option to place an annotation

in the middle of the plot region. The

text might look better if we increased

the margin around the text.

Uses allstatesn.dta & scheme vg rose

twoway (scatter ownhome borninstate) (lfit ownhome borninstate)

(lfit ownhome borninstate if stateab !="DC",

text(45 70 "Coef with DC .16" "Coef without DC .44", box margin(medium)))

Coef with DC .16

Coef without DC .44

40 50 60 70 80

% born in state of residence

% who own home Fitted values

Fitted values

We can expand the margin between the

text and the box with the margin()

option. Note the diﬀerence between this

and the bmargin() option (illustrated

previously), which increased the margin

around the outside of the box.

Uses allstatesn.dta & scheme vg rose

twoway (scatter ownhome borninstate) (lfit ownhome borninstate)

(lfit ownhome borninstate if stateab !="DC",

text(45 70 "Coef with DC .16" "Coef w/out DC .44", box margin(5 5 2 2)))

Coef with DC .16

Coef w/out DC .44

40 50 60 70 80

% born in state of residence

% who own home Fitted values

Fitted values

As with the bmargin() option, we can

more precisely modify the margin

around the text. Here, we use the

margin() option to make the size of the

margin 5, 5, 2, and 2 for the left, right,

top, and bottom, respectively.

Uses allstatesn.dta & scheme vg rose

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.11 More options for text and textboxes 309

twoway (scatter ownhome borninstate) (lfit ownhome borninstate)

(lfit ownhome borninstate if stateab !="DC",

text(45 70 "Coef with DC .16" "Coef without DC .44", box linegap(4)))

We can change the gap between the

lines with the linegap() option. Here,

we make the gap larger than it

normally would be. See Styles : Margins

(338) for more details.

Uses allstatesn.dta & scheme vg rose

Coef with DC .16

Coef without DC .44

40 50 60 70 80

% born in state of residence

% who own home Fitted values

Fitted values

Let’s now consider options that control the color of the textbox and the characteristics

of the outline of the box (including the color, thickness, and pattern). This next set of

graphs uses the vg past scheme.

twoway (scatter ownhome borninstate),

title("% own home by % reside in state")

Consider this graph with a title at the

top.

Uses allstatesn.dta & scheme vg past

40 50 60 70 80

% who own home

40 50 60 70 80

% born in state of residence

% own home by % reside in state

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

310 Chapter 8. Options available for most graphs

twoway (scatter ownhome borninstate),

title("% own home by % reside in state", box)

40 50 60 70 80

% who own home

40 50 60 70 80

% born in state of residence

% own home by % reside in state We can add the box option now for

aesthetic purposes.

Uses allstatesn.dta & scheme vg past

twoway (scatter ownhome borninstate),

title("% own home by % reside in state",

box bfcolor(ltblue) blcolor(gray) blwidth(thick))

40 50 60 70 80

% who own home

40 50 60 70 80

% born in state of residence

% own home by % reside in state We can change the box ﬁll color with

the bfcolor() option, the color of the

line around the box with blcolor(),

and the width of the surrounding box

line with blwidth(). See Styles : Colors

(328) for other possible values you

could use with the bfcolor() and

blcolor() options and

Styles : Linewidth (337) for other values

you could choose for blwidth().

Uses allstatesn.dta & scheme vg past

twoway (scatter ownhome borninstate),

title("% own home by % reside in state",

box bcolor(gold))

40 50 60 70 80

% who own home

40 50 60 70 80

% born in state of residence

% own home by % reside in state We can change the box color with the

bcolor() option. Here, we make the ﬁll

and outline color of the title box gold.

Uses allstatesn.dta & scheme vg past

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

8.11 More options for text and textboxes 311

Let’s now use the allstates ﬁle and consider some examples in which we use the by()

option to display multiple graphs broken down by the location of the state. We will look at

options for placing and aligning text in graphs that use the by() option. This next set of

graphs uses the vg s2c scheme.

scatter ownhome borninstate,

by(nsw, title("% own home" "by % born in state"))

Consider this graph in which we use the

by() option to show this scatterplot

separately for states in the North,

South, and West.

Uses allstates.dta & scheme vg s2c

50 60 70 8050 60 70 80

20 40 60 80

North South

West

% who own home

% born in state of residence

Graphs by Region North, South, or West

% own home

by % born in state

scatter ownhome borninstate,

by(nsw, title("% own home" "by % born in state",

ring(0) position(5) box))

Let’s put the title in the open hole in

the right corner of the graph using the

ring(0) and position(5) options. We

include the box option only to show the

outline of the textbox, not for

aesthetics.

Uses allstates.dta & scheme vg s2c

50 60 70 8050 60 70 80

20 40 60 80

North South

West

% who own home

% born in state of residence

Graphs by Region North, South, or West

% own home

by % born in state

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Markers Marker labels Connecting Axis titles Axis labels Axis scales Axis selection By Legend Adding text Textboxes

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

312 Chapter 8. Options available for most graphs

scatter ownhome borninstate,

by(nsw, title("% own home" "by % born in state",

ring(0) position(5) box width(65) height(40)))

50 60 70 8050 60 70 80

20 40 60 80

North South

West

% who own home

% born in state of residence

Graphs by Region North, South, or West

% own home

by % born in state

We can make the area for the textbox

bigger using the width() and height()

options. We change the value to make

the box approximately as tall as the

graph for the West and as wide as the

graph for the South.

Uses allstates.dta & scheme vg s2c

scatter ownhome borninstate,

by(nsw, title("% own home" "by % born in state", ring(0) position(5)

box width(65) height(40) justification(left) alignment(top)))

50 60 70 8050 60 70 80

20 40 60 80

North South

West

% who own home

% born in state of residence

Graphs by Region North, South, or West

% own home

by % born in state

We can left-justify the text and align it

with the top using the

justification(left) and

alignment(top) options. These

options make the title appear in the top

left corner of the empty hole.

Uses allstates.dta & scheme vg s2c

scatter ownhome borninstate,

by(nsw, title("% own home" "by % born in state", ring(0) position(5)

width(65) height(40) justification(left) alignment(top)))

50 60 70 8050 60 70 80

20 40 60 80

North South

West

% who own home

% born in state of residence

Graphs by Region North, South, or West

% own home

by % born in state

Now that we have aligned the text as

we would like, we can take away the

box by omitting the box option.

Uses allstates.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

9 Standard options available for all

graphs

This chapter discusses a class of options Stata refers to as standard options, because

these options can be used in most graphs. This chapter will begin by discussing options

that allow you to add or change the titles in the graph and then showing you how to use

schemes to control the overall look and style of your graph. Next, we demonstrate options

for controlling the size of the graph and the scale of items within graphs. The chapter

will conclude by illustrating options that allow you to control the colors of the plot region,

the graph region, and the borders that surround these regions. For further details, see

[G]std options.

9.1 Creating and controlling titles

Titles are useful for providing additional information that explains the contents of a

graph. Stata includes four standard options for adding explanatory text to graphs: title(),

subtitle(),note(),andcaption(). This section will illustrate how to use these ti-

tles and how to customize their content and their placement. For further information

about customizing the appearance of such titles (e.g., color, size, orientation, etc.), see

Options : Textboxes (303). For more information about titles, see [G]title options. This

section uses the vg s1m scheme.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Titles Schemes Sizing graphs Graph regions

313

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

314 Chapter 9. Standard options available for all graphs

scatter propval100 ownhome, title("My title")

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title The title() option adds a title to a

graph. Here, we add a simple title to

the graph. Although the title includes

quotes, we could have omitted them in

this case. Later, we will see examples

where the quotes become very

important.

Uses allstates.dta & scheme vg s1m

scatter propval100 ownhome, title("My title") subtitle("My subtitle")

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My subtitle

My title The subtitle() option adds a subtitle

to a graph. The subtitle, by default,

appears below the title in a smaller

font.

Uses allstates.dta & scheme vg s1m

scatter propval100 ownhome, subtitle("My smaller title")

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My smaller title We do not have to specify a title() to

specify a subtitle(). For example, we

might want a title that is smaller in size

than a regular title, so we could specify

a subtitle alone.

Uses allstates.dta & scheme vg s1m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

9.1 Creating and controlling titles 315

scatter propval100 ownhome, caption("My caption") note("My note")

In this example, the caption() option

adds a small-sized caption in the lower

corner, and the note() option places a

smaller-sized note in the bottom left

corner. If both options are speciﬁed,

the note appears above the caption. We

do not need to include both of these

options in the same graph.

Uses allstates.dta & scheme vg s1m

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My note

My caption

scatter propval100 ownhome, t1title("My t1title") t2title("My t2title")

b1title("My b1title") b2title("My b2title") l1title("My l1title")

l2title("My l2title") r1title("My r1title") r2title("My r2title")

Although these are not as commonly

used, Stata oﬀers a number of

additional title options for titling the

top of the graph (t1title() and

t2title()), the bottom of the graph

(b1title() and b2title()), the left

side of the graph (l1title() and

l2title()), and the right side of the

graph (r1title() and r2title()).

Uses allstates.dta & scheme vg s1m

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My r1title

My r2title

My l1title

My l2title

My t1title

My t2title

My b1title

My b2title

Stata gives you considerable ﬂexibility in the placement of these titles, notes, and cap-

tions, as well as controlling the size, color, and orientation of the text. This is illustrated

below using the title() option, but the same options apply equally to the subtitle(),

note(),andcaption() options.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Titles Schemes Sizing graphs Graph regions

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

316 Chapter 9. Standard options available for all graphs

scatter propval100 ownhome, title("My" "title")

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

title

In this example, we use multiple sets of

quotes in the title() option to tell

Stata that we want the title to appear

on two separate lines.

Uses allstates.dta & scheme vg s1m

scatter propval100 ownhome, title(‘"A "title" with quotes"’)

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

A "title" with quotes This example illustrates that we can

have quotation marks in the title()

option, as long as we open the title

with ‘" and close it with "’. (The open

single quote is often located below the

tilde on your keyboard, and the close

single quote is often located below the

double quote on your keyboard.)

Uses allstates.dta & scheme vg s1m

scatter propval100 ownhome, title("My title", position(7))

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title

The position() option can be used to

change the position of the title. Here,

we place the title in the bottom left

corner of the graph by indicating that it

should be at the 7 o’clock position. See

Styles : Clockpos (330) for more details.

Uses allstates.dta & scheme vg s1m

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

9.1 Creating and controlling titles 317

scatter propval100 ownhome, title("My title", position(1) ring(0))

As we saw in the last example, we can

use the position() option to control

the placement of the title, but this

option does not control the distance

between the title and center of the plot

region. That is controlled by the

ring() option. ring(0) means that

the item is inside the plot region, and

higher values for ring() place the item

farther away from the plot region.

Imagine concentric rings around the

plot area with higher values

corresponding to the rings that are

farther from the center.

Uses allstates.dta & scheme vg s1m

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title

scatter propval100 ownhome, title("This is my" "title",

position(11) box)

Because titles, subtitles, notes, and

captions are considered textboxes, you

can use the options associated with

textboxes to customize their display.

Here, we place a box around the title

using the box option. We also use the

position(11) option to place the title

in the 11 o’clock position.

Uses allstates.dta & scheme vg s1m

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

This is my

title

scatter propval100 ownhome, title("This is my" "title",

position(11) box span)

Here, we add the span option, so the

title spans the width of the graph,

positioning the title ﬂush left at the 11

o’clock position. Note that now the

title partly obscures the 100 labeling

the y-axis.

Uses allstates.dta & scheme vg s1m

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

This is my

title

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Titles Schemes Sizing graphs Graph regions

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

318 Chapter 9. Standard options available for all graphs

scatter propval100 ownhome, title("This is my" "title", box

justification(right))

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

This is my

title

We can use the justification(right)

option to right-justify the text inside

the box. Note the diﬀerence between

the position() option, which positions

the textbox, and the justification()

option, which justiﬁes the text within

the textbox.

Uses allstates.dta & scheme vg s1m

scatter propval100 ownhome, title("This is my" "title", box bexpand)

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

This is my

title

We can expand the box to ﬁll the width

of the plot region using the bexpand

option. If we wanted the box to span

the entire width of the graph, we could

add the span option (not shown).

There are numerous other textbox

options than can be used with titles;

see Options : Textboxes (303) and

[G]textbox options for more details.

Uses allstates.dta & scheme vg s1m

9.2 Using schemes to control the look of graphs

Schemes control the overall look of Stata graphs by providing default values for numer-

ous graph options. You can accept these defaults or override them using graph options. This

section ﬁrst examines the kinds of schemes available in Stata, discuss diﬀerent methods for

selecting schemes, and then show how to obtain additional schemes. For more information

about schemes, see [G]schemes. Stata has two basic families of schemes, the s2 family

and the s1 family, each sharing similar characteristics. There are also other specialized

schemes, including the sj scheme for making graphs like those in the Stata Journal and the

economist scheme for making graphs like those that appear in The Economist. We will

look at these schemes below.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

9.2 Using schemes to control the look of graphs 319

twoway (scatter propval100 urban) (scatter rent700 urban)

(lfit propval100 urban) (lfit rent700 urban), scheme(s2color)

This example uses the

scheme(s2color) option to create a

graph using the s2color scheme. Using

the scheme() option, we can manually

select which scheme to use for

displaying the graph we wish to create.

The s2color scheme is the default

scheme for Stata graphs.

Uses allstates.dta & scheme s2color

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

Fitted values Fitted values

twoway (scatter propval100 urban) (scatter rent700 urban)

(lfit propval100 urban) (lfit rent700 urban), scheme(s2mono)

The s2mono scheme is a

black-and-white version of the s2color

scheme. In this example, the symbols

diﬀer in gray scale and size, and the

lines diﬀer in their patterns.

Uses allstates.dta & scheme s2mono

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

Fitted values Fitted values

twoway (scatter propval100 urban) (scatter rent700 urban)

(lfit propval100 urban) (lfit rent700 urban), scheme(s2manual)

Here is an example using the s2manual

scheme, which is very similar to the

s2mono scheme. One diﬀerence is that

the lines of the ﬁt values are the same

pattern (solid) in this graph, but they

have diﬀerent patterns when we use

s2mono.

Uses allstates.dta & scheme s2manual

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

Fitted values Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Titles Schemes Sizing graphs Graph regions

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

320 Chapter 9. Standard options available for all graphs

twoway (scatter propval100 urban) (scatter rent700 urban)

(lfit propval100 urban) (lfit rent700 urban), scheme(s1color)

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

Fitted values Fitted values

This is an example of a graph using the

s1color scheme. Note how the lines

and markers are only diﬀerentiated by

their color. Both the plot area and the

border around the plot are white. Also,

note the absence of grid lines. (Stata

also has an s1rcolor scheme, in which

the plot area and border area are black.

This is not shown since it would be

diﬃcult to read in print.)

Uses allstates.dta & scheme s1color

twoway (scatter propval100 urban) (scatter rent700 urban)

(lfit propval100 urban) (lfit rent700 urban), scheme(s1mono)

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

Fitted values Fitted values

The s1mono scheme is similar to the

s1color scheme in that the plot area

and border are white and the grid is

omitted. In a mono scheme, the

markers diﬀer in gray scale and size,

and the lines diﬀer in their pattern.

Uses allstates.dta & scheme s1mono

twoway (scatter propval100 urban) (scatter rent700 urban)

(lfit propval100 urban) (lfit rent700 urban), scheme(s1manual)

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

Fitted values Fitted values

The s1manual is similar to s1mono, but

the sizes of the markers and text are

increased. This is useful if you are

making a small graph and want these

small elements to be magniﬁed to be

more easily seen.

Uses allstates.dta & scheme s1manual

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

9.2 Using schemes to control the look of graphs 321

twoway (scatter propval100 urban) (scatter rent700 urban)

(lfit propval100 urban) (lfit rent700 urban), scheme(sj)

The sj scheme is very similar to the

s2mono scheme. In fact, a comparison

of this graph with an earlier graph that

used the s2mono scheme shows no

visible diﬀerences. The sj scheme is

basedonthes2mono scheme and only

alters xsize() and ysize(). See

Appendix : Customizing schemes (379) for

more information about how to inspect

(and alter) the contents of graph

schemes.

Uses allstates.dta & scheme sj

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

Fitted values Fitted values

twoway (scatter propval100 urban) (scatter rent700 urban)

(lfit propval100 urban) (lfit rent700 urban), scheme(economist)

The economist scheme is quite

diﬀerent from all the other schemes and

is a very good example of how much

can be controlled with a scheme. Using

this scheme modiﬁes the colors of the

plot area, border, markers, lines, the

position of the y-axis, and the legend.

It also removes the line on the y-axis

and changes the angle of the labels on

the y-axis.

Uses allstates.dta & scheme economist 0

100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo Fitted values

Fitted values

As these examples have shown, we can change the scheme of a graph by supplying the

scheme() option on a graph command. If we want to use the same scheme over and over,

we can use the set scheme command to set the default scheme. For example, if we typed

. set scheme economist

the default scheme would become economist until we quit Stata. Or, we could type

. set scheme economist, permanently

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Titles Schemes Sizing graphs Graph regions

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

322 Chapter 9. Standard options available for all graphs

The economist scheme would be our default scheme, even after we quit and start Stata

again. If we will be creating a series of graphs that we want to have a common look, then

schemes are a very powerful tool for accomplishing this. Even though Stata has a variety

of built-in schemes, we may want to obtain other schemes. The findit command can be

used to search for information about schemes and to download schemes that others have

developed. To search for schemes, type

. findit scheme

and Stata will list web pages and packages associated with the word scheme.

See Intro : Schemes (14) for an overview of the schemes used in this book and Appendix : Online sup-

plements (382) for instructions for obtaining the schemes for this book.

Seeing how powerful and ﬂexible schemes are, we might be interested in creating our

own schemes. Stata gives us complete control over creating schemes. The section Ap-

pendix : Customizing schemes (379) provides tips for getting started.

9.3 Sizing graphs and their elements

This section illustrates how to use the xsize() and ysize() options to control the size

and aspect ratio of graphs. It also illustrates the use of the scale() option for controlling

the size of the text and markers. This section uses the vg s1c scheme.

scatter propval100 ownhome

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

Let’s ﬁrst consider this graph. The

graphs in this book have been sized to

be 3 inches wide by 2 inches tall.

Although we do not see it, some graphs

are sized via an xsize() and ysize()

option, and some are sized via schemes.

Uses allstates.dta & scheme vg s1c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

9.3 Sizing graphs and their elements 323

scatter propval100 ownhome, xsize(3) ysize(1)

Here, we make a graph to illustrate how

to use xsize() and ysize() to control

the aspect ratio of the graph, as well as

the size. Note that when we do this,

the size of the graph will not change on

the screen but the aspect ratio will.

Although we can size the graph on the

screen, when we export the graph, it

will have both the size and aspect ratio

we chose using xsize() and ysize().

Uses allstates.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

scatter propval100 ownhome, xsize(2) ysize(2)

Here, we make just one more graph to illustrate that we

can use xsize() and ysize() to control the aspect

ratio of the graph, as well as the size. Here, we make

the graph square by making the graph 2 inches high by

2 inches tall.

Uses allstates.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

scatter propval100 ownhome, scale(1.7)

In this example, we add the

scale(1.7) option to magnify the sizes

of the text and markers in the graph,

making them 1.7 times their normal

sizes. This can be useful when we make

small graphs and want to increase the

sizes of the text and markers to make

them easier to see.

Uses allstates.dta & scheme vg s1c

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Titles Schemes Sizing graphs Graph regions

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

324 Chapter 9. Standard options available for all graphs

scatter propval100 ownhome, scale(.5)

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

We can also use the scale() option to

decrease the size of the text and

markers. Here, we make the size of

these elements half their normal size.

Uses allstates.dta & scheme vg s1c

9.4 Changing the look of graph regions

This section discusses the region options that can be controlled via the plotregion()

and graphregion() options. These allow we to control the color of the plot region and

graph region, as well as the lines that border these regions. For more information, see

[G]region options. This section uses the vg s2c scheme.

scatter propval100 ownhome, title("My title")

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title Consider this scatterplot. In general,

Stata sees this graph as having two

overall regions. The area inside the x-

and y-axes where the data are plotted

is called the plot region. In this graph,

the plot region is white. The area

surrounding the plot region, where the

axes and titles are placed, is called the

graph region. In this graph, the graph

region is shaded light blue.

Uses allstates.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

9.4 Changing the look of graph regions 325

scatter propval100 ownhome, title("My title") plotregion(color(stone))

Here, we use

plotregion(color(stone)) to make

the color of the plot region stone. The

color() option controls the color of the

plot region.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title

scatter propval100 ownhome, title("My title") plotregion(lcolor(navy)

lwidth(thick) )

In this graph, we put a thick, navy blue

line around the plot region using the

lcolor() and lwidth() options. This

puts a bit of a frame around the plot

region.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title

scatter propval100 ownhome, title("My title") graphregion(color(erose))

Here, we use the

graphregion(color(erose)) option to

modify the color of the graph region to

be erose, a light rose color. The graph

region is the area outside of the plot

region where the titles and axes are

displayed.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Titles Schemes Sizing graphs Graph regions

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

326 Chapter 9. Standard options available for all graphs

scatter propval100 ownhome, title("My title")

graphregion(ifcolor(erose) fcolor(maroon))

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title The graph region is actually composed

of an inner part and an outer part.

Here, we use the ifcolor(erose)

option to make the inner graph region

light rose and the fcolor(maroon)

option to make the outer graph region

maroon. This has the eﬀect of putting a

maroon frame around the entire graph.

Uses allstates.dta & scheme vg s2c

scatter propval100 ownhome, title("My title")

graphregion(lcolor(navy) lwidth(vthick))

0 20 40 60 80 100

% homes cost $100K+

40 50 60 70 80

% who own home

My title We can put a somewhat diﬀerent frame

around the graph by altering the size

and color of the line that surrounds the

graph region. Using the lcolor(navy)

lwidth(vthick) options gives this

graph a very thick, navy blue border.

Uses allstates.dta & scheme vg s2c

This section omitted numerous options that we could use to control the plot region and

graph region, including further control of the inner and outer regions and further control of

the lines that surround these regions. Stata gives us more control than we generally need,

so rather than covering these options here, I refer you to [G]region options.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10 Styles for changing the look of

graphs

This section focuses on frequently used styles that arise in making graphs, such as

linepatternstyle,linewidthstyle,ormarkerstyle. The styles are covered in alphabetical order,

providing more details about the values you can choose. Each section refers to the appro-

priate section of [G]graph to provide complete details on each style. We begin by using

the allstates ﬁle and omitting Washington, DC.

10.1 Angles

An anglestyle speciﬁes the angle for displaying an item (or group of items) in the graph.

Common examples include specifying the angle for marker labels with mlabangle() or the

angle of the labels on the y-axis with ylabel(, angle()). We can specify an anglestyle as

a number of degrees of rotation (negative values are permitted, so for example, −90 can be

used instead of 270). We can also use the keywords horizontal for 0 degrees, vertical

for 90 degrees, rhorizontal for 180 degrees, and rvertical for 270 degrees. See [G]an-

glestyle for more information.

scatter workers2 faminc, mlabel(stateab) mlabangle(45)

Here, we use the mlabangle(45)

(marker label angle) to change the angle

of the marker labels to 45 degrees.

Uses allstatesdc.dta & scheme vg s2c

NC ND

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

327

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

328 Chapter 10. Styles for changing the look of graphs

scatter workers2 faminc, ylabel(, angle(0))

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Here, we change the angle of the labels

of the y-axis so that they read

horizontally by using the angle(0)

option. We could also have used

horizontal to obtain the same eﬀect.

Uses allstatesdc.dta & scheme vg s2c

scatter workers2 faminc, xlabel(15000(1000)30000, angle(45))

45 50 55 60 65 70

% HHs with 2+ workers

15000

16000

17000

18000

19000

20000

21000

22000

23000

24000

25000

26000

27000

28000

29000

30000

1979 Median Family Inc.

In this example, we label the x-axis

from 15,000 to 30,000 incremented by

1,000. When we have so many labels,

we can use the angle(45) option to

display the labels at a 45-degree angle.

Uses allstatesdc.dta & scheme vg s2c

10.2 Colors

Acolorstyle allows us to modify the color of an object, be it a title, a marker, a marker

label, a line around a box, a ﬁll color of a box, or practically any other object in graphs.

The two main ways to specify a color are either by giving a name of color (e.g., red,pink,

teal) or by supplying an RGB value giving the amount of red, green, and blue to be mixed

to form a custom color. See [G]colorstyle for more information.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10.2 Colors 329

scatter workers2 faminc, mcolor(gs8)

The mcolor() (marker color) option is

used here to make the marker a middle

gray. Stata provides 17 levels of gray

named gs0 to gs16. The darkest is gs0

(a synonym for black), and the lightest

is gs16 (a synonym for white).

Uses allstatesdc.dta & scheme vg s2c

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

scatter workers2 faminc, mcolor(lavender)

Here, we use the mcolor(lavender)

option to make the markers lavender,

one of the predeﬁned colors created by

Stata. The next example illustrates

more of the colors from which you can

choose.

Uses allstatesdc.dta & scheme vg s2c

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

vgcolormap, quietly

This vgcolormap command is a

command that I wrote to show the

diﬀerent standard colors available in

Stata all at once. We simply issue the

command vgcolormap, and it creates a

scatterplot that shows the colors we can

choose from and their names. See the

list of colors available in [G]colorstyle,

and see how to get vgcolormap in

Appendix : Online supplements (382).

Uses allstatesdc.dta & scheme s2color

black gs0 gs1 gs2 gs3 gs4

gs5 gs6 gs7 gs8 gs9 gs10

gs11 gs12 gs13 gs14 gs15 gs16

white blue bluishgray brown cranberry cyan

dimgray dkgreen dknavy dkorange eggshell emerald

forest_green gold gray green khaki lavender

lime ltblue ltbluishgray ltkhaki magenta maroon

midblue midgreen mint navy olive olive_teal

orange orange_red pink purple red sand

sandb sienna stone teal yellow ebg

ebblue edkblue eltblue eltgreen emidblue erose

Color Map of Standard Stata Colors

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

330 Chapter 10. Styles for changing the look of graphs

scatter workers2 faminc, mcolor("255 255 0")

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Despite all the standard color choices,

we may want to mix our own colors by

specifying how much red, green, and

blue that we want mixed together. We

can mix between 0 and 255 units of

each color. Mixing 0 units of each

yields black, and 255 units of each

yields white. Here, we mix 255 units of

red, 255 units of green, and 0 units of

blue to get a shade of yellow.

Uses allstatesdc.dta & scheme vg s2c

scatter workers2 faminc, mcolor("255 150 100")

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

By mixing 255 parts red, 150 parts

green, and 100 parts blue, we get a

peach color. Since colors for web pages

use this same principle of mixing red,

green, and blue, we can do a web search

using terms like color mixing html and

ﬁnd numerous web pages to help us ﬁnd

the right mixture for the colors that we

want to make.

Uses allstatesdc.dta & scheme vg s2c

10.3 Clock position

A clock position refers to a location using the numbers on an analog clock to indicate

the location, with 12 o’clock being above the center, 3 o’clock to the right, 6 o’clock below

the center, and 9 o’clock to the left. A value of 0 refers to the center but may not always

be valid. See [G]clockpos for more information.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10.4 Compass direction 331

scatter workers2 faminc, mlabel(stateab) mlabposition(5)

In this example, we add marker labels

to a scatterplot and use the

mlabposition(5) (marker label

position) option to place the marker

labels in the 5 o’clock position with

respect to the markers.

Uses allstatesdc.dta & scheme vg s2c AL

ID IL

NC ND

TN TX

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

scatter workers2 faminc, mlabel(stateab) mlabposition(0) msymbol(i)

In this example, we place the markers

in the center position using the

mlabposition(0) option. We also

make the symbols invisible using the

msymbol(i) option. Otherwise, the

markers and marker labels would be

atop each other.

Uses allstatesdc.dta & scheme vg s2c

ID IL

NC ND

TN TX

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

10.4 Compass direction

Acompassdirstyle is much like clockpos, but where a clockpos has 12 possible outer po-

sitions, like a clock, the compassdirstyle has only 9 possible outer positions, like the major

labels on a compass: north,neast,east,seast,south,swest,west,nwest,andcenter.

These can be abbreviated as n,ne,e,se,s,sw,w,nw,andc. Stata permits us to use

aclockpos even when a compassdirstyle is called for and makes intuitive translations; for

example, 12 is translated to north,or2is translated to neast. See [G]compassdirstyle

for more information.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

332 Chapter 10. Styles for changing the look of graphs

scatter workers2 faminc, title("Work Status and Income",

ring(0) placement(se))

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Work Status and Income

In this example, we use placement() to

position the title in the southeast

(bottom right corner) of the plot

region. The ring(0) option moves the

title inside the plot region.

Uses allstatesdc.dta & scheme vg s2c

scatter workers2 faminc, title("Work Status and Income",

ring(0) placement(4))

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Work Status and Income

If we instead specify the placement(4)

option (using a clockpos instead of

compassdir), Stata makes a suitable

substitution, and the title is placed in

the bottom right corner.

Uses allstatesdc.dta & scheme vg s2c

10.5 Connecting points

Stata supports a variety of methods for connecting points using diﬀerent values for the

connectstyle. These include l(lowercase L, as in line) to connect with a straight line, Lto

connect with a straight line only if the current x-value is greater than the prior x-value, J

for stairstep, stepstair for step then stair, and ifor invisible connections. For the next

few examples, let’s switch to using the spjanfeb2001 data ﬁle, keeping only the data for

January and February of 2001. See [G]connectstyle for more information.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10.5 Connecting points 333

scatter close tradeday

Here, we make a scatterplot showing

the closing price on the y-axis and the

trading day (numbered 1 to 40) on the

x-axis. Normally, we would connect

these points.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

scatter close tradeday, connect(l)

Here, we add the connect(l) option,

but this is probably not the kind of

graph we wanted to create. The

problem is that the observations are in

a random order, but the observations

are connected in the same order as they

appear in the data. We really want the

points to be connected based on the

order of tradeday.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

scatter close tradeday, connect(l) sort

To ﬁx the previous graph, we can either

ﬁrst use the sort command to sort the

data on tradeday or, as we do here, use

the sort option to tell Stata to sort the

data on tradeday before connecting the

points. We also could have speciﬁed

sort(tradeday), and it would have

had the same eﬀect.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

334 Chapter 10. Styles for changing the look of graphs

scatter close predclose tradeday

1250 1300 1350 1400

0 10 20 30 40

Trading day number

Closing price Lin. Fit close from tradeday

Say that we used the regress

command to predict close from

tradeday and generated a predicted

value called predclose. Here, we plot

the actual closing prices and the

predicted closing prices.

Uses spjanfeb2001.dta & scheme vg s2c

scatter close predclose tradeday, connect(i l) sort

msymbol(. i)

1250 1300 1350 1400

0 10 20 30 40

Trading day number

Closing price Lin. Fit close from tradeday

We use the connect(i l) option to

connect the predicted values and leave

the observed values unconnected. The i

option with close indicates that the

closing values are not connected, and

the l(letter l) option indicates that the

predclose values should be connected

with a straight line. We also add

msymbol(. i) to make the symbols

invisible for the ﬁt values.

Uses spjanfeb2001.dta & scheme vg s2c

scatter close tradeday, connect(J) sort

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

In other contexts (such as survival

analysis), we might want to connect

points using a stairstep pattern. Here,

we connect the observed closing prices

with the Joption (which can also be

speciﬁed as stairstep) to get a

stairstep eﬀect.

Uses spjanfeb2001.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10.5 Connecting points 335

scatter close tradeday, connect(stepstair) sort

In other contexts, we might want to

connect points using a stepstair

pattern. Here, we connect the observed

closing prices with the stepstair

option to get a stepstair eﬀect.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30 40

Trading day number

scatter close dom, connect(l) sort(date)

Say that we created a variable called

dom that represented the day of month

and wanted to graph the closing prices

for January and February against the

day of the month. Using the

sort(date) option combined with

connect(l), we almost get what we

want, but we get a line that swoops

back connecting January 31 to Feb 1.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30

Day of month

scatter close dom, connect(L) sort(date)

This kind of example calls for the

connect(L) option, which avoids the

line that swoops back by connecting

points with a straight line, except when

the x-value (dom) decreases (e.g., goes

from 31 to 1).

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

Closing price

0 10 20 30

Day of month

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

336 Chapter 10. Styles for changing the look of graphs

10.6 Line patterns

We can specify the pattern we want for a line in three ways. We can specify a word that

selects among a set of predeﬁned styles, including solid (solid line), dash (a dashed line),

dot (a dotted line), shortdash (short dashes), longdash (long dashes), and blank (invisi-

ble). There are also combination styles dash dot,shortdash dot,andlongdash dot.We

can also use a formula that combines the following ﬁve elements in any way that we wish:

l(letter l, solid line), (underscore, long dash), -(hyphen, medium dash), .(period, short

dash), and #(small amount of space). We could specify longdash dot or ".", and they

would be equivalent. See [G]linepatternstyle for more information.

twoway (line close tradeday, clpattern(solid) sort)

(lfit close tradeday, clpattern(dash))

(lowess close tradeday, clpattern(shortdash dot))

1250 1300 1350 1400

0 10 20 30 40

Trading day number

Closing price Fitted values

lowess close tradeday

In this example, we make a line plot

and use the clpattern() (connect line

pattern) option to obtain a solid

pattern for the observed data, a dash

for the linear ﬁt line, and a short dash

and dot line for a lowess ﬁt.

Uses spjanfeb2001.dta & scheme vg s2c

twoway (line close tradeday, clpattern("l") sort)

(lfit close tradeday, clpattern(". "))

(lowess close tradeday, clpattern("-###"))

1250 1300 1350 1400

0 10 20 30 40

Trading day number

Closing price Fitted values

lowess close tradeday

We can use the clpattern() option

specifying a formula to indicate the

pattern for the lines. Here, we specify a

solid line for the line plot, a dot and

dash for the lfit plot, and a dash and

three spaces for the lowess ﬁt.

Uses spjanfeb2001.dta & scheme vg s2c

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10.7 Line width 337

twoway (line close tradeday, clpattern("l") sort)

(lfit close tradeday, clpattern("##"))

(lowess close tradeday, clpattern("-.#"))

This example shows other formulas we

could create, including "##",which

yields long dashes with short and then

long breaks in the middle, and "-.#",

which yields a short dash, a dot, and a

space. Using these formulas, we can

create a wide variety of line patterns for

those instances where we need to

diﬀerentiate multiple lines.

Uses spjanfeb2001.dta & scheme vg s2c

1250 1300 1350 1400

0 10 20 30 40

Trading day number

Closing price Fitted values

lowess close tradeday

palette linepalette

We can use the built-in Stata command

palette linepalette to view a

variety of line patterns that are

available within Stata to help us choose

a pattern to our liking.

Uses spjanfeb2001.dta & scheme vg s2c

solid

dash

longdash_dot

dot

longdash

dash_dot

shortdash

shortdash_dot

blank

Line pattern palette

10.7 Line width

We can indicate the width of a line in two ways. We can indicate a linewidthstyle,

which allows us to use a word to specify the width of a line, including none (no width,

invisible), vvthin,vthin,thin,medthin,medium,medthick,thick,vthick,vvthick,and

even vvvthick. We can also specify a relativesize, which is a multiple of the line’s normal

thickness (e.g., *2 is twice as thick, or *.7 is .7 times as thick). See [G]linewidthstyle for

more information.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

338 Chapter 10. Styles for changing the look of graphs

twoway (line close tradeday, clwidth(vthick) sort)

(lfit close tradeday, clwidth(thick))

(lowess close tradeday, bwidth(.5) clwidth(thin))

1250 1300 1350 1400

0 10 20 30 40

Trading day number

Closing price Fitted values

lowess close tradeday

Now, we plot the same three lines but

this time diﬀerentiate them by line

thickness using the clwidth() (connect

line width) option.

Uses spjanfeb2001.dta & scheme vg s2c

twoway (line close tradeday, clwidth(*4) sort)

(lfit close tradeday, clwidth(*2))

(lowess close tradeday, bwidth(.5) clwidth(*.5))

1250 1300 1350 1400

0 10 20 30 40

Trading day number

Closing price Fitted values

lowess close tradeday

We could create a similar graph using

the clwidth() option and specify the

widths as relative sizes, making the line

four times as wide for the line plot,

two times as wide for the lfit

command, and half as wide for the line

for the lowess command.

Uses spjanfeb2001.dta & scheme vg s2c

10.8 Margins

We can specify the size of a margin in three diﬀerent ways. We can use a word that rep-

resents a predeﬁned margin. These include zero,vtiny,tiny,vsmall,small,medsmall,

medium,medlarge,large,andvlarge. They also include top bottom to indicate a medium

margin at the top and bottom, and sides to indicate a medium margin at the left and right.

A second method is to give four numbers giving the margins at the left, right, top, and bot-

tom. A third method is to use expressions such as b=5 to modify one or more of the margins.

These are illustrated below. See [G]marginstyle for more information.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10.8 Margins 339

scatter workers2 faminc, title("Overall title", margin(large) box)

We illustrate the control of margins by

adding a title to this scatterplot and

putting a box around it. We can then

see the eﬀect of the margin() option:

the gap between the title and the box

changes. Here, we specify a large

margin, and the margin on all four

sides is now large.

Uses allstatesdc.dta & scheme vg s2c

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Overall title

scatter workers2 faminc, title("Overall title", margin(top bottom) box)

Using margin(top bottom), we obtain

a margin that is medium on the top and

bottom but zero on the left and right.

Uses allstatesdc.dta & scheme vg s2c

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Overall title

scatter workers2 faminc, title("Overall title", margin(sides) box)

Using the margin(sides) option, we

obtain a margin that is medium on the

left and right but zero on the top and

bottom.

Uses allstatesdc.dta & scheme vg s2c

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Overall title

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

340 Chapter 10. Styles for changing the look of graphs

scatter workers2 faminc, title("Overall title", margin(9 6 3 0) box)

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Overall title In addition to the words describing

margins, we can manually specify the

margin for the left, right, bottom, and

top. In this example, we specify

margin(9 6 3 0) and make the margin

for the left 9, for the right 6, for the

bottom 3, and for the top 0.

Uses allstatesdc.dta & scheme vg s2c

scatter workers2 faminc, title("Overall title", margin(l=9 r=9) box)

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

Overall title We can also manually change some of

the margins without specifying all four

margins (as in the previous example).

By specifying margin(l=9 r=9),we

can make the margin at the left and

right 9 units, leaving the top and

bottom unchanged. We can specify one

or more of the expressions l=,r=,t=,

or b= to modify the left, right, top, or

bottom margins, respectively.

Uses allstatesdc.dta & scheme vg s2c

10.9 Marker size

We can control the size of the markers by specifying a markersizestyle or a relativesize.

The markersizestyle is a word that describes the size of a marker, including vtiny,tiny,

vsmall,small,medsmall,medium,medlarge,large,vlarge,huge,vhuge,andehuge.We

could also specify the sizes as a relativesize, which is either an absolute size or a multiple

of the original size of the marker (e.g., *2 is twice as large, or *.7 is .7 times as large). See

[G]markersizestyle for more information.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10.10 Orientation 341

twoway (scatter propval100 rent700 ownhome urban,

msize(vsmall medium large))

Here, we have an overlaid scatterplot

where we graph three variables on the

y-axis (propval100,rent700,and

ownhome) and use the msize(vsmall

medium large) option to make the

sizes of these markers very small,

medium, and large, respectively.

Uses allstatesdc.dta & scheme vg s2c

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

% who own home

twoway (scatter propval100 rent700 ownhome urban, msize(*.5 *1 *1.5))

We can repeat the previous graph but

use relative sizes within the msize()

option to control the sizes of the

markers, making them, respectively,

half the normal size, regular size, and

half again the normal size.

Uses allstatesdc.dta & scheme vg s2c

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

% who own home

10.10 Orientation

An orientationstyle is used to change the orientation of text, such as a y-axis title, an

x-axis title, or added text. An orientationstyle is similar to an anglestyle (see Styles : Angles

(327)). We can only specify four diﬀerent orientations using the keywords horizontal for

0 degrees, vertical for 90 degrees, rhorizontal for 180 degrees, and rvertical for 270

degrees. See [G]orientationstyle for more information.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

342 Chapter 10. Styles for changing the look of graphs

scatter workers2 faminc,

ytitle("Family" "Worker" "Status", orientation(horizontal))

45 50 55 60 65 70

Family

Worker

Status

15000 20000 25000 30000

1979 Median Family Inc.

This example shows how we can rotate

the title for the y-axis using the

orientation(horizontal) option to

make the title horizontal.

Uses allstatesdc.dta & scheme vg s2c

scatter workers2 faminc,

xtitle("Family" "Income", orientation(vertical))

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

Family

Income

This example shows how we can rotate

the title for the x-axis to be vertical

using the orientation(vertical)

option.

Uses allstatesdc.dta & scheme vg s2c

10.11 Marker symbols

Stata allows a wide variety of marker symbols. We can specify O(circle), D(diamond),

T(triangle), S(square), +(plus sign), X(x), p(a tiny point), and i(invisible). We can also

use lowercase letters o,d,t,s,andxto indicate smaller symbols. For circles, diamonds,

triangles, and squares, we can append an hto indicate that the symbol should be displayed

as hollow (e.g., Oh is a hollow circle). See [G]symbolstyle for more information.

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

10.11 Marker symbols 343

twoway (scatter propval100 rent700 ownhome urban, msymbol(S T O))

In this example, we use the msymbol(S

TO)(marker symbol) option to plot

the three symbols in this graph using

squares, triangles, and circles.

Uses allstatesdc.dta & scheme vg s2c

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

% who own home

twoway (scatter propval100 rent700 ownhome urban, msymbol(Sh Th Oh))

We append an hto each marker symbol

option to indicate that the symbol

should be displayed as hollow.

Uses allstatesdc.dta & scheme vg s2c

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

% who own home

twoway (scatter propval100 rent700 ownhome urban, msymbol(s t o))

In this example, we use the msymbol(s

to)option to specify small squares,

small triangles, and small circles.

Uses allstatesdc.dta & scheme vg s2c

0 20 40 60 80 100

20 40 60 80 100

Percent urban 1990

% homes cost $100K+ % rents $700+/mo

% who own home

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Angles Colors Clockpos Compassdir Connect Linepatterns Linewidth Margins Markersize Orientation Symbols Textsize

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published form of the book may be distributed or reproduced, either electronically or in printed form.

344 Chapter 10. Styles for changing the look of graphs

10.12 Text size

The textsizestyle is used to control the size of text, either by specifying a keyword

that corresponds to a particular size or by specifying a number representing a relative size.

The predeﬁned keywords include zero,miniscule,quarter tiny,third tiny,half tiny,

tiny,vsmall,small,medsmall,medium,medlarge,large,vlarge,huge,andvhuge.We

could also specify the sizes as a relative size, which is a multiple of the original size of the

text. See [G]textsizestyle for more information.

scatter workers2 faminc, mlabel(stateab) mlabsize(small)

ID IL

TN TX

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

This example uses mlabel(stateab) to

add marker labels with the state

abbreviation labeling each point. We

use the mlabsize(small) (marker label

size) option to modify the size of the

marker labels to make the labels small.

Uses allstatesdc.dta & scheme vg s2c

scatter workers2 faminc, mlabel(stateab) mlabsize(*1.5)

CO CT

ID IL

NCND

OK OR

TN TX

VT VA

45 50 55 60 65 70

% HHs with 2+ workers

15000 20000 25000 30000

1979 Median Family Inc.

In addition to using the keywords, we

can specify a relative size that is a

multiple of the normal size. Here, we

use the mlabsize(*1.5) option to

make the marker labels 1.5 times as

large as they would normally be.

Uses allstatesdc.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

11 Appendix

The appendix contains a hodgepodge of material that did not ﬁt well in any of the previ-

ous chapters. We begin by illustrating some of the other kinds of graphs Stata can produce

that were not covered in this book and how to use the options illustrated in this book to

make them. Next, we look at how to save graphs, redisplay graphs, and combine multiple

graphs into a single graph. This is followed by a section with more realistic examples that

require a combination of multiple options or data manipulation to create the graph. We

review some common mistakes in writing graph commands and showing how to ﬁx them,

followed by a brief look at creating custom schemes. This chapter and the book conclude

by describing the online supplements to the book and how to get them.

11.1 Overview of statistical graph commands, stat graphs

This section illustrates some of the Stata commands for producing specialized statistical

graphs. Unlike other sections of this book, this section merely illustrates these kinds of

graphs but does not further explain the syntax of the commands used to create them. The

graphs are illustrated on the following six pages, with multiple graphs on each page. The

title of each graph is the name of the Stata command that produced the graph. We can use

the help command to ﬁnd out more about that command or look up more information in

the appropriate Stata manual. The ﬁgures are described below.

•Figure 11.1 illustrates a number of graphs used to examine the univariate distribution

of variables.

•Figure 11.2 illustrates the gladder and qladder commands, which show the dis-

tribution of a variable according to the ladder of powers to help visually identify

transformations for achieving normality.

•Figure 11.3 shows a number of graphs that can be used to assess how your data meets

the assumptions of linear regression.

•Figure 11.4 shows some plots that help to illustrate the results of a survival analysis.

•Figure 11.5 shows a number of diﬀerent plots used to understand the nature of time-

series data and to select among diﬀerent time-series models.

•Figure 11.6 shows plots associated with Receiver Operating Characteristic (ROC) anal-

yses, which can also be used with logistic regression analysis.

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

345

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published form of the book may be distributed or reproduced, either electronically or in printed form.

346 Chapter 11. Appendix

0 .05 .1 .15

Density

0 10 20 30 40

hourly wage

histogram

0 10 20 30 40 50

Frequency

0 10 20 30 40

hourly wage

spikeplot

0 .05 .1 .15

Density

0 10 20 30 40

hourly wage

kdensity

0 10 20 30 40

Distance above median

0 1 2 3 4 5

Distance below median

symplot

0.00 0.50 1.00

Normal F[(wage−m)/s]

0.00 0.25 0.50 0.75 1.00

Empirical P[i] = i/(N+1)

pnorm

−10 0 10 20 30 40

hourly wage

−10 0 10 20 30

Inverse Normal

qnorm

Figure 11.1: Distribution graphs

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11.1 Overview of statistical graph commands, stat graphs 347

0 2.0e−044.0e−04

0 20000 40000 60000

cubic

0 .005 .01

0 1000 2000

square

0 .05 .1 .15

0 20 40

identity

0 .2 .4 .6

0 2 4 6

sqrt

0 .5 1

0 2 4

log

0 2 4

−1−.5 0

1/sqrt

0 2 4 6

−1−.5 0

inverse

0 10 20

−1−.5 0

1/square

0 10 20 30

−1−.5 0

1/cubic

Density

hourly wage

Histograms by transformation

gladder

−50000 0 50000

−20000 0 20000

cubic

−1000 0 1000 2000

−500 0 500 1000

square

−20 0 20 40

−20 0 20

identity

0 2 4 6

sqrt

0 2 4

log

−1−.5 0

1/sqrt

−1−.5 0

−.5 0 .5

inverse

−1−.5 0

−.2 0 .2

1/square

−1−.5 0

−.2 −.1 0 .1

1/cubic

hourly wage

Quantile−Normal plots by transformation

qladder

Figure 11.2: Ladder of power graphs

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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published form of the book may be distributed or reproduced, either electronically or in printed form.

348 Chapter 11. Appendix

−10 0 10 20

Residuals

−5 0 5 10 15 20

Fitted values

rvfplot

−10 0 10 20

Residuals

0 5000 10000

Pop/10 sq. miles

rvpplot

0 .1 .2 .3

Leverage

0 .1 .2 .3

Normalized residual squared

lvr2plot

−10 0 10 20 30

Component plus residual

0 5000 10000

Pop/10 sq. miles

cprplot

−10 0 10 20 30

Aug Comp Plus Res

0 5000 10000

Pop/10 sq. miles

acprplot

−10 0 10 20 30

e( rent700 | X )

−30 −20 −10 0 10 20

e( urban | X )

coef = .21476037, se = .06342317, t = 3.39

avplot

Figure 11.3: Regression diagnostics graphs

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11.1 Overview of statistical graph commands, stat graphs 349

0.00 0.50 1.00

0 500 1000 1500 2000 2500

analysis time

sts graph, by()

.2 .4 .6 .8 1

Survival

0 500 1000 1500 2000 2500

analysis time

stcurve, surv

.2 .4 .6 .8 1

Proportion Surviving

0 500 1000 1500 2000 2500

recurrence free survival time,

ltable, graph

0 .2 .4 .6 .8 1

Survival probability

0 5000 10000 15000

analysis time

stci, graph

0 2 4 6

−ln[−ln(Survival Probability)]

2 4 6 8

ln(analysis time)

stphplot

0.00 0.50 1.00

Survival Probability

0 500 1000 1500 2000 2500

analysis time

stcoxkm

Figure 11.4: Survival graphs

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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published form of the book may be distributed or reproduced, either electronically or in printed form.

350 Chapter 11. Appendix

−1.00 −0.50 0.00 0.50 1.00

Autocorrelations of close

0 10 20 30 40

Lag

0.00 0.50 1.00

Partial autocorrelations of close

0 10 20 30 40

Lag

pac

−5.00 0.00 5.00

Closing price

Log Periodogram

0.00 0.10 0.20 0.30 0.40 0.50

Frequency

pergram

0.00 0.50 1.00

Closing price

Cumulative spectral distribution

0.00 0.10 0.20 0.30 0.40 0.50

Frequency

cumsp

−0.40 −0.20 0.00

Cross−correlations of close and volume

−20 −10 0 10 20

Lag

xcorr

0.00 0.50 1.00

Cumulative periodogram for close

0.00 0.10 0.20 0.30 0.40 0.50

Frequency

wntestb

Figure 11.5: Time-series graphs

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published form of the book may be distributed or reproduced, either electronically or in printed form.

11.1 Overview of statistical graph commands, stat graphs 351

0.00 0.25 0.50 0.75 1.00

Sensitivity

0.00 0.25 0.50 0.75 1.00

1 − Specificity

Area under ROC curve = 0.8828

roctab, graph

0 .25 .5 .75 1

Sensitivity

0 .25 .5 .75 1

1 − Specificity

Area under curve = 0.8945 se(area) = 0.0305

rocplot

0.00 0.25 0.50 0.75 1.00

Sensitivity

0.00 0.25 0.50 0.75 1.00

1−Specificity

roccomp, graph

0.00 0.25 0.50 0.75 1.00

Sensitivity

0.00 0.25 0.50 0.75 1.00

1 − Specificity

Area under ROC curve = 0.8828

lroc

0.00 0.25 0.50 0.75 1.00

Sensitivity/Specificity

0.00 0.25 0.50 0.75 1.00

Probability cutoff

Sensitivity Specificity

lsens

Figure 11.6: ROC graphs

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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352 Chapter 11. Appendix

11.2 Common options for statistical graphs, stat graph op-

tions

This section illustrates how to use Stata graph options with specialized statistical graph

commands. Many of the examples will assume that we have run the command

. regress propval100 popden pcturban

and will illustrate subsequent commands with options to customize those specialized statis-

tics graphs.

lvr2plot

0 .1 .2 .3

Leverage

0 .05 .1 .15 .2

Normalized residual squared

Consider this regression analysis, which

predicts propval100 from two

variables, popden and pcturban.We

can use the lvr2plot command to

produce a leverage-versus-residual

squared plot.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

lvr2plot, msymbol(Oh) msize(vlarge)

0 .1 .2 .3

Leverage

0 .05 .1 .15 .2

Normalized residual squared

We can add options such as msymbol()

and msize() to control the display of

the markers in the graph. See

Options : Markers (235) for more details.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

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published form of the book may be distributed or reproduced, either electronically or in printed form.

11.2 Common options for statistical graphs, stat graph options 353

lvr2plot, mlabel(stateab)

We can add the mlabel() option to add

marker labels to the graph. We could

also add further options to control the

size, color, and position of the marker

labels; see Options : Marker labels (247)

for more details.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban AL AK

0 .1 .2 .3

Leverage

0 .05 .1 .15 .2

Normalized residual squared

kdensity propval100

Consider this kernel-density plot for the

variable propval100. We could add

options to control the display of the

line. See the following example.

Uses allstates.dta & scheme vg s2c

0 .005 .01 .015 .02 .025

Density

0 20 40 60 80 100

% homes cost $100K+

kdensity propval100, clwidth(thick) clpattern(dash)

The section Options : Connecting (250)

shows a number of options we could

add to control the display of the line.

Here, we add the clwidth() and

clpattern() options to make the line

thick and dashed.

Uses allstates.dta & scheme vg s2c

0 .005 .01 .015 .02 .025

Density

0 20 40 60 80 100

% homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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published form of the book may be distributed or reproduced, either electronically or in printed form.

354 Chapter 11. Appendix

avplot popden

−40 −20 0 20 40 60

e( propval100 | X )

−2000 0 2000 4000 6000

e( popden | X )

coef = .00673009, se = .00120878, t = 5.57

Consider this added-variable plot. We

can modify the axis titles as illustrated

in the following examples.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

avplot popden, xtitle("popden adjusted for percent urban")

ytitle("property value adjusted for percent urban")

−40 −20 0 20 40 60

property value adjusted for percent urban

−2000 0 2000 4000 6000

popden adjusted for percent urban

coef = .00673009, se = .00120878, t = 5.57

Here, we use the xtitle() and

ytitle() options to change the titles of

the x-andy-axes. See

Options : Axis titles (254) for more

details.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

avplot popden, note("Regression statistics for popden", prefix)

−40 −20 0 20 40 60

e( propval100 | X )

−2000 0 2000 4000 6000

e( popden | X )

Regression statistics for popden

coef = .00673009, se = .00120878, t = 5.57

The prefix option can be used with

the diﬀerent title options to add a preﬁx

to an existing title. In the note()

option, for example, we add text before

the existing note. In this way, we add

additional descriptive information to an

existing title, subtitle, note, or caption.

We could also use the suffix option to

add information after an existing title.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

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published form of the book may be distributed or reproduced, either electronically or in printed form.

11.2 Common options for statistical graphs, stat graph options 355

avplot popden, xtitle(, size(huge))

We can modify the look of the existing

title without changing the text. For

example, we add the size(huge)

option to make the existing title huge

in size. See Options : Axis titles (254)

and Options : Textboxes (303) for more

details.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

−40 −20 0 20 40 60

e( propval100 | X )

−2000 0 2000 4000 6000

e( popden | X )

coef = .00673009, se = .00120878, t = 5.57

rvfplot

Consider this residual-versus-ﬁt plot.

We often hope to see an even

distribution of points around zero on

the y-axis. To help evaluate this

distribution, we might want to label the

y-axis identically for the values above 0

and below 0.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

−20 0 20 40 60

Residuals

0 20 40 60 80 100

Fitted values

rvfplot, ylabel(-60(20)60, nogrid) yline(-20 20)

Here, we add the ylabel() option to

label the y-axis from −60 to 60,

incrementing by 20, and suppress the

grid. Further, we use the yline()

option to add a y-lineat20and−20.

For more information about labeling

and scaling axes, see Options : Axis labels

(256) and Options : Axis scales (265).

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

−60 −40 −20 0 20 40 60

Residuals

0 20 40 60 80 100

Fitted values

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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356 Chapter 11. Appendix

sts graph, by(hormon)

0.00 0.25 0.50 0.75 1.00

0 500 1000 1500 2000 2500

analysis time

hormon = 0 hormon = 1

Kaplan−Meier survival estimates, by hormon This graph shows survival-time

estimates broken down by whether one

is in the treatment group or the control

group. The legend speciﬁes the groups,

but we might want to modify the labels

as shown in the next example.

Uses hormone.dta & scheme vg s2c

sts graph, by(hormon) legend(label(1 Control) label(2 Treatment))

0.00 0.25 0.50 0.75 1.00

0 500 1000 1500 2000 2500

analysis time

Control Treatment

Kaplan−Meier survival estimates, by hormon We can use the legend() option to use

diﬀerent labels within the legend. See

Options : Legend (287) for more details.

Uses hormone.dta & scheme vg s2c

sts graph, by(hormon) legend(off)

text(.5 800 "Control", box) text(.8 1500 "Treatment", box)

Control

Treatment

0.00 0.25 0.50 0.75 1.00

0 500 1000 1500 2000 2500

analysis time

Kaplan−Meier survival estimates, by hormon To suppress the display of the legend,

we can use the legend(off) option.

Instead, we can use the text() option

to add text directly to the graph to

label the two lines; see

Options : Adding text (299) for more

information. We also use the box

option to surround the text with a box;

see Options : Textboxes (303) for more

details.

Uses hormone.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

11.2 Common options for statistical graphs, stat graph options 357

avplot popden, title("Added variable plot")

We return to the regression analysis

predicting propval100 from two

variables, popden and pcturban. Here,

we show an added-variable plot with

the title() option to add a title. We

could also add a subtitle(),

caption(),ornote() to the graph, as

well; see Standard options : Titles (313)

for more details.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

−40 −20 0 20 40 60

e( propval100 | X )

−2000 0 2000 4000 6000

e( popden | X )

coef = .00673009, se = .00120878, t = 5.57

Added variable plot

avplot popden, note("")

Here, we add the note("") option,

which suppresses the display of the note

at the bottom showing the coeﬃcients

for the regression model.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

−40 −20 0 20 40 60

e( propval100 | X )

−2000 0 2000 4000 6000

e( popden | X )

avplot popden, scheme(economist)

We can change the look of the graph by

selecting a diﬀerent scheme. Here, we

use scheme(economist) to display the

graph using the economist scheme. See

Standard options : Schemes (318) for

more details.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

−40

−20

e( propval100 | X )

−2000 0 2000 4000 6000

e( popden | X )

coef = .00673009, se = .00120878, t = 5.57

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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358 Chapter 11. Appendix

avplot popden, xsize(3) ysize(1) scale(1.3)

−40 −20 0 20 40 60

e( propval100 | X )

−2000 0 2000 4000 6000

e( popden | X )

coef = .00673009, se = .00120878, t = 5.57

The section Standard options : Sizing

graphs (322) describes options we can

use to control the size of the graph and

the scale of the contents of the graph.

Here, we show the xsize(),ysize(),

and scale() options.

Uses allstates.dta & scheme vg s2c

Before running the graph command,

type

reg propval100 popden pcturban

11.3 Saving and combining graphs, save/redisplay/combine

This section shows how to save, redisplay, and combine Stata graphs. We begin by

showing how to save graphs either to disk or in memory. We also show how to redisplay

the graph and, when we redisplay the graph, control the look of the graph.

twoway histogram urban, saving(hist1)

0 .01 .02 .03

Density

40 60 80 100

Percent urban 1990

Most, if not all, Stata graph commands

allow us to use the saving() option to

save the graph as a Stata .gph ﬁle. We

save this graph, naming it hist1.gph,

and store it in the current directory.

We will assume in these examples that

all graphs are stored in the current

directory, but we can precede the

ﬁlename with a directory name and

store it wherever we wish.

Uses allstates.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

11.3 Saving and combining graphs, save/redisplay/combine 359

graph use hist1.gph

At a later time (including after quitting

and restarting Stata), we can view the

saved graph with the graph use

command. If hist1.gph had been

stored in a diﬀerent directory, we would

have to precede it with the directory

where it was saved or use the cd

command to change to that directory.

Uses allstates.dta & scheme vg s2c

0 .01 .02 .03

Density

40 60 80 100

Percent urban 1990

graph use hist1.gph, scheme(s1mono)

When we view the graph, we can add

the scheme() option to view the same

graph using a diﬀerent scheme. Here,

we view the last graph but use the

s1mono scheme.

Uses allstates.dta & scheme s1mono

0 .01 .02 .03

Density

40 60 80 100

Percent urban 1990

twoway histogram propval100, name(hist2)

The name() option is much like the

saving() option, except that the graph

is saved in memory instead of on disk.

We can then view the graph later

within the same Stata session, but once

we quit Stata, the graph in memory

will be gone.

Uses allstates.dta & scheme vg s1c

0 .01 .02 .03 .04

Density

0 20 40 60 80

% homes cost $100K+

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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360 Chapter 11. Appendix

graph display hist2

0 .01 .02 .03 .04

Density

0 20 40 60 80

% homes cost $100K+

The graph display command is

similar to the graph use command,

except that it redisplays graphs saved

in memory. Here, we redisplay the

graph we created with the name(hist2)

option.

Uses allstates.dta & scheme vg s1c

graph display hist2, xsize(2) ysize(2)

0 .01 .02 .03 .04

Density

0 20 40 60 80

% homes cost $100K+

The graph display command allows us to use the

xsize() and ysize() options to change the size and

aspect ratio of the graph. Here, we redisplay the graph

we named hist2 and make the graph 2 inches tall by 2

inches wide.

Uses allstates.dta & scheme vg s1c

graph display hist2, scheme(s1mono)

0 .01 .02 .03 .04

Density

0 20 40 60 80

% homes cost $100K+

We can also use the scheme() option to view the same

graph using a diﬀerent scheme. Here, we view the

previous graph but use the s1mono scheme.

Uses allstates.dta & scheme s1mono

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published form of the book may be distributed or reproduced, either electronically or in printed form.

11.3 Saving and combining graphs, save/redisplay/combine 361

Let’s look at some examples to show how to combine graphs once they have been cre-

ated and saved. First, we will see how to show two scatterplots side by side rather than

overlaying them.

twoway scatter propval100 urban, name(scat1)

Using the name(scat1) option saves

this scatterplot in memory with the

name scat1.

Uses allstates.dta & scheme vg s2c

0 20 40 60 80 100

% homes cost $100K+

20 40 60 80 100

Percent urban 1990

twoway scatter rent700 urban, name(scat2)

We save this second scatterplot with

the name scat2.

Uses allstates.dta & scheme vg s2c

0 10 20 30 40

% rents $700+/mo

20 40 60 80 100

Percent urban 1990

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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362 Chapter 11. Appendix

graph combine scat1 scat2

0 20 40 60 80 100

% homes cost $100K+

20 40 60 80 100

Percent urban 1990

0 10 20 30 40

% rents $700+/mo

20 40 60 80 100

Percent urban 1990

Using the graph combine command,

we can see these two scatterplots side

by side. In a sense, the y-axis is on a

diﬀerent scale for these two graphs

since they are diﬀerent variables.

However, in another sense, the scale for

the two y-axes is the same since they

are both measured in percents.

Uses allstates.dta & scheme vg s2c

graph combine scat1 scat2, ycommon

0 20 40 60 80 100

% homes cost $100K+

20 40 60 80 100

Percent urban 1990

0 20 40 60 80 100

% rents $700+/mo

20 40 60 80 100

Percent urban 1990

This graph is the same as the last one,

except that the y-axes are placed on a

common scale by using the ycommon

option. This makes it easy to compare

the two y-variables by forcing them to

be on the same metric. Note that the

ycommon option does not work when the

graphs have been made using diﬀerent

kinds of commands, e.g., graph bar

and graph box.

Uses allstates.dta & scheme vg s2c

Let’s look at more detailed examples showing how we can combine graphs and at options

we can use in creating the graphs. The next set of examples uses the sp2001ts data ﬁle.

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11.3 Saving and combining graphs, save/redisplay/combine 363

twoway rarea high low date, name(hilo)

We make a graph showing the high and

low closing price of the S&P 500 for

2001 and save this graph in memory,

naming it hilo.

Uses sp2001ts.dta & scheme vg s2c

900 1000 1100 1200 1300 1400

High price/Low price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

twoway spike volmil date, name(vol)

We can make another graph that shows

the volume (millions of shares sold per

day) for 2001 and save this graph in

memory, naming it vol.

Uses sp2001ts.dta & scheme vg s2c

.5 1 1.5 2 2.5

Volume (millions)

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

graph combine hilo vol

We can now use the graph combine

command to combine these two graphs

into a single graph. The graphs are

displayed as a single row, but say that

we would like to display them in a

single column.

Uses sp2001ts.dta & scheme vg s2c

900 1000 1100 1200 1300 1400

High price/Low price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

.5 1 1.5 2 2.5

Volume (millions)

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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published form of the book may be distributed or reproduced, either electronically or in printed form.

364 Chapter 11. Appendix

graph combine hilo vol, cols(1)

90010001100120013001400

High price/Low price

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

.5 1 1.5 2 2.5

Volume (millions)

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

Using the cols(1) option, we can

display the price above the volume.

However, because the x-axes of these

two graphs are scaled the same, we

could save space and remove the x-axis

scale from the top graph.

Uses sp2001ts.dta & scheme vg s2c

twoway rarea high low date, xscale(off) name(hilo, replace)

900 1000 1100 1200 1300 1400

High price/Low price

Here, we use the xscale(off) option

to suppress the display of the x-axis,

including the space that would be

allocated for the labels. We name this

graph hilo again but need to use the

replace option to replace the existing

graph named hilo.

Uses sp2001ts.dta & scheme vg s2c

graph combine hilo vol, cols(1)

900 1000 1100 1200 1300 1400

High price/Low price

.5 1 1.5 2 2.5

Volume (millions)

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

We combine these two graphs; however,

we might want to push the graphs a bit

closer together.

Uses sp2001ts.dta & scheme vg s2c

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11.3 Saving and combining graphs, save/redisplay/combine 365

graph combine hilo vol, cols(1) imargin(b=1 t=1)

Here, we use the imargin(b=1 t=1)

option to make the margin at the top

and bottom of the graphs to be very

small before combining them. However,

we might want the lower graph of

volume to be smaller.

Uses sp2001ts.dta & scheme vg s2c

900 1000 1100 1200 1300 1400

High price/Low price

.5 1 1.5 2 2.5

Volume (millions)

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

twoway spike volmil date, ylabel(1 2) fysize(25) name(vol, replace)

Using the fysize() (force ysize)

option makes the graph 25% of its

normal size. We use this instead of

ysize() because the graph combine

command does not respect the ysize()

or xsize() options. For aesthetics, we

also reduce the number of labels. We

save this graph in memory, replacing

the existing graph named vol.

Uses sp2001ts.dta & scheme vg s2c

1 2

Volume (millions)

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

graph combine hilo vol, cols(1) imargin(b=1 t=1)

We combine these graphs again, and

the combined graph looks pretty good.

We might further tinker with the graph,

changing the xtitle() for the volume

graph to be shorter or modifying the

xlabel() for the volume graph.

Uses sp2001ts.dta & scheme vg s2c

900 1000 1100 1200 1300 1400

High price/Low price

1 2

Volume (millions)

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

Date

Introduction Twoway Matrix Bar Box Dot Pie Options Standard options Styles Appendix

Stat graphs Stat graph options Save/Redisplay/Combine More examples Common mistakes Customizing schemes Online supplements

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366 Chapter 11. Appendix

11.4 Putting it all together, more examples

Most of the examples in this book have focused on the impact of a single option or a

small number of options, using datasets that required no manipulation prior to making the

graph. In reality, many graphs use multiple options together, and some require prior data

management. This section addresses this issue by showing some examples that combine

numerous options and require some data manipulation before making the graph.

twoway (scatter urban pcturban80) (function y=x, range(30 100)),

xtitle(Percent Urban 1980) ytitle(Percent Urban 1990)

legend(order(2 "Line where % Urban 1980 = % Urban 1990") pos(6) ring(0))

20 40 60 80 100

Percent Urban 1990

20.0 40.0 60.0 80.0 100.0

Percent Urban 1980

Line where % Urban 1980 = % Urban 1990

This graph shows the percentage of

population living in an urban area of a

state in 1990 against that of 1980. If

there had been no changes from 1980 to

1990, the values would fall along a

45-degree line, where the value of y

equals the value of x.Overlaying

(function y=x),wecanseeany

discrepancies from 1980 to 1990. The

range(30 100) option makes the line

span from 30 to 100 on the x-axis.

Uses allstates.dta & scheme vg s2c

twoway (lfitci ownhome borninstate) (lfitci ownhome borninstate,

ciplot(rline) blcolor(blue) blwidth(thick) blpattern(dash))

(scatter ownhome borninstate), legend(off) ytitle("% Own Home")

40 50 60 70 80

% Own Home

20 40 60 80

% born in state of residence

This example shows how we can make a

scatterplot, a regression line, and a

conﬁdence interval for the ﬁt shown as

an area. We also add a thick, blue,

dashed line showing the upper and

lower conﬁdence limits. The ﬁrst

lfitci makes the ﬁt line and area; the

second lfitci makes a thick, blue,

dashed outline for the area; and

scatter overlays the scatterplot.

Uses allstates.dta & scheme vg s2c

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11.4 Putting it all together, more examples 367

twoway scatter ownhome borninstate,

by(nsw, hole(1) title("%Own home by" "%born in St." "by region",

pos(11) ring(0) width(65) height(35) justification(center)

alignment(middle)) note(""))

The hole(1) option leaves the ﬁrst

position empty when creating the

graphs, and the title is placed there

using pos(11) and ring(0).Weuse

width() and height() to adjust the

size of the textbox and

justification() and alignment() to

center the textbox horizontally and

vertically. The note("") option

suppresses the note in the bottom

corner of the graph.

Uses allstates.dta & scheme vg s2c

50 60 70 80

20 40 60 80 20 40 60 80

North

South West

% who own home

% born in state of residence

%Own home by

%born in St.

by region

twoway (rspike hi low date) (rcap close close date, msize(medsmall)),

tlabel(08jan2001 01feb2001 21feb2001) legend(off)

Before making this high/low/close

graph, we ﬁrst type tsset date,

daily to tell Stata that date should be

treated as a date in the tlabel()

option. The rcap command uses close

for both the high and the low values,

making the tick line for the closing

price, and the legend(off) option

suppresses the legend. Using the

vg samec scheme makes the spikes and

closes the same color.

Uses spjanfeb2001.dta & scheme

vg samec

1200 1250 1300 1350 1400

08jan2001 01feb2001 21feb2001

Date

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368 Chapter 11. Appendix

twoway (rspike hi low date) (rcap close close date, msize(medsmall))

(scatteri 1220 15027 1220 15034, recast(line) clwid(vthick) clcol(red)),

tlabel(08jan2001 01feb2001 21feb2001) legend(off)

1200 1250 1300 1350 1400

08jan2001 01feb2001 21feb2001

This example is the same as above,

except that this one uses scatteri() to

draw a support-level line. Two yxpairs

are given after the scatteri, and the

recast(line) option draws them as a

line instead of two points. The x-values

were calculated beforehand using

display d(21feb2001) and

display d(28feb2001) to compute the

elapsed date values.

Uses spjanfeb2001.dta & scheme

vg samec

The rest of the examples in this section involve some data management before we create

the graph. For the next few examples, we use the allstates data ﬁle and run a regression

command,

. vguse allstates

. regress ownhome propval100 workers2 popden

and then issue the

. dfbeta

command, creating DFBETAs for each predictor: DFpropval100,DFworkers2,andDFurban,

which are used in the following graph.

twoway dropline DFpropval100 DFworkers2 DFurban statefips,

mlabel(stateab stateab stateab)

−1−.5 0 .5 1

0 20 40 60

state code

Dfbeta propval100 Dfbeta workers2

Dfbeta urban

In this example, we show each of the

DFBETAsasadropline plot. We add

the mlabel() option to label each point

with the state abbreviation.

Uses allstates.dta & scheme vg s2c

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11.4 Putting it all together, more examples 369

twoway (dropline DFpropval100 id if abs(DFpropval100)>.25, mlabel(stateab))

(dropline DFworkers2 id if abs(DFworkers2)>.25, mlabel(stateab))

(dropline DFurban id if abs(DFurban)>.25, mlabel(stateab))

This example is similar to the one above

but simpliﬁes the graph by showing

only the points where the DFBETA

exceeds .25. Note that we have taken

the example from above and converted

it into three overlaid dropline plots,

each of which has an if condition.

Uses allstates.dta & scheme vg s2c

HI MN

−1−.5 0 .5 1

0 10 20 30 40 50

Dfbeta propval100 Dfbeta workers2

Dfbeta urban

Before making the next graph, we need to issue three predict commands to generate

variables that contain the Cook’s distance, the studentized residual, and the leverage based

on the previous regression command:

. predict cd, cook

. predict rs, rstudent

. predict l, leverage

We are then ready to run the next graph.

twoway (scatter rs id) (scatter rs id if abs(rs) > 2, mlabel(stateab)),

legend(off)

This graph uses scatter rs id to

make an index plot of the studentized

residuals. It also overlays a second

scatter command with an if

condition showing only studentized

residuals that have an absolute value

exceeding 2 and showing the labels for

those observations. Using the vg samec

scheme makes the markers the same for

both scatter commands.

Uses allstates.dta & scheme vg samec

−6−4−2 0 2

Studentized residuals

0 10 20 30 40 50

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370 Chapter 11. Appendix

twoway (scatter rs id, text( -3 27 "Possible Outliers", size(vlarge)))

(scatteri -3 18 -4.8 10, recast(line))

(scatteri -3 18 -3 3, recast(line)), legend(off)

Possible Outliers

−6−4−2 0 2

0 10 20 30 40 50

This graph is similar to the one above

but uses the text() option to add text

to the graph. Two scatteri commands

are used to draw a line from the text

Possible Outliers to the markers for

those points. The yxcoordinates are

given for the starting and ending

positions, and recast(line) makes

scatteri behave like a line plot,

connecting the points to the text.

Uses allstates.dta & scheme vg s2c

twoway (scatter rs l [aw=cd], msymbol(Oh))

(scatter rs l if cd > .1, msymbol(i) mlabel(stateab) mlabpos(0))

(scatter rs l if cd > .1, msymbol(i) mlabel(cd) mlabpos(6)), legend(off)

.1903994

.1235647

.6812371

−6−4−2 0 2

Studentized residuals

0 .1 .2 .3 .4

Leverage

This graph shows the

leverage-versus-studentized residuals,

weighting the symbols by Cook’s D

(cd). We overlay it with a scatterplot

showing the marker labels if cd exceeds

.1, with the cd value placed underneath.

Uses allstates.dta & scheme vg s2c

Imagine that we have a data ﬁle called comp2001ts that contains variables representing

the stock prices of four hypothetical companies: pricealpha,pricebeta,pricemu,and

pricesigma, as well as a variable date. To compare the performance of these companies,

let’s make a line plot for each company and stack them. We can do this using twoway

tsline with the by(company) option, but we ﬁrst need to reshape the data into a long

format. We do so with the following commands:

. vguse comp2001ts

. reshape long price, i(date) j(compname) string

We now have variables price and company and can graph the prices by company.

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11.4 Putting it all together, more examples 371

twoway tsline price, by(compname, cols(1) yrescale note("") compact)

ylabel(#2, nogrid) subtitle(, pos(5) ring(0) nobexpand nobox color(red))

title(" ", box width(130) height(.001) bcolor(ebblue))

We graph price for the diﬀerent

companies with the by() option.

Further, cols(1) puts the graphs in

one column. yrescale and ylabel(#2)

allow the y-axes to be scaled

independently and labeled with about 2

values. The subtitle() option puts

the name of the company in the

bottom, right corner of each graph. The

title() option creates an empty title

that is thin, wide, and blue. Combined

with the compact option, the title

creates a border between the graphs.

Uses comp2001ts.dta & scheme vg s2c

0 2040 50 6040 6020 40

1Jan01 1Apr01 1Jul01 1Oct01 1Jan02

alpha

beta

sigma

price

Date

For the next graph, we want to create a bar chart that shows the mean of wages by

occupation with error bars showing a 95% conﬁdence interval for each mean. To do this,

we ﬁrst collapse the data across the levels of occupation, creating the mean, standard devi-

ation, and count. Next, we create the variables wageucl and wagelcl, which are the upper

and lower conﬁdence limits, as shown below.

. vguse allstates

. collapse (mean) mwage=wage (sd) sdwage=wage (count) nwage=wage, by(occ7)

. generate wageucl = mwage + invttail(nwage,0.025)*sdwage/sqrt(nwage)

. generate wagelcl = mwage - invttail(nwage,0.025)*sdwage/sqrt(nwage)

After this, we are ready to execute the following command:

twoway (bar mwage occ7, barwidth(.5))

(rcap wageucl wagelcl occ7, blwid(medthick) blcolor(navy) msize(large)),

xlabel(1(1)7, valuelabel noticks) xscale(range(.5 7.5))

This bar chart is overlaid with a range

plot showing the upper and lower

conﬁdence limits. The xlabel() option

labels the values from 1 to 7,

incrementing by 1. The valuelabel

option indicates that the value labels

for occ7 will be used to label the

x-axis. The xscale() option adds a

margin to the outer bars, and the

barwidth() option creates the gap

between the bars.

Uses allstates.dta & scheme vg s2c

4 6 8 10 12

Prof Mgmt Sales Cler. Operat. Labor Other

Occupation recoded into 7 categories

(mean) wage wageucl/wagelcl

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372 Chapter 11. Appendix

twoway (rcap wageucl wagelcl occ7, blwidth(medthick) msize(large))

(bar mwage occ7, barwidth(.5) bcolor(navy)),

xlabel(1(1)7, valuelabel noticks) xscale(range(.5 7.5))

4 6 8 10 12

Prof Mgmt Sales Cler. Operat. Labor Other

Occupation recoded into 7 categories

wageucl/wagelcl (mean) wage

This graph is similar to the previous

graph, except that we have reversed the

order of the commands, placing the

rcap command ﬁrst, followed by the

bar command. As a result, only the top

half of the error bar is shown. As in the

previous example, the xlabel() option

determines the labels on the x-axis.

Uses allstates.dta & scheme vg s2c

Suppose that we wanted to show the mean wages with conﬁdence intervals broken down

by occupation and whether one graduated college. We use the collapse command to create

the mean, standard deviation, and count by the levels of occ7 and collgrad, and then we

create the upper and lower conﬁdence limits. Finally, the separate command makes sepa-

rate variables for mwage based on whether one graduated college, creating mwage0 (wages for

noncollege grad) and mwage1 (wages for college grad). These commands are shown below,

followed by the command to create the graph.

. vguse nlsw

. collapse (mean) mwage=wage (sd) sdwage=wage

(count) nwage=wage, by(occ7 collgrad)

. generate wageucl = mwage + invttail(nwage,0.025)*sdwage/sqrt(nwage)

. generate wagelcl = mwage - invttail(nwage,0.025)*sdwage/sqrt(nwage)

. separate mwage, by(collgrad)

twoway (line mwage0 mwage1 occ7) (rcap wageucl wagelcl occ7),

xlabel( 1(1)7, valuelabel) xtitle(Occupation) ytitle(Wages)

legend(order(1 "Not College Grad" 2 "College Grad"))

0 5 10 15

Wages

Prof Mgmt Sales Cler. Operat. Labor Other

Occupation

Not College Grad College Grad

Here, we make a line graph showing the

mean wages for the noncollege

graduates, mwage0, and the college

graduates, mwage1, by occupation. We

overlay that with a range plot showing

the conﬁdence interval. The xlabel()

option labels the x-axis with value

labels, and the legend() option labels

the legend.

Uses nlsw.dta & scheme vg s2c

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published form of the book may be distributed or reproduced, either electronically or in printed form.

11.4 Putting it all together, more examples 373

This next graph shows a kind of scatterplot of the mean and conﬁdence interval for

union and collgrad for each level of occ7. To do this, we collapse the data ﬁle by occ7

and use those summary statistics to compute the conﬁdence intervals below, followed by

the command to create the graph.

. vguse nlsw

. collapse (mean) pct un=un pct coll=collgrad

(sd) sd un=union sd coll=collgrad

(count) ct un=union ct coll=collgrad, by(occ7)

. gen lci un = pct un - sd un/sqrt(ct un)

. gen uci un = pct un + sd un/sqrt(ct un)

. gen lci coll = pct coll - sd coll/sqrt(ct coll)

. gen uci coll = pct coll + sd coll/sqrt(ct coll)

twoway (rcap lci coll uci coll pct un) (rcap lci un uci un pct coll, hor)

(sc pct coll pct un, msymbol(i) mlabel(occ7) mlabpos(10) mlabgap(5)),

ylabel(0(.2).7) xtitle(% Union) ytitle(% Coll Grads) legend(off)

titl e("% Union and % college graduates" "(with CIs) by occupation")

The overlaid rcap commands show the

conﬁdence intervals for both union and

collgrad for each occupation. The

scatter command uses an invisible

marker and labels each occupation at

the 10 o’clock position with a larger

gap than normal.

Uses .dta & scheme vg s2c

Prof

Mgmt

Sales

Cler.

Operat.

Labor

Other

0 .2 .4 .6

% Coll Grads

0 .1 .2 .3 .4

% Union

% Union and % college graduates

(with CIs) by occupation

This section concludes with a graph adapted from an example on the Stata web site.

The graph combines numerous tricks, so rather than show it all at once, let’s build it up a

piece at a time. Below is the ultimate graph we would like to create. It shows the population

(in millions) for males and females in 17 diﬀerent age groups, ranging from “Under 5” up

to “80 to 84”. The blue bar represents the males, and the red bar represents the females.

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374 Chapter 11. Appendix

graph display

Under 5

5 to 9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84

12 8 4 4 8 12

Population in millions

Male Female

This is the graph that we wish to

create. For now, we simply use the

graph display command to display

the graph. Because this is displayed

using the s2color scheme, the size of

the text is not enlarged as in the other

vg schemes, so the text may be hard to

read.

Uses pop2000mf.dta & scheme s2color

To build this graph, we ﬁrst use the data ﬁle pop2000mf, which contains 17 observa-

tions corresponding to 17 age groups (for example, “Under 5”, “5 to 9”, “10 to 14”, and so

forth). The variables femtotal and maletotal contain the number of females and males in

each age group. After using the ﬁle, we create femmil, which is the number of females per

million, and malmil, which is the number of males per million, but this is made negative so

that the male (blue) bar will be scaled in the negative direction. We also make a variable

zero, which contains 0 for all observations.

. vguse pop2000mf

. gen femmil = femtotal/1000000

. gen malmil = -maletotal/1000000

. gen zero = 0

We now take the ﬁrst step in making this graph.

twoway (bar malmil agegrp) (bar femmil agegrp)

−10 −5 0 5 10

0 5 10 15 20

Age category

malmil femmil

This is our ﬁrst attempt to make this

graph by overlaying the bar chart for

the males with the bar chart for the

females. The agegrp variable ranges

from1to17andformsthex-axis, but

we can rotate this as shown in the next

example.

Uses pop2000mf.dta & scheme s2color

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11.4 Putting it all together, more examples 375

twoway (bar malmil agegrp, horizontal) (bar femmil agegrp, horizontal)

Adding the horizontal option to each

bar chart, we can see the graph taking

shape. However, we would like the age

categories to appear inside of the red

(female) bars.

Uses pop2000mf.dta & scheme s2color

0 5 10 15 20

Age category

−10 −5 0 5 10

malmil femmil

twoway (bar malmil agegrp, horizontal) (bar femmil agegrp, horizontal)

(scatter agegrp zero, msymbol(i) mlabel(agegrp) mlabcolor(black))

This scatter command uses agegrp

(ranging from 1–17) as the y-value and

zero (0) for the x-value, leading to the

stack of 17 observations. Using the

msymbol(i) and mlabel() options

suppresses the symbol but displays the

name of the age group from the labeled

value of agegrp. Next, we will ﬁx the

label and title for the x-axis.

Uses pop2000mf.dta & scheme s2color

Under 5

5 to 9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84

0 5 10 15 20

Age category

−10 −5 0 5 10

malmil femmil

Age category

twoway (bar malmil agegrp, horizontal) (bar femmil agegrp, horizontal)

(scatter agegrp zero, msymbol(i) mlabel(agegrp) mlabcolor(black)),

xlabel(-12 "12" -8 "8" -4 "4" 4 8 12) xtitle("Population in millions")

We use the xlabel() to change −12 to

12, −8to8,−4 to 4, and to label the

positive side of the x-axis as 4, 8, and

12. We also add a title for the x-axis.

Next, let’s ﬁx the y-axis and the legend.

Uses pop2000mf.dta & scheme s2color

Under 5

5 to 9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84

0 5 10 15 20

Age category

12 8 4 4 8 12

Population in millions

malmil femmil

Age category

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376 Chapter 11. Appendix

twoway (bar malmil agegrp, horizontal) (bar femmil agegrp, horizontal)

(scatter agegrp zero, msymbol(i) mlabel(agegrp) mlabcolor(black)),

xlabel(-12 "12" -8 "8" -4 "4" 4 8 12) xtitle("Population in millions")

y scale(off) ylabel(, nogrid) legend(order(1 "Male" 2 "Female"))

Under 5

5 to 9

10 to 14

15 to 19

20 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

75 to 79

80 to 84

12 8 4 4 8 12

Population in millions

Male Female

We suppress the display of the y-axis

using the yscale(off) option and

suppress the grid lines with the

ylabel(, nogrid) option. Finally, we

use the legend() option to label the

bars and suppress the display of the

third symbol in the legend.

Uses pop2000mf.dta & scheme s2color

11.5 Common mistakes

This section discusses mistakes that are frequently made when creating Stata graphs.

Using Stata 7 syntax

No matter how long we have been using Stata 8 (or later), we might revert back to old

habits and type a graph command in Stata 7 style. Consider this example:

. graph propval100 rent700

Stata replies with this error message:

propval100graph g.new rent700: class member function not found

r(4023);

Clearly, the easiest solution is to convert the command to the proper Stata 8 syntax.

Commas with graph options

With Stata 8, graph options can accept their own options (sometimes referred to as

suboptions); for example,

. twoway scatter propval100 popden rent700, xtitle("My Title", box)

Note that the xtitle() option allows us to specify the x-title followed by a comma and

a further suboption that places a box around the x-title. If we had been content with the

existing x-title, we could have issued this command:

. twoway scatter propval100 popden rent700, xtitle( , box)

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11.5 Common mistakes 377

The box option places a box around the title. Note that we place a comma before the

box option. Now, suppose that we are content with the existing legend but wish to make

the legend display in a single column.

. twoway scatter propval100 popden rent700, xtitle( , box) legend(cols(1))

Based on the syntax from the title() option, we might have been tempted to have

typed legend( , cols(1)), but that would have led to an error. Some options, like the

legend() option, simply take a list of options with no comma permitted.

Using options in the wrong context

Consider the example below. Our goal is to move the labels for the x-axis from their

default position at the bottom of the graph to the alternate position at the top of the graph.

. twoway scatter propval100 rent700, xlabel( , alternate)

This command executes, but it does not have the desired eﬀect. Instead, it staggers the

labels of the x-axis, alternating between the upper and lower positions. In this context,

the alternate option means something diﬀerent than we had intended. What we really

wanted to specify was xscale(alternate):

. twoway scatter propval100 rent700, xscale(alternate)

This command moves the entire scale of the x-axis to the alternate position and has the

desired eﬀect. Another mistake we might have made was to put the alternate option as

an overall option. This command is shown below with the result:

. twoway scatter propval100 rent700, alternate

option alternate not allowed

invalid syntax

r(198);

In this case, we are half-right. There is an option alternate, but we have used it in the

wrong context, yielding the syntax error. In such cases, remember that the option we are

specifying may be right, but we just need to put it into the right context.

Options appear to have no eﬀect

When we add an option to a graph, we generally expect to see the eﬀect of adding the

option. However, sometimes adding an option has no eﬀect. Consider this example:

. twoway scatter propval100 rent700, mlabpos(12)

This command executes, but nothing changes as a result of including the mlabpos(12)

option, which would change the position of the marker labels to the 12 o’clock position.

There are no marker labels in the graph, so adding this option has no eﬀect. We would have

to use the mlabel() option to add marker labels before we saw the eﬀect of this option.

Consider another example, which is a bit more subtle. We would like to make the line

(periphery) of the marker thick. When we run the following command, we do not see any

eﬀect from adding the mlwidth(thick) option:

. twoway scatter propval100 rent700, mlwidth(thick)

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378 Chapter 11. Appendix

The reason for this is that the marker has a line color and a fill color, and by default,

they are the same color, so it is impossible to see the eﬀect of changing the thickness of the

line around the marker. However, if we make the line and fill colors diﬀerent, as in the

following example, we can see the eﬀect of the mlwidth() option:

. twoway scatter propval100 rent700, mlwidth(thick)

mlcolor(black) mfcolor(gs13)

Options when using by()

Using the by() option changes the meaning of some options. Consider the following

example:

. twoway scatter propval100 rent700, by(north) title(My title)

We might think that the title() option will provide an overall title for the graph, as it

would when the by() option is not included. However, actually, each graph will have “My

title” as the title; the graph as a whole will not. Instead, to provide an overall title for the

graph, we would specify the command this way:

. twoway scatter propval100 rent700, by(north, title(My title))

When using the legend() option combined with the by() option, we should place

options that aﬀect the position of the legend within the by() option. Consider this example:

. twoway scatter propval100 popden rent700,

by(north, legend(pos(12))) legend(cols(1))

Here, the legend(pos(12)) option controls the position of the legend, placing it at

the 12 o’clock position, so we place it within the by() option. On the other hand, the

legend(cols(1)) option does not aﬀect the position of the legend, so we place it outside

of the by() option. For more details on this, see Options : By (272).

Altering the wrong axis

When we use multiple x-ory-axes, it is easy to modify the wrong axis. Consider this

example:

. twoway (scatter propval100 ownhome)

(scatter rent700 ownhome, yaxis(2) ytitle(Rents over 700))

We might think that the ytitle() option will change the title for the second y-axis,

but it will actually change the ﬁrst axis. Because ytitle() is an option that concerns the

overall graph, we should place it at the very end of the graph command, as shown below.

. twoway (scatter propval100 ownhome)

(scatter rent700 ownhome, yaxis(2)), ytitle(Rents over 700, axis(2))

Note that we use the axis(2) option to indicate that ytitle() should be modiﬁed for the

second y-axis.

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11.6 Customizing schemes 379

When all else fails

I hope that, by describing these errors, I can help you avoid some common errors. Here

are some additional ideas and resources to help you when you are struggling:

•Build graphs slowly. Rather than trying to make a ﬁnal graph all at once, try

to build the graph slowly adding, one option at a time. This is illustrated in In-

tro : Building graphs (29), where we took a complex graph and built it one piece at a

time. Building slowly helps us isolate problems to a particular option, which we can

then further investigate.

•When possible, model graphs from existing examples. This book strives to provide

examples to model from. For additional online examples, see Appendix : Online sup-

plements (382) for the companion web site for the book, which links to additional

examples.

•For more detailed information about the syntax of Stata graphics, see [G]graph.

Please remember that some of the graph commands available in Stata were added

after the printing of [G]graph but are documented via the help graph command.

See also Appendix : Online supplements (382), which has links to the online help that

are organized according to the table of contents of this book.

•Reach out to fellow Stata users, either local friends, friends at Statalist, or friends at

Stata tech support. See http://www.stata.com/support/ for more details.

11.6 Customizing schemes

This section shows how to customize your own schemes. Although schemes can look

complicated, it is possible to easily create some simple schemes on our own. Let’s look at

the vg lgndc scheme as an example. This scheme is based on the s2color scheme but

changes the legend to display at the 9 o’clock position, in a single column, with the keys

stacked on top of the symbols. Here are the contents of that scheme:

#include s2color // start with the s2color scheme

clockdir legend position 9 // put the legend in the 9 o’clock position

numstyle legend cols 1 // make the legend display in 1 column

yesno legend stacked yes // stack the keys & symbols on top of each other

gsize legend_key_gap half_tiny // very, very small gap between key and label

gsize legend_row_gap small // somewhat larger gap between key/label pairs

Rather than creating the vg lgndc scheme from scratch, which would be very laborious,

we use the #include s2color statement to base this new scheme on the s2color scheme.

The subsequent statements change the position of the legend and the number of columns

in the legend and stack the legend keys and symbols upon each other.

Say that we liked the vg lgndc scheme but wanted to make our own version in which

the legend is in the 3 o’clock position instead of the 9 o’clock position, naming our version

legend3. To do this, we would start the Stata do-ﬁle editor, for example, by typing doedit

and then type the following into it: (Of course, the scheme will work ﬁne if we omit

comments after the double slashes.)

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380 Chapter 11. Appendix

#include s2color // start with the s2color scheme

clockdir legend position 3// put the legend in the 3 o’clock position

numstyle legend cols 1 // make the legend display in 1 column

yesno legend stacked yes // stack the keys & symbols on top of each other

gsize legend_key_gap half_tiny // very, very small gap between key and label

gsize legend_row_gap small // somewhat larger gap between key/label pairs

We can then save the ﬁle as scheme-legend3.scheme, and we are ready to use it. We

can then use the scheme(legend3) option at the end of a graph command or type set

scheme legend3, and Stata will use that scheme for displaying our graph. Below, we show

an example using this scheme. (Note that the legend3 scheme is not included among the

downloadable schemes.)

twoway (scatter propval100 rent700) (lfit propval100 rent700),

scheme(legend3)

0 20 40 60 80 100

0 10 20 30 40

% rents $700+/mo

% homes cost $100K+

Fitted values

Here, we see an example using our

newly created legend3 scheme, and

indeed, we see the legend in the 3

o’clock position, in a single column,

with the legend stacked.

Uses allstates.dta & scheme legend3

So far, things are going great. However, note that Stata will only know how to ﬁnd

the newly created scheme-legend3.scheme while we are working in the directory where we

saved that scheme. If we change to a diﬀerent directory, Stata will not know where to ﬁnd

scheme-legend3.scheme. If, however, we save the scheme into our PERSONAL directory,

Stata would know where to ﬁnd it regardless of the directory we were in. For example, on

my computer, I used the sysdir command, and it showed me the following information:

. sysdir

STATA: C:\Stata8\

UPDATES: C:\Stata8\ado\updates\

BASE: C:\Stata8\ado\base\

SITE: C:\Stata8\ado\site\

PLUS: c:\ado\plus\

PERSONAL: c:\ado\personal\

OLDPLACE: c:\ado\

From this, I know that my PERSONAL directory is located in c:\ado\personal\,soifI

store either .ado ﬁles or .scheme ﬁles there, Stata will be able to ﬁnd them. So, if instead

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11.6 Customizing schemes 381

of saving scheme-legend3.scheme into the current directory, we save it into our PERSONAL

directory, Stata will be able to ﬁnd it. (If we have already saved scheme-legend3.scheme

to the current directory and also save it to the PERSONAL directory, we may want to remove

the copy from the current directory.)

So far, this section has really focused on the mechanics of creating a scheme but has not

said much about the possible content that could be placed inside a scheme. This is beyond

the scope of this little introduction, but here are three other places where you can ﬁnd this

kind of information:

First, the help for schemes via help schemes will tell us about schemes in general. Also,

help scheme files contains documentation about scheme ﬁles and what we can change

using schemes.

Second, looking at other schemes can help us ﬁnd ideas, for example, the downloaded

schemes for this book (see Appendix : Online supplements (382)). Say that we wanted to look

at the vg rose scheme. We could type which scheme-vg rose.scheme, and that would

tell us where that scheme is located. Then, we could use any editor (including the do-ﬁle

editor) to view that scheme for ideas.

Third, we can look at the built-in Stata schemes, such as s1color,s2color,oreconomist.

Looking at these schemes shows us the menu of items that we can ﬁddle with in our own

schemes, but these schemes should never be modiﬁed directly. We can use the strategy

outlined above where we make our own scheme and use #include to read in a scheme, and

then we can add our own statements to modify the scheme as desired.

Schemes that other people have created and the schemes built into Stata will contain

statements that control some aspect of a graph, but we may not know which aspect they

control. For example, in the vg rose scheme there is the statement

color background eggshell

which obviously controls the color of some kind of background element, but we might not

be sure which element it controls. We can ﬁnd out by making a copy of the scheme and

then changing eggshell to some other nonsubtle value, such as red, and then make a graph

using this new scheme (using scheme(schemename),notset scheme schemename). The

part of the graph that becomes red will indicate the part that is controlled by the color

background statement.

Of course, we have just scratched the surface of how to create and customize schemes.

However, this should provide the basic tools needed for making a basic scheme, storing

it in the personal directory, and then playing with the scheme. Because schemes are so

powerful, they can appear complicated, but if built slowly and methodically, the process

can be straightforward, logical, and, actually, quite a bit of fun.

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382 Chapter 11. Appendix

11.7 Online supplements

This book has a number of online resources associated with it. I encourage all readers

to take advantage of these online extras by visiting the web site for the book at

http://www.stata-press.com/books/vgsg.html

Resources on the web site include

•Programs and help ﬁles. You can easily download and install the programs and help

ﬁles associated with this book. To install these programs and help ﬁles, just type

. net from http://www.stata-press.com/data/vgsg

. net install vgsg

After installing the programs and help, type whelp vgsg foranoverviewofwhathas

been installed.

•Data ﬁles. All the data ﬁles used in the book are available at the web site for down-

loading. I encourage you to download the data ﬁles used in this book, play with these

examples, and try variations on your own to solidify and extend your understanding.

If you visit the website, you can download and save all the data ﬁles at once. You

can quickly download all the datasets into your current working directory from within

Stata by typing

. net from http://www.stata-press.com/data/vgsg

. net get vgsg

If you prefer, you can obtain any of the data ﬁles over the Internet with the vguse

command. Each example concludes by indicating the data ﬁle and scheme that was

used to make the graph. For example, a graph may conclude by saying

Uses allstates.dta & scheme vg s2c

This indicates that you can type vguse allstates and Stata will download and use

the data ﬁle over the Internet for you (assuming that you have installed the programs).

•Schemes. This book uses a variety of schemes, and when you download the programs

and help ﬁles (see above), the schemes used in this book are downloaded as well,

allowing you to use them to reproduce the look of the graphs in this book.

•Hopefully, a very short or empty Errata will be found at the web site. Although I

have tried very hard to make this book true and accurate, I know that some errors

will be found, and they will be listed there.

•Links to the online Stata Graphics Reference Manual, which are organized according

to the structure of the table of contents of this book.

•Other resources that may be placed on the site after this book goes to press, so visit

the site to see what else may appear there.

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Subject index

ac ...................................350

acprplot............................348

added-variable plot ..........see avplot

adjacent lines................see alsize

alsize ..............................188

alternate ..........................171

alternate axes ........see axes, alternate

angle ...........................327–328

axis labels........see xlabel() and

ylabel()

label.....31, 127, 145, 171, 182, 261

marker labels. . . . . .see mlabangle()

angle0()............................220

area() ...............................81

area graphs............see twoway area

color . ............................62

horizontal........................61

setting the base . . ................62

shading..........................62

sorting...........................61

ascategory . . 110, 125–126, 169–170, 203

aspect ratio . ....................323–324

asyvars . . 30–33, 115, 122, 123, 131–136,

159, 161–162, 175, 177–179, 194,

197, 205

augmented component-plus-residual plot

.........see acprplot

avplot ...........348, 354–355, 357–358

aweight .........................37, 240

axes

alternate . . . . 126, 127, 146, 171, 204,

210, 266

bar graphs........123–130, 143–147

base ............................. 47

box plots. .........168–174, 179–183

categorical

bar graphs...............123–130

box plots. ................168–174

dot plots . ................202–204

titles .......................... 31

axes, continued

displaying for multiple graphs . . . 277

dot plots . . ........202–204, 207–210

label gap........................129

labels . . see xlabel() and ylabel()

lines......see xline() and yline()

log scale........................267

multiple . .......85, 92, 98–100, 256,

271–272, 303

options . . ..............254–256, 272

reverse scale....................266

scale . . . see xscale() and yscale()

scaling independently . ..........276

selecting....................271–272

size ........................ 323–324

suppressing............146, 182, 266

titles . . . see xtitle() and ytitle()

axis() ..........................99, 256

b1title() .....31–33, 130, 174, 285, 315

b2title() .................130, 174, 315

bar() ..........................149, 150

bcolor() .......................150

bfcolor() ......................150

blcolor() ......................150

blwidth() ......................150

bar graphs . . . see graph bar and twoway

bar

axes............see axes, bar graphs

bar height ...................... 139

bar width........................64

base ............................. 63

by() .......................151–155

categorical axes............see axes,

categorical, bar graphs

color. . . . ................30, 149–150

conﬁdence intervals . ............366

descending . . . ..............119–123

excluding missing bars. . .see nofill

ﬁll color. .........................64

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384 Subject index

bar graphs, continued

format...........................32

gaps............................118

horizontal..........see graph hbar

labels.......................32, 136

legend......................130–142

line color. ........................64

lines.............................47

look........................147–150

ordering....................119–123

overlaying.......................148

placing labels below bars........142

placing labels inside bars........141

placing labels outside bars......142

reverse order....................123

sorting.....................120–122

stacked .................... 111, 115

titles . ......................143–144

vertical separators ............93–94

y-variables.................107–111

bargap()............................148

barwidth() ...................64, 73, 78

base() ........................47, 62, 63

bcolor() .....................53, 70, 74

bexpand() ......................306–307

bfcolor() .............62, 64, 70, 74, 78

bin() ................................75

bins

lower limit . ......................76

number..........................75

biweight .............................81

blabel() ...........32–33, 127, 136–142

bfcolor() ......................142

box .............................142

format() ................32–33, 142

gap() ......................141–142

position() ................141–142

size() .........................142

blcolor() .....47, 53, 62, 64, 69, 70, 72,

74, 78

blpattern() ..................... 53, 69

blwidth(). . . . . .47, 53, 69, 70, 72, 74, 78

box()...........................186–187

bcolor() .................. 186–187

blcolor() ......................187

blwidth() ......................187

box..................................356

box plots . ................see graph box

adjacent lines ........... see alsize

box plots, continued

alphabetical order...............165

axes.............see axes, box plots

by() ............see by(), box plots

categorical axes............see axes,

categorical, box plots

descending order ................165

excluding missing categories.....see

nofill

horizontal..........see graph hbox

legend......................174–179

lines............................181

look........................183–189

median values.......see medtype(),

medmarker(),andmedline()

ordering....................165–167

over() .......see over(), box plots

patterns ........................181

sorting..........................165

titles. ...........................180

whiskers customized . ............see

cwhiskers

y-variables.................157–162

boxgap() .......................185, 186

bubble plots. . . . .............37, 240–241

building a graph..................29–33

by() ........ 103–105, 191–192, 215–216,

232–234, 272–287, 297–299

alignment() ...................312

b1title() ......................286

bar graphs ................. 151–155

box ........................311–312

box plots . ..................189–193

caption() ......................280

colfirst .......................274

cols() ......153, 192, 216, 275, 277

combining options . . . ........... 286

compact ...........44, 104–105, 275

dot plots . ..................214–217

errors...........................378

height() .......................312

holes() ........................274

iscale() .......................275

ixaxes .....................278–279

ixtitle ...................279, 286

iyaxes.....................277, 278

iytitle ...................278, 286

justification() ...............312

l1title() ......................286

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Subject index 385

by(),continued

legend() . . . 154, 192, 233–234, 285,

286, 297–299

at() .....................234, 299

position() . . . 154, 192, 234, 285,

286, 299

missing ..........152–153, 191, 216

noedgelabel ...................276

note().....................153, 283

suffix .......................283

pie charts ..................232–235

position() ................311–312

rescale ...................277, 286

ring() .....................311–312

row()...........................192

rows() .........................274

scale() ........................105

scatterplot matrices. . . .....103–105,

273–286

sts graph ....................... 356

subtitle().....................282

textboxes...................311–312

title. ............................273

title() ...............279–280, 286

title...........................284

position() ..................284

ring() .......................284

total . . . 44, 153, 191–192, 216, 273,

286

twoway . . . ................44–45, 85

width() ........................312

xrescale .......................276

yrescale .......................276

caps......................see capsize()

capsize() ..........................189

caption()......................280, 315

categorical axes.....see axes, categorical

ciplot ...............................53

clcolor() ............54, 56, 58, 82, 253

clock position . ..................330–331

clpattern() .........52, 56, 82, 90, 253,

336–337, 353

clstyle()............................89

clwidth(). .26, 52, 54, 56, 58, 82, 88–90,

94, 253, 338, 353

color

area graphs......................62

color, continued

axis lines.........................43

bar ﬁll...........................64

bar graphs......................150

bar lines......................47, 64

box plots . ..................186–187

conﬁdence level ..................52

connecting lines ......... 54, 69, 253

graph region . . . . see graphregion()

histogram bars ...................78

intensity...................149, 186

labels......................128, 172

legend..........................296

lines....................82, 208, 211

marker ﬁll...................48, 242

marker outline . .................242

marker symbols..................37

markers . . . 55, 69, 211–213, 242–244

median line.....................187

pie charts ..................221–223

plot region . ......see plotregion()

schemes.........................319

styles.......................328–330

textbox....................304, 310

cols() .........................364–365

columns.............................230

combining graphs...............361–365

commas with graph options. . . . ......376

compass direction . ..............331–332

component-plus-residual plot.........see

cprplot

conﬁdence interval

ﬁt (regression predictions) . . . . 50–54

for means and percentils of survival

time . . . ............... see stci

selecting display command . ......53

setting level . .....................51

conﬁdence level

color . ............................52

pattern . ......................... 52

width............................52

connect() .......39–41, 55, 84, 251–253,

333–335

connect lines width . ........see clwidth

connected plots . . see twoway connected

connecting

lines...........see lines, connecting

points........................39–41

styles.......................332–335

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

386 Subject index

correlogram . .............see ac and pac

cprplot .............................348

cross-correlogram . ............see xcorr

cumsp ...............................350

cumulative spectral distribution graph . .

...........see cumsp

cwhiskers ..........................188

dates..............................58–59

density . . . .....see kdensity and twoway

kdensity

descending ....................122, 220

diagonal............................101

bfcolor() ......................101

discrete .............................79

displaying named graphs ............359

distribution graphs ..................346

distribution plots. . ................74–82

dot plots . ....see graph dot and twoway

dot

alphabetical order...............200

axes ............. see axes, dot plots

by() ........... see by(), dots plots

categorical axes............see axes,

categorical, dot plots

descending order . . ..............200

excluding missing categories.....see

nofill

legend......................205–207

look........................210–214

ordering........................200

over()........see over(), dot plots

reverse order....................202

sorting..........................200

dots() ..............................211

mcolor() .......................211

msize() ........................211

msymbol() ......................211

dropped-line plots. ...........see twoway

dropline

exclude() ..........................135

exclude0 ..................145–146, 209

exploding pie slices..................222

ﬁts (regression predictions)

fractional polynomial . . . . see twoway

fpfit and twoway fpfitci

linear.........see twoway lfit and

twoway lfitci

quadratic . ....see twoway qfit and

twoway qfitci

formatting numbers

axis labels......................261

bar graphs......................142

bar labels........................32

pie slices...................226–227

forty-ﬁve degree lines................362

fraction() ..........................76

fractional polynomial ﬁts.....see twoway

fpfit and twoway fpfitci

frequency() .........................77

frequency............................81

function, line plot of . ........see twoway

function

fysize()............................365

gap

between bars....................148

between bars and edge of plot . . . 148

between boxes..................185

between boxes and edge of plot. .184

between columns................297

between groups.................185

between labels and outside of graph

..............173

between labels and ticks........172

between lines ...................213

between marker and label.......250

between rows...................297

box plots . ..................163–164

dot plots . .......................199

textboxes.......................309

gap() .............................77–78

gladder .............................347

glcolor .............................265

graph bar ...........27, 29–33, 107–155

graph box ......................157–193

graph combine .................362–363

graph display ..................29, 360

graph dot ..................13, 193–217

graph hbar .............13, 63, 107–155

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

Subject index 387

graph hbox .................13, 157–193

graph matrix ............12, 27, 95–105

graph pie ..................14, 217–235

graph use ..........................359

graphing a function . .........see twoway

function

graphregion() .................325–326

color() ........................325

fcolor() .......................326

ifcolor() ......................326

lcolor() .......................326

lwidth() .......................326

graphs, specialized. . . . ....see specialized

graphs

grids

displaying..................264–265

suppressing. ...........146, 182, 264

groups.............see by() and over()

half ................................102

height

bar.............................139

histogram bar ....see histogram, bar

height

symbol..........................297

hi-lo graphs..............see range plots

histogram . . . . ....see twoway histogram

bar color . ........................78

bar height....................75–77

bar width....................78, 80

gap between bars ................ 77

horizontal........................79

overlaying........................81

horizontal ........48, 61, 63, 68, 79, 80

if .................see samples, selecting

imargin() ..........................365

immediate graphs . . . .........see twoway

scatteri

in .................see samples, selecting

intensity() ..............149, 186, 223

jitter()............................102

jittering ........see scatterplot matrices,

jittering

justiﬁcation

textboxes...................305–307

titles . . see title(),justification

kdensity ........................12, 353

kernel density . ........see kdensity and

twoway kdensity

horizontal........................80

lines.............................82

methods.........................81

overlaying........................81

l1title() . . . . . . . 130, 174, 204, 285, 315

l2title() ............130, 174, 204, 315

label() ........................298, 356

labels.............................22–23

alternate . ..................127, 171

angles...............see angle, label

axes....see xlabel() and ylabel()

bar graphs..................32, 136

changing . . . . 124–126, 133, 168, 177,

203

color . ......................128, 172

gap from axis..............129, 130

gap from outside edge of graph. .173

gap from ticks..................172

legend.............see legend, labels

marker symbols...............38–39

markers.............83, 97, 247–250

matrix. ......................98–100

missing values .................. 124

pie charts..............224–228, 233

placing below bars..............142

placing inside bars..............141

placing outside bars.............142

points .......................... 300

position. ..........140–142, 226, 301

scale............................105

size................97, 128, 171, 226

suppressing ......104, 126, 128, 132,

137, 176–177, 206, 260

ticks............................172

time series . ......................59

titles . ............................84

ladder of power graphs ..............347

legend ..................... 23–25, 40–41

bar graphs ................. 130–142

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

388 Subject index

legend, continued

box plots . ..................174–179

columns . . . . . 135, 154, 178, 230, 291

dot plots . . . ................205–207

key ........................ 289, 292

labels........154, 206, 288–289, 296

margins.........................296

options . ....................287–299

overlaid graphs ...............90–92

pie charts..............228–231, 234

placing within plot regions . .....134

position. .31, 134–136, 154, 178, 231,

234, 293–294

rows..........31, 133, 177, 207, 291

stacked................136, 179, 293

subtitle . ........................154

suppressing ................133, 225

text.............................290

titles. . . . ........................295

twoway ..........................86

width...........................294

legend() . . . 23–25, 27–29, 31–33, 40–41,

46, 86, 90–94, 133–136, 138, 154,

177–179, 206–207, 225, 229–231,

234, 285, 288–299, 356

bexpand ........................294

bfcolor() ......................296

bmargin() ......................296

box .............................296

by() ..............see by(),legend

colfirst . . . . 133, 177, 230, 291–292

colgap() .......................297

color() ........................296

cols() . . . . 24, 41, 86, 135, 154, 178,

207, 231, 234, 291, 293, 299

holes() ...................230, 292

label() .....................24–25,

40–41, 90–91, 94, 133, 154, 177,

206, 229, 285–286, 288–289

legend() .......................192

cols() .......................192

note() .........................295

order() ...........41, 230, 289–295

position() .....27, 31–33, 134–136,

178, 207, 293–295, 298

region() .......................296

fcolor() .....................296

lcolor() .....................296

lwidth() .....................296

region(),continued

margin() .....................296

ring() .........31–33, 134–135, 293

rowgap() .......................297

rows() . . . 31–33, 133, 177, 207, 231,

291–296

size() .........................296

span ............................294

stack . . 136, 154, 179, 192, 231, 234,

293

subtitle()

bexpand ......................295

box ...........................295

symxsize().....................297

symysize().....................297

textfirst ............135, 178, 292

title() ..........206, 229–230, 295

position() .........230–231, 295

level() ..............................52

leverage-versus-squared-residual plot . . . .

.........see lvr2plot

life tables for survival data . . . see ltable

line() ..............................223

lcolor() .......................223

lwidth() .......................223

line plots .............. see twoway line

sorting...........................54

line,twoway ..........see twoway line

linear ﬁts..........see lfit and lfitci

linear regression diagnostics graphs . . 348

linegap() ......................213–214

lines

adjacent........................188

axes

color. ..........................43

width..........................43

box plots . . ..................... 181

color .............. 82, 181, 208, 211

connecting . . . 52, 54, 69, 84, 250–253

ﬁt............................52–53

gap between....................213

graph region . . . . see graphregion()

median .........................187

overlaying........................89

patterns . ......43, 82, 181, 208, 211,

336–338

plot region . . . . . . . see plotregion()

styles............................89

textbox outlines............309–310

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

Subject index 389

lines, continued

whiskers.....see box plots, whiskers

width...............82, 88, 181, 208

lines() ........................188, 211

lcolor() ..................188, 211

lwidth() ..................188, 211

linetype().....................211–212

loading graphs . . ......... see graph use

local linear smooth plots . ....see twoway

lowess

lroc ................................351

lsens ...............................351

ltable ..............................349

lvr2plot ..................348, 352–353

margins

graph region . . . . see graphregion()

legend..........................296

plot region . . . . . . . see plotregion()

styles.......................338–340

textboxes.......................308

marker() ..................189, 212–213

mcolor() .......................212

mfcolor() ......................213

mlcolor() ......................213

mlwidth() ......................213

msize() ...............189, 212–213

msymbol() ............ 189, 212–213

markers

ﬁll color. .........................48

box plots . . . .................... 189

color . .......55, 69, 96–97, 211–213,

242–244

displaying for data points........55

ﬁll color ........................ 242

invisible ........................ 239

label gap........................250

label size.........................97

labels........38–39, 83, 97, 247–250

line width........................48

median line.....................188

options . .............95–97, 235–250

outline color . ...................242

outline width ...................243

overlaying........................90

plus sign........................237

schemes.........................247

markers, continued

size....55, 69, 96, 211–213, 240–241,

340–341

squares.........................236

styles..........88, 244–246, 340–343

symbols.......25–27, 36–38, 83, 87,

95–96, 342–343

width............................69

matrix

axis labels...................98–100

scatterplot . . see scatterplot matrices

titles. ...........................101

maxes() ...................100, 104–105

xlabel() .......................100

xtick() ........................100

ylabel() ..............100, 104–105

ytick() ........................100

mcolor(). . . .37, 55, 69, 96, 242, 329–330

mean ................................108

median .........................108, 196

median band plots . . . . see twoway mband

median line

color . ...........................187

markers.........................188

width...........................187

median points...........see medtype(),

medmarker(),andmedline()

median spline plots ..........see twoway

mspline

medline() ..........................187

lcolor() .......................187

lwidth() .......................187

medmarker() ........................188

msize() ........................188

msymbol() ......................188

medtype() ......................187–188

mfcolor() ......48–49, 97, 242–243, 246

missing..........116, 124, 162, 197, 218

mlabangle() ...................249, 327

mlabcolor() ........................250

mlabel() ........38–39, 83, 97, 239, 241,

248–250, 300, 303, 327, 344, 353

mlabpos() ..........................248

mlabgap() ..........................250

mlabposition() . . . . 38–39, 239, 241, 331

mlabsize() .............38, 97, 249, 344

mlabvposition() ...............248–249

mlcolor() ...........48–49, 97, 242–243

mlwidth() ...............48–49, 243–244

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

390 Subject index

mountain plots. ........see twoway area

msize() . . . . 37–38, 48–49, 55, 69, 71, 72,

96, 240–241, 246, 341, 352

mstyle() ...................88, 244–246

msymbol() .....22, 26, 36, 39–41, 48–49,

55, 56, 69, 72, 83, 87, 90, 94–96,

236–241, 247, 331, 343, 352

multiple axes ..........see axes, multiple

multiple plots . ............see overlaying

name() ................359, 361, 363–365

naming graphs . . .............see name()

ndots() .............................211

noclockwise ........................219

nofill .............30–33, 117, 162, 198

nofit ................................52

nogrid ..............................355

nolabel..........128, 132, 137, 176, 206

nooutsides.....................157–193

note() ......190–192, 282, 315, 354, 357

options. . . .........................20–29

adding text.................299–303

axes...................254–256, 272

labels.........................22–23

legend........................23–25

marker symbols...............25–27

markers....................235–250

region . .....................324–326

scatterplot matrices ........102–103

specialized graphs ..........352–358

standard...................324–326

textboxes...................303–313

titles . . .......................20–22

using in the wrong context . . . . . . 377

ordering

bars........see bar graphs, ordering

boxes........see box plots, ordering

orientation

textboxes.......................305

titles . ......................341–342

outergap() ....................148, 184

outside values

color . ...........................189

size.............................189

suppressing . ....................180

over() .........29–33, 111–150, 157–235

asyvars ....................161–162

axis() ................130, 142, 173

outergap() .........130, 142, 173

bar graphs........111–123, 151–155

box plots. .........157–162, 193–202

descending ...........119–121, 123,

165–167, 200–202

display only existing variables . . . 117

dot plots . ..................193–202

gap() . . . 118–119, 163–164, 185, 199

label() . . 33, 126–130, 138, 171–173

angle() ............. 33, 127, 171

labcolor()..............128, 172

labgap() ...........129, 172–173

labsize() ...............128, 171

ticks....................129, 172

tlength() ...............129, 172

tlwidth() ...............129, 172

tposition() ............129, 172

missing.........................116

pie charts.......................218

relabel() .......124–126, 168–170,

203–204

sort() . . 120–123, 165–167, 200–202

sum() ........................122

y-variables.................110–111

overlaying..................49–50, 87–94

bar graphs..........see bar graphs,

overlaying

connected marker plots. . . ........56

ﬁts, CIs, smooths, and scatters . . 49–

54, 89–90

histograms . . .........see histogram,

overlaying

kernel density . . . . see kernel density,

overlaying

legends . . see legend, overlaid graphs

lines............see lines, overlaying

markers.....see markers, overlaying

mixed plot types...........6, 25–26

scatterplots ........ see scatterplots,

overlaying

pac..................................350

patterns

axis lines.........................43

box plots . . ..................... 181

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

Subject index 391

patterns, continued

conﬁdence level ..................52

connecting lines ......... 52, 69, 253

lines......82, 181, 208, 211, 336–338

percent()............................76

percent .............................226

percentages...............110–111, 115

pergram .............................350

periodogram . . see pergram and wntestb

pie() ..........................222, 233

color() ........................222

explode ...................222, 233

pie charts................see graph pie

adding text.................227–228

angles .......................... 220

by()............see by(), pie charts

color . ......................221–223

counterclockwise................219

descending order ................220

exploding slices.............222–223

labels..................224–228, 233

legend.................228–231, 234

over() .......see over(), pie charts

slices.......................221–223

sorting ................ 219–221, 233

titles. ...........................229

types.......................217–218

plabel() ..................224–227, 233

color() ........................226

format() .................. 226–227

gap() ......................226–227

legend() .......................227

name ............................233

size() .........................226

plotregion() .......................325

color() ........................325

lcolor() .......................325

lwidth() .......................325

pnorm ...............................346

points, connecting . .......see connecting

points

population pyramid . . ...........367–368

position

labels.........38, 140–142, 226, 301

legend . . . 31, 134–136, 154, 178, 231,

234, 293–294

marker labels...............248–249

standard options ...........330–332

ticks.......................129, 263

position, continued

titles . .......see title(),position

preﬁx...................see titles, preﬁx

ptext() ........................227–228

bfcolor() ......................228

box .............................228

margin() .......................228

orientation() .................228

placement() ................... 228

qladder .............................347

qnorm ...............................346

quadratic ﬁts ......see twoway qfit and

twoway qfitci

r1title() ..........................315

r2title() ..........................315

range() ..........................81, 82

range plots

with area shading . . . ....see twoway

rarea

with bars . . ........see twoway rbar

with capped spikes ......see twoway

rcap

with capped spikes and marker sym-

bols ................ see twoway

rcapsym

with connected lines .....see twoway

rconnected

with lines ........see twoway rline

with markers ............see twoway

rscatter

with spikes ......see twoway rspike

rectangles() .......................212

fcolor() .......................212

lcolor() .......................212

reference lines ........see lines, axes and

yline()

region . . . ....................... 324–326

replace ........................364, 365

rescheming graphs..........see schemes,

rescheming graphs

residual-versus-ﬁt plot......see rvfplot

residual-versus-predictor plot.........see

rvpplot

restoring graphs..........see graph use

reusing graphs..................359–360

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

392 Subject index

reversing axes.....see axes, reverse scale

ROC analysis........................351

roccomp .............................351

rocplot .............................351

roctab ..............................351

rvfplot ........................348, 355

rvpplot .............................348

rwidth()............................212

samples, selecting.....................58

saving()............................358

saving graphs........................358

scale ................................ 182

adjusting...................322–324

axes...............85, 209, 265–269

labels...........................105

markers.........................105

scale()...............103, 323–324, 358

scatter with immediate arguments. . . .see

twoway scatteri

scatter,twoway ...see twoway scatter

scatterplot matrices. ..................12

by()..see by(), scatterplot matrices

displaying lower half............102

jittering. ........................102

options . ....................102–103

scale............................103

scatterplots ........ see twoway scatter

overlaying.............39–40, 84–86

scheme() .......................359–360

schemes..................14–20, 318–321

colors ...........................319

customizing ................379–381

economist .................321, 357

markers.........................247

rescheming named graphs ....... see

scheme()

s1color ........................320

s1manual .......................320

s1mono .........................320

s2color ........................319

s2manual .......................319

s2mono .........................319

sj ..............................321

vg blue ..........................19

vg brite ........................ 19

vg lgndc ........................ 18

vg outc ..........................17

schemes, continued

vg outm ..........................17

vg palec ........................ 16

vg palem ........................ 16

vg past ..........................18

vg rose ..........................18

vg s1c ...........................15

vg s1m ...........................15

vg s2c ...........................15

vg s2m ...........................16

vg samec ........................ 17

vg teal ..........................19

vg lgndc .......................179

separating graphs . ....................44

shading area graphs .................. 62

showyvars ............ 132–133, 176–177

size

adjacent line....................188

adjusting...................322–324

axes........................323–324

caps............................189

labels..............38, 128, 171, 226

marker symbols...............37–38

markers.....55, 69, 96–97, 211–213,

240–241, 340–341

text.............................344

textbox....................144, 304

titles . ......................144, 181

slices ........................... 221–223

sort . . 39–41, 54–56, 61, 65, 84, 220–221,

233–234, 251–253, 333–335

sorting

area graphs.........see area graphs,

sorting

box plots......see box plots, sorting

line plots . .....see line plots, sorting

pie charts.....see pie charts, sorting

spacing.........................see gaps

specialized graphs...............345–358

spike plots . ...........see twoway spike

splines......................see mspline

stack .................111, 115, 122, 139

stacking bars..................see stack

standard error of forecast.......see stdf

standard options . . . .............324–326

standardized normal probability graphs. .

..............346

start() ..............................76

Stata 7 syntax. ......................376

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

Subject index 393

statistical function graphs....see twoway

function

stci ................................349

stcoxkm .............................349

stcurve .............................349

stdf ................................. 51

stepstair......................252, 335

storing graphs...........see graph save

stphplot............................349

strip plots. .................277–278, 371

sts graph......................349, 356

styles...........................327–345

angles......................327–328

clock position ..............330–331

color . ......................328–330

compass direction . .........331–332

lines.............................89

margins....................338–340

marker symbols ............ 342–343

markers.......88, 244–246, 340–343

orientation . ................341–342

text size........................344

subtitle() ...........154, 281–284, 314

nobexpand ......................154

position() ...........154, 283–284

prefix .....................281–282

ring().....................154, 284

suffix .....................281–282

suﬃx....................see titles, suﬃx

sum..................................139

survival graphs......................349

symbols

height .......................... 297

margin .............. see msymbol()

width...........................297

symmetry plots . .....................346

symplot .............................346

t1title() ..........................315

t2title() ..........................315

text

adding..............45, 86, 299–303

legend..........................290

orientation . .....................101

pie charts ..................227–228

scale............................105

size.............................344

text() .............45, 86, 299–313, 356

blwidth() .......................45

box ..............................45

color() ........................304

linegap() ......................309

margin() ...................45, 308

orientation() .................305

placement(). . . . . .301–302, 304–305

size() ......................45, 304

textboxes.......................303–313

annotations . . ...................308

by() ............see by(), textboxes

color . ......................304, 310

interline gaps...................309

justiﬁcation . ...............305–307

margins.........................308

orientation . .....................305

outline . ....................309–310

size........................144, 304

thickness......................see width

ticks

controlling ................. 258–265

labels...........................172

length .......................... 263

matrix . .........................100

position. ........................263

suppressing .....................259

time series . ......................60

time series

labels............................59

line plots . ....................57–60

minor labels ..................... 59

ticks.............................60

titles . ............................59

time-series line. .see tsline and tsrline

tin() ................................58

title() ....20–22, 26, 86, 279, 309–318,

357

bcolor() .......................310

bexpand ........................318

bfcolor() ..................86, 310

blcolor() ..................86, 310

blwidth() ..................86, 310

bmargin() ......................307

box ....21–22, 86, 305–312, 317–318

justification() .....306–307, 318

margin() .................. 339–340

nobox ............................21

placement() ................... 332

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

394 Subject index

title(),continued

position() ................316–317

ring() .........................317

size() .......................21–22

span ............................317

titles . . . .............20–22, 130, 313–318

axes.................see axes, titles

bar graphs.....see bar graphs, titles

box plots. .......see box plots, titles

categorical axes............see axes,

categorical, titles

justiﬁcation ........... see title(),

justification

legend..........................295

matrix . .........................101

multiple lines . ..................316

orientation . ................341–342

pie charts.......................229

placing in a box . . . see title(),box

placing inside a plot region . .....see

title(),placement

position . ....see title(),position

preﬁx.............255, 281–282, 354

size..............see title(),size

suﬃx...........1, 255, 281–282, 354

time series . ......................59

width......................317–318

tlabel() .............................59

tline() ..............................60

tmlabel()............................59

tmtick() .........................54, 60

ttext() ..............................60

orientation() ..................60

ttitle() .............................59

twoway

adding text......................86

by() ..............see by(), twoway

graphs........................35–94

legend ........................... 86

options . ......................82–86

overlaying....................87–94

titles . ........................26, 86

twoway area ...................9, 61–62

twoway bar ...................10, 62–64

twoway connected..........8, 55–56, 88

twoway dot ........................8, 49

twoway dropline ..............7, 48–49

twoway fpfit .....................6, 50

twoway fpfitci ..................50–54

twoway function .................12, 82

twoway histogram . . 11, 75–81, 346, 358

twoway kdensity ........ 12, 80–82, 346

twoway lfit . . 5–6, 25–26, 49–50, 89–91,

93–94, 287–297

twoway lfitci .................7, 50–54

twoway line . . . 8, 54–60, 88–89, 268–269

twoway lowess ....................6, 50

twoway mband.......................6, 7

twoway mspline ...................6, 50

twoway qfit ........6, 50, 90–91, 93–94,

287–299

twoway qfitci ............50–54, 91–92

twoway rarea ......10–11, 66, 70, 92–93

twoway rbar ..............11, 67, 73–74

twoway rcap ..............11, 66, 71–72

twoway rcapsym ..............11, 67, 72

twoway rconnected .......10, 65, 68–69

twoway rline.................10, 65, 70

twoway rscatter .............10, 65, 69

twoway rspike ......11, 67, 72, 265–269

twoway scatter......5–7, 20–26, 35–54,

83–94, 104, 235–313

twoway scatteri .................45–46

twoway spike ......7, 47–48, 92–93, 346

twoway tsline .............9, 57–60, 88

twoway tsrline ...................9, 57

types of graphs.....................4–14

using graphs ............. see graph use

whiskers .........see box plots, whiskers

width

axes............................267

bars..............................64

box plot lines...............185–187

conﬁdence level ..................52

connecting lines . .....52, 54, 69, 253

histogram bars............75, 78, 80

legend..........................294

lines................82, 88, 181, 208

marker outline . .................243

markers..........................69

median line.....................187

symbols.........................297

ticks............................129

titles . . . ....................317–318

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

Subject index 395

width() ...................75–77, 80, 81

wntestb .............................350

xalternate................126, 170, 204

xaxis() ........................270, 303

xcorr ...............................350

xlabel() .........22–23, 42–43, 98–100,

257–258, 260–262, 267, 328

alternate ......................262

angle() ........................262

axis() ......................98–100

format() .......................261

grid ............................264

labsize() .......................23

nogrid .........................264

valuelabels ...................260

xline() ..................see lines, axes

xscale()...............43, 266–268, 364

lwidth() .......................267

xsize() ...................323, 358, 360

xtitle() ......41, 84, 254–256, 354–355

box .............................255

orientation() .................342

prefix .........................255

size().....................255, 355

y-variables

bar graphs..........see bar graphs,

y-variables

multiple . ...................109–111

yalternate................146, 183, 210

yaxis() .........85, 92–93, 256, 270–272

ycommon .............................362

ylabel() . . 31–33, 42–43, 84–86, 98–100,

145–146, 181–182, 209, 257–265,

269, 271–272, 328, 355, 365

angle() . . . 31–33, 145, 182, 261, 328

axis().............98–100, 271–272

glcolor ........................264

glpattern .................264–265

glwidth ........................264

grid....................43, 264–265

labgap() .......................263

labsize() ......................262

nogrid ............43, 146, 182, 264

nolabel ........................260

noticks ........................259

ylabel(),continued

tlength() ......................263

tlwidth() ......................263

tposition() ................... 263

yline() ...........43, 144, 181, 208, 355

lcolor() ..........43, 144, 181, 208

lpattern() .......43, 144, 181, 208

lwidth() ..........43, 144, 181, 208

ymlabel()......................258, 265

glcolor() ......................265

glpattern() ................... 265

grid ............................265

ymtick() .......................259, 263

tposition() ................... 263

yreverse ..................147, 183, 210

yscale() .....85, 92, 138, 146, 182, 209,

268–269

axis() ......................92, 269

range() ...........92, 138, 268–269

ysize() ...................323, 358, 360

ytick() ........................258, 263

tposition() ................... 263

ytitle() . . . 30–33, 41–42, 144, 180–181,

254–256, 272, 354

axis() .........................272

bexpand...............144, 181, 208

bfcolor() ......................208

box ...................144, 181, 208

orientation() .................342

size() .................42, 144, 181

suffix .........................255

ytitle ..............................208

yvaroptions() . . 125–126, 128, 163–167,

169–170, 203–204

label() ........................128

relabel() .......125–126, 169–170,

203–204

The electronic form of this book is solely for direct use at UCLA and only by faculty, students, and staff of UCLA.

published form of the book may be distributed or reproduced, either electronically or in printed form.

Stata Press Publication A Visual Guide To Graphics

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