Discourse Network Analyzer Dna Manual

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Discourse Network Analyzer
Philip Leifeld, Johannes Gruber and Felix Rolf Bossner

Last update: DNA 2.0 beta 20 with rDNA 2.0.2 on 2018-01-11.

Abstract
Discourse Network Analyzer
rDNA (Leifeld 2017) and demonstrates how to install, set up
The Java software DNA is a tool for qualitative content analysis with network export

This document describes the open-source standalone software

(DNA) and its companion
and use them.

package

facilities. It can perform all necessary steps for a discourse network analysis from importing raw
text over annotating statements that persons or organizations make to returning network matrices
of actors connected by shared concepts.

DNA

rDNA

integrates the results from an analysis performed in

with the statistical computing environment

material. Both

DNA

and

rDNA

posted in the issue tracker on

to perform more in-depth analysis on the coded

can be downloaded from

GitHub.

Questions and bug reports can be

Contents
1

Introduction

DNA

algorithms

2.1

Congruence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.2

Conict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.3

Subtract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.4

Ignore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.5

Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.6

Aliation networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.7

Normalization for aliation networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Installation of

DNA

3.1

Windows

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.2

macOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3

Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.4

Installing the programs themselves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

and

rDNA

Using DNA: Preparation of your DNA Workspace

4.1

Creating a new DNA database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1.1

Creating a local DNA le . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1

4.1.2
4.2

User Management: Multiple Coders and Permissions . . . . . . . . . . . . . . . . . . . . 22

4.3

Statement Types and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.4
5

Creating and using a remote database (MySQL) . . . . . . . . . . . . . . . . . . 20

4.3.1

Adjusting the variables of interest . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.3.2

Adjusting the statement types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Final step: Approving your workspace and creating the DNA le . . . . . . . . . . . . . 29

Using DNA: Importing and Organizing your Raw Data

5.1

Opening an existing DNA database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.2

Importing Documents (Raw Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.3

5.2.1

Importing single Documents manually via Copy and Paste . . . . . . . . . . . . . 31

5.2.2

Importing multiple Documents semi-automatically from text les . . . . . . . . . 32

5.2.3

Importing Documents from other DNA databases . . . . . . . . . . . . . . . . . . 35

Organizing documents (Raw Data)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.3.1

Deleting and navigating through documents . . . . . . . . . . . . . . . . . . . . . 39

5.3.2

Editing the documents' meta data (author, time etc...) . . . . . . . . . . . . . . . 39

Using

DNA:

Coding the Data

Using

DNA:

Exporting the coded Data

rDNA:

Using

DNA

from

8.1

Getting started with rDNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

8.2

Retrieving networks and attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Introduction

Section 2 is a concise and fairly technical description of the types of networks DNA can export. Section 3 explains how to install both DNA and rDNA which both rely on a correctly set up Java runtime
environment. Then four sections follow, which describe the usage of DNA in detail: Section 4 describes
how to set up a project in DNA, including the creation of a database, adding and managing users and
how to set up or edit statement types and variables. Section 5 explains how you import and organise
your raw data (i. e. documents). Section 6 and Section 7 will explainonce they are completedhow
material is coded in DNA and how coded data can be experted to other programs for further analysis.
Section 8 is an introductory tutorial on using the rDNA package.

DNA algorithms

Philip Leifeld
This section summarizes the main algorithms implemented in DNA in a technical way.

X is a three-dimensional array representing statement counts. xijk is a specic count value in this array,
with the rst index i denoting an instance of the rst variable (e. g. , organization i), the second index
j denoting an instance of the second variable (e. g. , concept j ), and the third index k denoting a level
on the qualier variable (e. g. , agreement = 1). For example, xijk = 5 could mean that organization i
mentions concept j with intensity k ve times.
Where the qualier variable is binary, false values are represented as 0 and true values as 1 on the
k index, i. e. , K binary = {0; 1}. Where the qualier variable is integer, the respective integer value is
used as the level. This implies that k can take positive or negative values or 0, i.e, K integer ⊆ Z. Note
that all k levels of the scale are included in K , not just those values that are empirically observed.
Indices with a prime denote a second instance of an element, e. g. , i0 may denote another organization.
Y denotes the output matrix to be obtained by applying a transformation to X . The following
transformations are possible:

2.1 Congruence
In a congruence network, the edge weight between nodes i and i0 represents the number of times they
co-support or co-reject second-variable nodes (if a binary qualier is used) or the cumulative similarity
between i and i0 over their assessments of second-variable nodes (in the case of an integer qualier
variable).
In the integer case:


congruence
yii
= Φii0 
0

n XX
X
j=1 k



0
|k − k | 
xijk xi0 jk0 1 −
|K| − 1

(1)

where Φii0 (·) denotes a normalization function (to be specied below).
In the binary case, i. e. , |K| = 2, this reduces to


n X
X
binary
y congruence
= Φii0 
xijk xi0 jk + (1 − xijk )(1 − xi0 jk ) .
0
ii

(2)

j=1 k

2.2 Conict
Binary case:



n X
X
conict binary
yii
= Φii0 
(1 − xijk )xi0 jk + xijk (1 − xi0 jk ) .
0

(3)

j=1 k

More generally, in the integer case:


conict = Φ 0 
yii
0
ii

n XX
X
j=1 k

xijk xi0 jk0



|k − k 0 | 
|K| − 1

(4)

2.3 Subtract
subtract = y congruence − y conict
yii
0
ii0
ii0

(5)

2.4 Ignore

ignore
yii
= Φii0 
0

!!

n
X

j=1

xijk

xi0 jk

(6)



2.5 Normalization
In the simplest case, normalization can be switched o, in which case Φno
ii0 (ω) = ω .
Alternatively, edge weights can be divided by the average activity of nodes i and i0 :

Φavg
ii0 (ω) =

1
2

P
n

j=1

k xijk +

j=1

0
k xi jk

(7)

With Jaccard normalization, we don't just count i's and i0 's activity and sum them up independently,
but we add up both their independent activities and their joint activity, i. e. , both matches and nonmatches:

ΦJaccard
(ω) = Pn
ii0

j=1

xijk [x

i0 jk

= 0] +

j=1

ω
P
k

xi0 jk [xijk = 0] +

j=1

xijk xi0 jk

(8)

With cosine normalization, we take the product in the denominator:

ω
qP
.
Φcosine
(ω) = q P
ii0
P
P
n
n
( j=1 k xijk )2 ( j=1 k xi0 jk )2

(9)

2.6 Aliation networks
Ignoring the qualier variable:
aliation ignore

yij

!
= Φij

(10)

xijk

Subtracting negative from positive ties (integer case):

aliation subtract binary

yij

= Φij

(11)

k · xijk

Subtracting negative from positive ties (binary case):
aliation subtract binary

yij

!
= Φij

(k · xijk − (1 − k) · xijk )

(12)

Note that the binary case is

not

merely a special case of the weighted aliation network in this case.
5

2.7 Normalization for aliation networks
With

activity

normalization, ties from active nodes receive lower weights:

(ω) = Pn
Φactivity
ij

j=1

With

prominence

ω
P

xijk

(13)

normalization, ties to prominent nodes receive lower weights:

ω
Φprominence
(ω) = Pm P
ij
i=1

xijk

(14)

Installation of DNA and rDNA

Johannes Gruber
This section explains how DNA and rDNA can be installed on common desktop operating systems. As
DNA is written in Java, both DNA and rDNA rely on Java to work on your computer properly. Installing
and conguring a valid Java Runtime Environment on your machine will thus be the rst and only
complicated step of the installation. However, following the simple steps below, one should not run
into problems while setting up Java. The advantage of the Java programming language for academic
software is that it both runs on dierent operating systems without altering the source codeonce the
Runtime Environment is set upand that it isfor the most partopen source. Besides setting up
the Java Runtime Environment, the installation of DNA and rDNA is identical on dierent operating
systems. If you feel condent that Java is already correctly set up on your computer, you can therefore
skip to Section 3.4 if you like. Otherwise please continue to the section for the operating system you
wish to install DNA and rDNA on: Windows, macOS or Linux.
For more experienced users, here is a short version of the steps described below:
1. (On Mac: install Apple's legacy version of Javaeven though we will never use it. )
2. Install Java Runtime Environment (JRE) (Version 8) on your computer.
3. (On Windows and Mac: set up the JAVA_HOME to the installation path of your JRE.)
4. Download the newest executable JAR from github.com/leifeld/dna/releases.
5. (On Linux: make the JAR le executable.)
(On Mac: allow excetuting apps from an unidentied developer.)
6. You can now run the standalone DNA or continue to install rDNA as well.
7. Download and install R (and RStudio).
8. In R: install the necessary R packages rJava and devtools.
9. In R: install rDNA via

devtools::install_github("leifeld/dna/rDNA",
args = "--no-multiarch")

3.1 Windows
To install the necessary Java Runtime Environment on your Windows computer, simply go to
java.com/en/download/manual.jsp, scroll down to and download Windows Oine (64-bit) (see
Figure 1; download Windows Oine instead if you are using a 32-bit version of Windows). During
the installation, you can accept all the default options, including the installation path.
Next, you should set JAVA_HOME in your environmental variables to tell your Windows PC
where your Java installation lives. This step is optional, but can prevent many issues with Java,
people had in the past. To set JAVA_HOME, you need to navigate to the menu edit the system
button on your keyboard
environment variables . The easiest way to get there is to hit the
and enter environment. Windows will then search for programs and settings menus which include

Figure 1: Downloading JRE from Oracle
this title and should usually display the menu we are looking for on top.1 In this menu you have to
nd the button Environment variables... . Clicking this button should open the window shown in
Figure 2.
Under User Variables, click New.2 Enter the variable name JAVA_HOME and the path to your
java installation in the eld Variable value . If you haven't altered the default install location,
you should nd Java in "C:\Program Files\Java\jre1.8.0_151" or if you chose to install a 32bit version of Java in "C:\Program Files (x86)\Java\jre1.8.0_151" (which will cause problems
though if you try to use it with a 64-bit version of R).
Windows should now recognise Java and be able to run Java commands. To test this, we can open
the Command Prompt (press the
button on your keyboard and simply enter cmd and then hit
Enter) and type a Java command, e. g. java -version. If the installation was successful, the
output should display information about the Java-version and build as depicted in Figure 3.
After installing Java, you are ready to use DNA and could skip to Section 3.4 if you are not interested in
installing rDNA as well. In order to use rDNA the rest of this section will explain how to install R and a
recommended integrated development environment (IDE) called RStudio, which makes working with
R a lot easier and also looks a lot better than the default interface.
Install

on Windows

1. First, you need to download R from cran.r-project.org/bin/windows/base/.
2. On the top of the page click on Download R 3.4.3 for Windows (or a newer version if
available).
1 On

older versions of Windows, this might not work.

Computer and select Properties

2 This

→ Advanced.

On Windows 7 you can alternatively right-click on My

On Windows 8 Control Panel

sets JAVA_HOME just for the current user. If you want to make

you are working on, you can create a System Variable instead.

→ System → Advanced System Settings.

Java available for all users on the computer

Figure 2: Edit JAVA_HOME to tell Windows where your Java lives

Figure 3: Testing Java installation in Windows Command Prompt

3. Install the downloaded le, e. g. R-3.4.3-win.exe. Usually, it is ne to leave all default settings
in the installation options.
4. Go to rstudio.com/products/rstudio/download/.
5. At the bottom of the page, under Installers for Supported Platforms, click on the link RStudio 1.1.383 - Windows Vista/7/8/10 (or a newer version if available). Again the default
installation options are ne in most cases and can be accepted unchanged.
6. After installation, you can use R by opening RStudio.
Traditionally, the rst test you perform in a new programming language is to write a Hello, World!
program. To do this in R, you simply type print(Hello World!) in the Console (the window
which covers the left half of RStudio ). Alternatively, you can make R perform a simple mathematical
operation. If everything is set up correctly, the output should look like this:

print("Hello World!")
## [1] "Hello World!"
# You can also use R as a calculator
2 * 3
## [1] 6
The chunk of code above marks the rst time we are using R commands in this manual. It might be
worth, to explain what this means for users who are not familiar with documents which contain R-code.
Whenever code is shown in this manual it is decorated with a light grey background. Comments in
R-code (i. e. text targeted at the user to explain what is happening in a specic line) are marked
with a #, are formatted in italic font and in dark grey. The output, which is generated by running
a command, is marked by two # and formatted in black. This means that every line which does not
start with ## contains R-code which you can copy and paste to the Console in RStudio and run.
Alternnativly, you can also copy the code to an R script and execute it by either clicking on this button
on the upper right of RStudio, near the corner or you can use the shortcut Ctrl+Enter. Both
ways, the highlighted code or the line in which the caret is currently ashing are sent to the console
and executed. If this works ne, you should be able to continue to the next section which describes
Installing the programs themselves.

3.2 macOS
On macOS, you have to install two versions of Java in order for rDNA to work properly. The reasons
behind this are too complicated to cover here, but basically, Apple built its own version of Java, which
needs to be on your machine, even though it is outdated. Therefore we need to rst install the legacy
Java 6which we will never usebefore installing the correct Java Runtime Environment version 8.3
First, please download the le support.apple.com/downloads/DL1572/en_US/javaforosx.dmg and install it, accepting all defaults. After this has nished, we can proceed to get the new version of the
Java Runtime Environment. Go to java.com/en/download/manual.jsp and scroll down to download
3 If

rDNA or any other R package which relies on Java, you might not need both versions
Java Runtime Environment. However, installing Java version 8 before the legacy Java

you do not wish to ever use

and can just download the newest

will cause problems if you'll ever change your mind.

Figure 4: Downloading JRE from Oracle
Mac OS X (10.7.3 version and above)

defaults.

(see Figure 4). Again, install the program accepting all

After installing Java, you are ready to use DNA and could skip to Section 3.4 if you are not interested
in installing rDNA as well. In order to use rDNA the rest of this section will explain how to install R
and a recommended integrated development environment (IDE) called RStudio, which makes working
with R a lot easier and also looks a lot better than R's default interface.
install

on Mac

1. First, you need to download R from cran.r-project.org/bin/macosx/.
2. On the top of the page click on R-3.4.3.pkg (or a newer version if available).
3. Install the downloaded le. Usually, it is ne to leave all default settings in the installation
options.
4. Go to rstudio.com/products/rstudio/download/.
5. At the bottom of the page, under Installers for Supported Platforms, click on the link RStudio
1.1.383 - Mac OS X 10.6+ (64-bit) (or a newer version if available). Again the default
installation options are ne in most cases and can be accepted unchanged.
6. Then you need to install the program Xcode from the app store.
7. After installation, you can use R by opening RStudio.
Traditionally, the rst test you perform in a new programming language is to write a Hello, World!
program. To do this in R, you simply type print(Hello World!) in the Console (the window
11

which covers the left half of RStudio ). Alternatively, you can make R perform a simple mathematical
operation. If everything is set up correctly, the output should look like this:

print("Hello World!")
## [1] "Hello World!"
# You can also use R as a calculator
2 * 3
## [1] 6
The chunk of code above marks the rst time we are using R commands in this manual. It might be
worth, to explain what this means for users who are not familiar with documents which contain R-code.
Whenever code is shown in this manual it is decorated with a light grey background. Comments in
R-code (i. e. text targeted at the user to explain what is happening in a specic line) are marked
with a #, are formatted in italic font and in dark grey. The output, which is generated by running
a command, is marked by two # and formatted in black. This means that every line which does not
start with either # or ## contains R-code which you can copy and paste to the Console in RStudio
and run. Alternatively, you can also copy the code to an R script and execute it by either clicking
on this button
on the upper right of RStudio, near the corner or you can use the shortcut
Ctrl+Enter. Both ways, the highlighted code or the line in which the caret is currently ashing are
sent to the console and executed.
Now unfortunatly, working with Java from within R on a Mac is a bit messy. Apple's own version of
Java, although important to have installed, does not run in combination with R. That is why we have
to tell your system which version of Java to use by default. To do this, we have to enter a few system
commands, which you can either do in the Terminal app or directly from within R using the system
function:

# list files in java_home
system("/usr/libexec/java_home -V")
##Matching Java Virtual Machines (3):
##
1.8.0_60, x86_64: "Java SE 8" /Library/Java/JavaVirtualMachines/jdk1.8.0_60...
##
1.6.0_65-b14-468, x86_64: "Java SE 6" /Library/Java/JavaVirtualMachines/1.6...
##
1.6.0_65-b14-468, i386: "Java SE 6" /Library/Java/JavaVirtualMachines/1.6.0...
# see default version of Java
system("java -version")
##java version "1.8.0_60"
##Java(TM) SE Runtime Environment (build 1.8.0_60-b27)
##Java HotSpot(TM) 64-Bit Server VM (build 25.60-b23, mixed mode)
If your output looks like the above, you are ready to install rJava. If the rst command does not
show 1.8.0_60, x86_64 (or any other version staring with 1.8.), you need to install Java version 8
again (see above ) and possibly reboot your computer. If the second command shows java version
"1.6.0_65", but version 1.8 is listed in the output from the rst command, you can set the default
by excecuting the following command:

# Set JAVA_HOME
system("export JAVA_HOME=`/usr/libexec/java_home -v 1.8`")
After that, you should be able to continue to the next section which describes Installing the programs
themselves.

3.3 Linux
Since you are using Linux, we assume that you are suciently comfortable with using the command
line. Therefore, we only provide the necessary steps for installing Java as commands.
First check if Java might already be installed:

$java -version
If not, install it, e. g. via apt-get:

$sudo apt-get install default-jre
Optional: You can also install the Java development kit at this point, which is sometimes recommended
for working with R and Java.

$sudo apt-get install default-jdk
After installing Java, you are ready to use DNA and could skip to Section 3.4 if you are not interested
in installing rDNA as well. In order to use rDNA the rest of this section will explain how to install R
and a recommended integrated development environment (IDE) called RStudio, which makes working
with R a lot easier and also looks a better than the default GUI.
install

on Linux

1. Since the version of R on the default repositories tends to be fairly outdated, we add the repository
of the Comprehensive R Archive Network (CRAN) to the sources.list:

$sudo echo "deb https://cran.rstudio.com/bin/linux/ubuntu/artful/"
$sudo tee -a /etc/apt/sources.list
Note, that you need to replace /ubuntu/artful/ with your avour and version of
Linux. Visit CRAN to see which ones are available

2. Next, you need to add R to your keyring. Seen below is how you would accomplish that in
Ubuntu:

$gpg --keyserver keyserver.ubuntu.com --recv-key E084DAB9
$gpg -a --export E084DAB9 | sudo apt-key add 3. Update apt and install R (and r-base-dev if you wish to compile packages from source):

$sudo apt-get update
$sudo apt-get install r-base
$sudo apt-get install r-base-dev
4. Now install RStudio via gdebi (and install gdebi rst if you don't already have it)4 :

$sudo apt-get install gdebi-core
$wget https://download1.rstudio.org/rstudio-1.1.414-amd64.deb
$sudo gdebi -n rstudio-0.99.896-amd64.deb
$rm rstudio-1.1.414-amd64.deb
5. After the installation has nished, you can use R by opening RStudio.
Traditionally, the rst test you perform in a new programming language is to write a Hello, World!
program. To do this in R, you simply type print(Hello World!) in the Console (the window
which covers the left half of RStudio ). Alternatively, you can make R perform a simple mathematical
operation. If everything is set up correctly, the output should look like this:

print("Hello World!")
## [1] "Hello World!"
# You can also use R as a calculator
2 * 3
## [1] 6
The chunk of code above marks the rst time we are using R commands in this manual. Since it looks
similar to the terminal commands we used above, you probably have no problem reading it. But just
in case, it might be worth to explain what you see there: whenever code is shown in this manual it is
decorated with a light grey background. Comments in R-code (i. e. text targeted at the user to explain
what is happening in a specic line) are marked with a #, are formatted in italic font and in dark
grey. The output, which is generated by running a command, is marked by two # and formatted in
black. This means that every line which does not start with ## contains R-code which you can copy
and paste to the Console in RStudio and run. Alternnativly, you can also copy the code to an R script
and execute it by either clicking on this button
on the upper right of RStudio, near the corner
or you can use the shortcut Ctrl+Enter. Both ways, the highlighted code or the line in which the
caret is currently ashing are sent to the console and executed.
Now before we can actually run rDNA, we need to associate Java with R. To do this, you can either go
back to the terminal, or you can invoke a system command directly from within R using the system
function:

$sudo R CMD javareconf
Or:
4 alternativly,

you can download an installation le from rstudio.com/products/rstudio/download/.

Figure 5: Download DNA jar le from GitHub releases page

system("sudo R CMD javareconf")
After this is nished, you are now set to start installing DNA and the rDNA-package themselves.

3.4 Installing the programs themselves
Once Java is set up correctly, you can simply download the latest version of DNA as a JAR le from
github.com/leifeld/dna/releases (see Figure 5). JAR or .jar les are technically archive les which
usually contain a computer program written in Java, along with all the pictures and libraries necessary
to run the program. Once the download is nished, you can start the program by double-clicking on
the downloaded le. However, on Linux, it is sometimes necessary to make the le executable rst
(e. g. via $chmod +x /path/to/your/dna.jar or using a GUI-method). On newer version of macOS,
a program from an "unidentied developer" (i. e., if the program has not been registered with apple)
needs to be made a security exception before you can run it. To do so for DNA control-click the
program's icon, then choose Open from the shortcut menu. If clicking on the le does not open the
program on a windows machine, right-click on the .jar le → Open with → Use another app and
then navigate to the le ``C:\Program Files\Java\jre1.8.0_151\bin\javaw.exe''.
If you are not interested in using rDNA, you can now skip to the next section.
At this point, I assume that you have installed R and have at least a minimal understanding of how
the program works. If that is not the case, you might want to jump back to where we explain how to
install Install R on Windows, install R on Mac or install R on Linux. If you have already done this,
we can go ahead and install rDNA from within R. First, we need to install the package rJava (Urbanek
2016), which is the most important dependency of rDNA:

install.packages("rJava")
To see if this worked, or to troubleshoot potential problems, we can run a couple of Javacommands
from within R:

library("rJava")
# 1. initialize JVM
.jinit()
# 2. retrieve the Java-version
.jcall("java/lang/System", "S", "getProperty", "java.version")
## [1] "1.8.0_151"
# 3. retrieve JAVA_HOME location
.jcall("java/lang/System", "S", "getProperty", "java.home")
## [1] "C:\\Program Files\\Java\\jre1.8.0_151"
# 4. retrieve Java architecture
.jcall("java/lang/System", "S", "getProperty", "sun.arch.data.model")
## [1] "64"
# 5. retreive architecture of OS (This should have 64 in it if step 4 displays
# "64")
.jcall("java/lang/System", "S", "getProperty", "os.arch")
## [1] "amd64"
# 6. retrieve architecture of R as well (This should again have 64 in it if step
# 4 and 5 display 64)
R.Version()$arch
## [1] "x86_64"
Now what you want to make sure, in case something is not working correctly with rJava, is if the
architectures of Java, your operating system and your version of R match (see comments 4., 5., and
6. in above's code chunk ).
Once this is done, you should install the package devtools (Wickham and Chang 2016), which permits
installing R packages from GitHub.

install.packages("devtools")
Since we only need one function from the package devtools at this point, it is not necessary to invoke
the library command to load the whole package. Instead you can write devtools:: and then type

the function you want to use.5

devtools::install_github("leifeld/dna/rDNA",
args = "--no-multiarch")
After this is done as well, the nal step of the installation is to test if rDNA can be loaded into R
correctly and to perform a basic operation with itopening DNA from within R. In order to do so,
you rst need to download DNA, which can also be done in R with the download.file command (see
Section 8 for more information about this code chunk ).

# download two files necessary to test rDNA
download.file(
"https://github.com/leifeld/dna/releases/download/v2.0-beta.19/dna-2.0-beta19.jar",
destfile = "dna-2.0-beta19.jar", mode = "wb")
# download DNA jar
download.file(
"https://github.com/leifeld/dna/releases/download/v2.0-beta.19/sample.dna",
destfile = "sample.dna", mode = "wb")
# download sample file
# load library
library("rDNA")
# initialise the file you just downloaded
dna_init("dna-2.0-beta19.jar")
# start up DNA from R with the sample file to see if everything worked
dna_gui(infile = "sample.dna")
If these commands can be executed correclty, you are ready and set to use both DNA and rDNAHow you
can do so will be described in the rest of this manual.

5 The

option

be necessary, but prevents errors on some operating

systems.

Since

version of a package during installation, the process

inevitably fails

args = "no-multiarch" should normally not
devtools tries to test both 32-bit and 64-bit
as only one architecture of Java is available.

Using DNA: Preparation of your DNA Workspace

Felix Rolf Bossner and Johannes Gruber
After installing the program (see Section 3), you can now create your rst DNA database for your own
research project. How you set up a DNA database will mainly depend on the needs of your personal
research designwhich should usually be clear before you start analysing data. Therefore, DNA can be
customised during the creation of a new database in accordance with how you are planning to use the
tool.

4.1 Creating a new DNA database
In order to create a new DNA database le, you have to click on the index tab File (in the upper left
corner of your DNA program window) and select the option New DNA database (see Figure 6).
As a result, a new window will open (see Figure 7), in which you nd a menu that provides you with
a step-by-step guidance for specifying the conguration of your personal DNA database
Clicking on the rst tab in the sidebar of this menuDatabase (see Figure 7)opens a menu,
which allows you to choose the le name and storage location of your database. For this rst step of
your set-up, DNA provides you with two options in respect to the type of database, in which your data
is stored. Which of these options best ts your research project is dependent on the circumstances of
your coding process:
The preset option Local .dna le means, that the dataset is stored in a local le

6 on your PC or device. This le, with the le extension .dna, can be moved on your

machine, sent via email, uploaded and shared via a cloud le hosting servicesuch as
Dropboxand can generally be treated in the same way as any other le PC users are
familiar with. A local .dna le will be sucient in most user scenarios, for example, if
you employ a single coder working on a single computer, if multiple coders work on a
single dataset at non-overlapping intervals or when multiple coders work at the same time
on dierent datasets, which you merge after the coding process (see Section 5.2.3). For
most users, this simpler option will adequate in order to use DNAt is not necessary to be
familiar with setting up and managing an SQLite or MySQL database. If you think, the
scenarios described above cover your intended use of DNA, you can now jump to the next
section and start Creating a local DNA le.
However, for more experienced user or research projects in which several coders want
to work on the same database at the same time, a second option was included into DNA
Remote database on a server . This stores your data in a MySQL database which
could be stored locally on your machinewhich would defy the purpose thoughon a
private seversuch as a Network-attached storage (NAS)or on an online Cloud server.
You should select this option if you employ a single coder working on multiple devices
or multiple coders working on a single dataset at the same time. The preconditions for
using this type of storage are that all coders have a stable connection to the database
during the coding processe. g. via the internetand that you set up an online MySQL
database in advance. If this is how you want to proceed, you can now jump directly to
the section which descibes the necessary steps for Creating and using a remote database
(MySQL).
6 technically

an SQLite le.

Figure 6: Starting a new Database

Figure 7: Choose if database will be stored locally or remotly

Figure 8: Choose location of database window
4.1.1

Creating a local DNA le

1. Click on the button Browse (see
in Figure 8should be open.

Figure 7).

Now a pop-up menusimiliar to the one shown

2. In this pop-up menu, you can choose the storage location of your database on your local device
from the Save in slide down menu. Enter the name of your database in the eld File Name
and conrm your choices by pressing the Save button (see Figure 8). Now the pop-up menu
will close.
3. Next, it is important, that you conrm your choices again by pressing the Apply
button (see Figure 9). If you forget to press this button, you cannot create the database in the
nal step, because the program will report No database selected (see Figure 19).
If you just employ a single coder and don´t want to change or supplement the preset standard research
variables (person, organization, concept, agreement) or types of codeable statements (Statement, Annotation), you can now proceed directly to the nal step. If you use this manual as a
beginner´s tutorial for working with DNA, however, it would be helpful to follow the steps outlined
in sections 4.2 and 4.3 in order to gain a better understanding of the DNA's potential uses and its
functions.
4.1.2

Creating and using a remote database (MySQL)

Before you can congure DNA for working with a remote MySQL database, it is necessary to execute
at least three basic operations in MySQL (see Figure 10).7
1. You have to create a database on your MySQL server (usually by the command CREATE DATABASE
'DatabaseName')
7 For

a detailed introduction to database management with MySQL see dev.mysql.com/doc/mysql-getting-started.

Figure 9: Apply database choice

Figure 10: Create MySQL database

2. As you probably don´t want to allow all coders access to all other databases stored on your
MySQL server, you should create distinct user prole(s) for the coding process of your DNA
project. Even if DNA itself allows for managing multiple dierent coder roles, we recommend to
create separate user proles for each of the individual codersespecially if they simultaneously
edit the content of your database. It is also advisable to create passwords for the access to your
database, not only for safety reasons, but also because DNA sometimes has problems with signing
in users without a password. Consequently you would use the CREATE USER 'Username'@'%'
IDENTIFIED BY 'Password' Command. It should be noted, thatif necessaryin this step you
can restrict the respective users access to your database to a specic device (by replacing '%'
through a particular server address).
3. Finally you, have to equip the users with the necessary rights to edit your database. For this, you
use the GRANT ALL PRIVILEGES ON Databasename.* TO 'Username'@'%' command, because
you can specify distinct user roles and rights with DNA itself in the next step, which is specically
designed for discourse network-analytical coding purposes.
Once the MySQL database is set up, you only have to select the option Remote database on a
server in the rst tab of the sidebar menu Database in DNA (see Creating a new DNA database)
and enter the respective username and password created in the previous step in the respective elds
User and Password as well as to specify the server address of the database, with which you
want to connect, in the eld mysql:// . If you want to access the database remotely from another
device, you have to indicate the URL or IP-address of your host server, the port (which is 3306 in
default, but can be congured manually) and the name of your database in the format Hostserveraddress:Port/Databasename . If you use DNA on the device hosting the database you can instead
use the conguration shown in Figure 11 (localhost/Databasename ). By clicking the button
Check you can now check if DNA is able to connect to your database. If this is successful, you will
receive the message Ok. Tables will be created (see Figure 11) ; if not, DNA will report Error: Connection could not be established . In case of the latter, you should check the validity
of your server address, username and password andif necessaryrepeat the steps outlined above.
It should be noted thatfor security reasonsMySQL doesn´t allow remote access with the root
superuser-prole in most cases. Similar to the generation of a local .dna le, it is nally important,
that you conrm your choices again by pressing the Apply button (see Figure 11). If you forget
to press this button, you cannot create the database in the nal step, because the program will report
No database selected (see Figure 19).

4.2 User Management: Multiple Coders and Permissions
This second step of preparing your DNA workspace allows you to generate multiple user identities
with dierent sets of rights for dierent coders. Thus, you can specify for each coder, which parts of
the dataset each user can see or edit and thereby pre-structure your coding and research process. In
order to do so, click on second tab Coder in the sidebar of the Create new database menu (see
Figure 12).
In the main window (see Figure 12) you can now see a list with all coders and how many of the 12
possible actions they are permitted to perform. Now you can either add a new user prole by clicking
the Add button (see Figure 12) or select an existing coder and adjust her/his users rights by clicking
on the user and then on the Edit button (see Figure 15). Both options will open the pop-up menu
shown in (see Figure 13).
This pop-up menu allows you to congure an individual prole for each coder in three simple steps:
1. You can choose the colour for the coder (see Figure 13, step 1 ). It is recommended to choose
dierentif possibledivergent colours for each coder, because this permits you to detect at
22

Figure 11: Connecting to local MySQL database

Figure 12: Adding a second coder to the database

Figure 13: Conguring coder permissions

Figure 14: Change coder identity
the rst glance, which user coded which statement, as every coded statement is marked in the
individual colour of its respective coder (see middle column of Figure 14).
2. You can enter the preferred name of each coder in the eld Name . If possible with respect
to data protection rules, it is recommended to use the real names of the coders. This makes it
easier for them to select their prole (in the upper left of the main program window) the rst
time they start the program (see Figure 14).
3. The nal step allows you to congure the permissions of each coder individually by (de)selecting
the respective rights via a click (see Figure 13, step 3 ). Each new user has all of the 12 congurable permissions in the preset mode. Which parts of the dataset an individual coder should be
able to see or edit, should depend on your coding process. For better orientation a few practical
implications of the 12 congurable permissions are listed in Table ?? . Please keep in mind,
that every user can see and change to other user identities either accidentally or because of
non-compliance, as s/he has to select her/his role the rst time s/he starts the program and can
change her/his role anytime (see above and Figure 14)
Finally you approve your choices by clicking the OK button (see
24

4, Figure 13).

It is possible to change

Figure 15: Edit coder details
the settings either in the new database menu by selecting the respective user and clicking the Edit
button (see Figure 14) or changing the coder settings in the main menu.
Table 1: User permissions explained
Permission

add documents
import documents
delete documents

edit documents

view others' documents

Practical Implication

The user can add new documents (i. e., raw data) manually (via copy and
paste or retyping) to the database ⇒ user has (also) a research function.
The user can import new documents from other sources like .txt or other
.dna les to the database or recode the metadata of multiple documents ⇒
user has (also) a research function.
The user can delete documents from the database or dataset. This option
requires at least the other permission view others' documents if the user
has an organizing or editing function (structuring database for coding by
other users) or the permission add documents and add statements if the
coder determines own codes and organizes her/his own set of data.
The user can edit her/his own documents (i. e., raw data), but not necessarily the codings in these documents that were made by other userswhich
would require the permission edit others' statementsor the documents
uploaded by other userswhich requires the permission edit others' documents. This option requires at least the other permission add documents
or import documents and should be selected if the user determines own
codes and organizes her/his own set of data or acts as a researcher for the
other coders.
The user can view the documents uploaded by other users. This option is
necessary for a collaborative coding process in which only a part of the users
selects and uploads the raw data (i. e., documents) for all other users. The
option should not be selected if each coder comes up with own codes and
organizes her/his own set of data.

Table 1: User permissions explained
Permission

edit others' documents
add statements

view others' statements

edit others' statements

add coders
edit statement types

edit regex settings

Practical Implication

The user can edit the documents uploaded by other users. This option
requires at least the other permission view others' documents and should
be selected if a user organizes or edits the raw data provided by other users.
The coder actually codes the data by creating and editing statements. If
only a part of the users select and upload the raw data this option requires
the additional permission view others' documents. If the coder suggests
own codes and organizes her/his own set of data this option requires either
the additional permission add documents or import documents .
The coder can view the statements coded by other users. For example the
Coder DNA User would not see the yellow statement of the Coder Admin
in Figure 14 if this option was deselected for her/his user role. This option
should be de-selected if you want to establish a blind coding process.
The coder can edit or correct the statements coded by other users. This
option requires at least the other permission view others' statements and
should only be selected for few users with an organizing, controlling or editing
function.
The user can add new coders (see Section 4.2). This option should only be
selected for few users with an organizing function.
The user can change or complement the variables of interest (see Section 4.3). This option should only be selected for very few users or the
researchers themselves because possible adjustment of these variables is
usually only necessary in cases when the research design and/or research
questions change fundamentally.
The user can specify keywords which are highlighted in the text, along with
a text color. For example, in Figure 14 the word colors is highlighted in
the raw data text (middle column), because it was specied as a keyword
in the regex highlighter sidebar in the bottom left of the DNA window. If a
user does not have the right to edit the regex setting, the buttons Add and
Remove in this highlighter would be hidden, but the keyword would nevertheless be visibly highlighted in the text and listed in the regex highlighter
sidebar. Thus, if you specify a distinct set of theory based keywords in advance in order to render the coding procedure semi-automatic, you should
not enable this option or select it only for few users, as the respective coder
could change the keywords. However, if you don´t have a theoretically relevant set of keywords in advance or just specify them as a assistance for your
coders, you can allow them to formulate such keywords by themselves.

4.3 Statement Types and Variables
Clicking on the third tab in the sidebar of the Create new database menuStatement Types
(see Figure 16)opens a menu, which allows you to adjust or supplement either the variables or the
types of statements, which your coders derive from the raw data.

Figure 16: Edit Statement Types
4.3.1

Adjusting the variables of interest

The statement type DNA Statement represents a text portion of your raw data, where an actor
reveals her/his opinion/belief/etc. about an issue. Thus, the main task of your coder(s) is to identify
such text portions and gain the relevant data about the actor or his opinion/belief/etc. Your research
question or theory should not only dictate what kind of information should be coded as statements,
but also which relevant variables of this information should be captured by the coder. As you can see
in the Statement Types menu, DNAs default conguration allows capturing four variables. Selecting
DNA Statement and clicking on the button Edit (see Figure 16) opens a pop-up window (see
Figure 17), which reveals the nature of this four precongured variables, along whose lines the coders
can collect information:
the person who makes the statement.
the organization the speaker is aliated with.
the concept (opinion/belief/etc.) which is raised by the actor.
a dummy variable indicating whether the actor agrees with the concept or not.
Furthermore the pop-up window depicted in Figure 17 shows, that each variable is assigned to a
specic data type: While person, organization and conceptaccording to their nature as nominal
variableswill be coded by a short text, agreement as a dichotomous variable will be coded as a
boolean data type , which accordingly only allows for two forms (either agreement or non-agreement).
Neither the data type nor the name of the variables can be changed directly. However by selecting a
variable and clicking on the trash symbol (on the right side of the Add Variable button, Figure 17,
step 4) you can delete a variable and subsequently replace it by a new one. Generating a new variable
either to replace one of the precongured variables or because you are interested in an additional or a
dierent set of variablesis possible in ve simple steps:
1. You have to select an existing variable in order to activate the variable menu (see
ure 17).

1, Fig-

Figure 17: Edit Statement Type details
2. Now you can enter the name of the new variable in the text eld at the bottom of the pop-up
window (see 2, Figure 17). For example, in Figure 17 we are interested in collecting the age of
the person who makes the statement. Please note, that DNA does not allow spaces in variable
names. Putting a space in the variable name will disable the Add Variable button necessary
for step 4.
3. Now you can choose the data type of your variable by clicking on one of the four options.
In our example, we choose the option integer, as the age of a person is neither a nominal nor a
dichotomous variable, but an integer number) (see Figure 17, step 3 ).
4. You have to click on the Add-Variable button, which has the form of a green plus symbol
(see 4, Figure 17). If this button is disabled, you probably did not select a existing variable (step
1) or have a space in your variable name (see step 2).
5. Click the OK button to conrm your choices (see

Figure 17, step 5 ).

Please note, thatfor the statement type DNA Statementyou should only specify variables, in
which you have an actual research interest in and that accordingly have to be coded for all statements
by all coders. If you are interested in additional and optional information about some statements, you
can specify them as variables of the other precongured statement typeAnnotation .
4.3.2

Adjusting the statement types

There are very few research scenarios, in which it is necessary to complement the two existing types of
statements with further ones or with an adjustment of type DNA statement. One of them would be,
28

Figure 18: Summary of your about to be created DNA database
if you study two parallel yet dierent research questions, which employ the same dataset and the same
coders at the same time. In this case, you could rst rename the statement type DNA Statement
by selecting it from the statement type menu, clicking the Edit button (see Figure 16), entering
the new name (in this case: Statement for Research Project 1) in the text eld on top of the
pop-up window (see Figure 17) and pressing the OK button (see 5, Figure 17). Subsequently you
would open a new pop-up window by clicking on the Add button in the statement type menu
(left button in Figure 16). Then name the new statement type (in this case: Statement for Research
Project 2) in the text eld on top of the pop-up window and choose a color (dierent from the other
type) by clicking on the colored button next to this text eld. Then you also need to specify the
relevant variables synchronous to the procedure depicted in Section 4.3.1. However, please evaluate
carefully, if it is really neccesary for your second research interest that you specify a second statement
type or if it would be possible to either conceptualize it as a variable of the existing statement type or
study it sequentially or with a dierent set of coders (and therefore in a dierent DNA dataset). More
than two statement types (besides Statement and Annotation) can cause a confusion
of the coders and therefore compromise the validity of the coding procedure.

4.4 Final step: Approving your workspace and creating the DNA le
Finally, clicking on the last tab in the sidebar of the Create new database menuSummary
provides you with a summary of your choices in respect to the conguration of your coding process
(see Figure 18). After controlling each of the three information you can now create your database
by clicking on the Create database button. If this button is disabled and you get the error No
database selected (see Figure 19), you probably forgot to click the Apply button after specifying
your database (see Section 4.1.1, step 3 ). After creating the database, the new database will open in
the main DNA window (see Figure 6) and you can proceed towards loading up and organizing the raw
data.

Figure 19: No databse selected (e. g. if choice was not applied)

Using DNA: Importing and Organizing your Raw Data

Felix Rolf Bossner
This section describes how to upload and organize your research project's raw datai. e. the text
les (newspaper articles, press releases etc.) containing the uncoded statementsin DNA. First it will
be layed out how you open an existing databaseeither locally or from a remote location. Then you
will learn how to import new documenst into DNAeither by importing one document at a time or by
selecting mutliple documents for import. Finally, we tell you how you can organise the documents in
your database and how you can change your docuemtns' metadata.

5.1 Opening an existing DNA database
First of all, you have to choose, in which DNA Database you want to upload and process your data. To
open a DNAdatabase, simply follow the steps depicted in Figure 1: First, click on the index tab File
and select the option Open DNA database (see Figure 20, step 1 ). As a result, a pop-up window
will appear, which allows you to choose between opening a Local .dna le or a remote database
on a server . If your database is stored on a remote server, you should choose the second option and
repeat the procedure outlined in Creating and using a remote database (MySQL). If your dataset is
stored in a folder on your local PC or device, you can proceed with the preset option and click on the
button Browse (see Figure 20, step 2 ), which will open a further pop-up window, in which you can
nd your database by choosing its storage location from the Save in slide down menu (see step 3 ),
selecting the respective database (see step 4 ) and clicking on the button Open both in the pop-up
and the Open existing database... window (see steps 5 and 6 ).

Figure 20: Opene DNAdatabase

5.2 Importing Documents (Raw Data)
There are four dierentpartly semi-automaticways to upload your raw data and related descriptive
information (title, date, author, source, section and type of document) into DNA: Importing single
Documents manually via Copy and Paste, Importing multiple Documents semi-automatically from
text les, Importing Documents from other DNA databases and using rDNA to import data which is
already available in R (WIP!). All four will be explained in detail in this section.
5.2.1

Importing single Documents manually via Copy and Paste

The most basic way to import data to DNA requires you to manually copy and paste the content and
the descriptive information for each of your documents into the text elds of a pop-up window, which
you open by clicking on the index tab Documents and selecting the option Add new document
(see Figure 21). This window has eight text boxes, in which you can enter information from and about
your source data (see Figure 21):
The eld title is mandatory and may include any kind of information, for instance a unique ID
if you plan to collect additional information about the articles in a separate database. Duplicate
article titles are not allowed.
The eld date is also mandatory and preset on the current time and day. You can change
it by either clicking on the year, month, day or time and adjusting the respective value via the
arrows on the right or by manually entering the date in the format YYYY-MM-DD hh:mm:ss.
Please make sure you enter the date correctly because otherwise the algorithms for longitudinal
data will not work properly.
The elds author , source , section and type are optional, but this additional information can help you to eciently organize your data and ensure the reproducibilty, transparency
31

Figure 21: Open DNA-database
and future usage of your research project. You can enter these information either manually or
select an author, source, section or type you specied for a previously added document from the
drop-down menu, which appears when you click on the downward arrow buttonon the left of the
respective eld.
To insert the content of your document, copy your article from a website or any other text
source and paste it in the text eld (largest eld at the bottom of the pop-up window).
Single line breaks are automatically removed, while double line breaks (paragraph breaks) are
preserved. Some escape sequences and special characters are automatically removed when text
is inserted.
If you want to add further meta information to your document, which does not t the preset
categories, you can use the eld notes .
Finallyafter checking your specicationsyou can import the document to DNA by clicking the Add
button.
5.2.2

Importing multiple Documents semi-automatically from text les

If you want to analyze a greater number of articles, it quickly becomes tedious to manually copy and
paste each document and its meta data. This is why DNA also oers a semi-automatic way to upload
multiple documents and their relevant meta data (author, date, source, type) at the same time.
This way of importing raw data to DNA requires
(one le for each article) in a common folder.
Please note, that you have to use the .txt format for saving your data, as DNA can not import .doc or
Downloading and Preparing your Raw Data.
that you save all documents as separate .txt les

Figure 22: Downloading les from the LexisNexis newspaper archive
.pdf les.8 In case you use the newspaper database of LexisNexiswhich is available through many
university lbrariesfor nding and retrieving your raw data, please make sure that you download all
documents separately (by selecting the individual document before clicking the download button, see
Figure 22, step 1-2 ) and choose the document format Text (under Format Options in the Download
pop-up menu, see Figure 22, step 3-4 ) before downloading the data (see Figure 22, step 5 ).9
If you want to use the preset regex congurations (in contrast to adjusting them) for automatically
detecting and uploading the meta data of your documents, you should use a le name in the format
DD.MM.YYYY - Author - Source - TYPE.txt with blanks before and after the minuses,
where DD.MM.YYYY is the date, on which the article was published. While Author and Source
do not require a special format or length (e. g. you can use the rst and/or last name of the author),
the type of the document must always be indicated by capital letters. For example, the le name
of the article spon.de/aeclD, which is used as an example here, would have the format 31.03.2014 Ralf Neukirch - SPON International - DIGITALRESOURCE.txt. Please note, that plain text les
are somtimes saved as .TXT instead of .txt les. While this is technically the same, it can cause
problems while importing multiple text les. If this is the case, you have to either change the preset
Regex conguration or correct the .txt sux manually in the le name(s). Otherwise the automatic
detection of your documents' meta data will not work.
If you prepared your data adequately, you can retrieve
the documents and the relevant additional information in four simple steps (see Figure 23):

Importing your Raw Data into DNA

1. Click on the index tab Documents and select the option Import text les (see Figure 23,
step 1 ). As a result, a new window will open, in which you press the button Select folder
(see step 2 ). This will open a further pop-up menu. Here, you have to select the folder, in which
you saved the text les of your raw data, from the Look in slide down menu (see step 3 )
and click the button Open (see step 4 ).
8 You

can, however, save Word-documents as .txt les or use an online converter to transform PDFs into txt les.

Note, that you need to make sure (both cases) that the .txt le is saved with UTF 8 encoding.

9 If

you use

rDNA

it will soon also be possible to import LexisNexis data into

called LexisNexisTools.

DNA

via using

rDNA

and a new

Rpackage

Figure 23: Import text les
2. Now all documents, which are stored in the respective folder, should be listed in the main window
of the Import text les... pop-up (see Figure 24). If this isn't the case, please check if your
documents are saved in the right le format (.txt). In order to check, whether DNA is able to
automatically identify your documents' meta data, select one of the documents and click on the
Refresh button (see Figure 24). If you specied the le names correctly, you can now see the
respective meta data of the selected document in the elds Title, Author, Source, Type
and Date of the Preview Section at the bottom right of the Import text les window (see
Figure 24).
3. If you want to adjust or amend the meta data manually, just select the document, uncheck the
box Regex of the eld you want to edit and enter the divergent/additional information in the
eld on the left. Then click again on the Refresh button to check, whether your changes were
accepted.
4. Finally, click on the button Import les to import all documents of the respective folder into
your DNA database (you do not need to select each document for import).
The previous
steps assumed that you use the preset conguration of DNA to detect and upload the meta data (Title,
Author, Source, Type, Date) of your documents automatically into your database. However, if you
are interested in automatically importing additional information about your source data (in the elds
Section or Notes ) or if your le names depart from the naming system layed out here (but
nevertheless contain all relevant information in a systematic order), DNA allows you to change, adjust
or amend the pattern, through which the meta data about your documents is derived from the le
names. The commands/rules, on which the translation of le names into meta data is based, are
formulated in the Regular expessions (in short: Regex) syntax and can be edited for each kind of
information (Title, Author, Source, Section, Tyoe, Notes, Date) in the eld Pattern on the bottom
Adjusting the Regex Conguration for automatic identication of meta data.

Figure 24: Import text les
left of the Import text les... window (see Figure 24). If you want to amend or adjust this settings
it is recommended to use a Regex Cheatsheet (see e. g. cheatography.com or this regex translator ).
As further support, Figure 25 translates the preset regular expressions of the DNA Import text les...
option.
5.2.3

Importing Documents from other

DNA

databases

You can also import documents from other DNA databases. This function is particularly relevant in
two scenarios: First, if you not only want to use the raw data, but also the coded statements
of an already nished research project, this function allows you to import both. Secondly, if there is
more than one person working on the same project at the same time and you did not use
multiple user roles (see Section 4.2) to enable your coders to work on the same remote database. In
the second scenario, you should use this function to prepare your datasets or merge the codings, as
it is usually dicult to merge the les manually later on. In the latter scenario, the function helps
you to avoid trouble with duplicate statement IDs and article names, as DNA will take care of e. g.
duplicates automatically.
Make sure, that you know which version of DNA (DNA 2.0 or older) was used to create and edit
the database, from which you want to import data, before using the Import from DNA function. If
you use this manual as a beginner's tutorial for working with DNA please download the le sample.dna
from the DNA github.com/leifeld/dna/releases. This le contains a small selection of documents and
statements from a larger project about congressional hearings on climate change, employed in the
project described in Fisher et al. (2013a,b).
To import documents (and the included code statements), click on the index tab Documents and
select the option Import from DNA 2.0 le , if DNA 2.0 was used to create and edit the database. As
the internal structure of .dna les has signicantly changed since version 1.31, databases created with
an older version of DNA need to be impored using the seperate method Import from DNA 1.31 le
35

Figure 25: Import text les

Figure 26: Import a DNA 2.0-database
(see Figure 26, step 1 ). As a result of either step, a further pop-up menu will open (see Figure 27).
In this window, you have to select the folder, in which you saved the text les of your raw data, from
the Look in slide down menu (see step 2 ) and select the respective .dna le (see step 3 ).
Click the button Open (see step 4 ) to then open the menu depicted in Figure 27.
In this menu, you can select, which documents (and respective which coded statements) from the
original DNA database you want to import in your database by either manually checking or unchecking
the boxes on the left of the document title or by using the function Keyword lter . This function
is particularly helpful if you want to only import few documents with a specic common characteristic
(author, topic) from a very large dataset. Clicking on the button Keyword lter... (see left button
in Figure 27) opens a new pop-up window, in which you can enter a specic search term. For example,
if you downloaded and opened the sample.dna le, you can select all congressional hearings of NGO
representatives by entering the keyword NGO in the text eld and pressing the button OK
in the Keyword ler pop-up window (see Figure 27). Now only the boxes of the three documents,
which contain the hearings of NGO representatives Kateri Callahan, David Hamilton and Nayak Navin,
should be checked, while the other boxes are unchecked. The Keyword lter.. function is based on
the same regex syntax described in Adjusting the Regex Conguration for automatic identication of
meta data. This means, you can also use more specied regular expressions (see Figure 25 or regex
cheatsheet) to select certain articles. For example, if you enter a N in the Keyword lter DNA
will select all articles starting with a capital N. If you want to undo your selections, you can also
automatically select or unselect all articles by pressing the button (Un)select all in the middle of
the Import statements window (see Figure 27). Pressing the right button Import selected in
the same window imports all documents with a checked box (and the respective coded statements) in
your DNA database (see Figure 27). If you use this manual as a beginner's tutorial for working with
DNA, you should try importing all documents and the respective statements from the le sample.dna
into your database.

Figure 27: Import Statments menu

Figure 28: Import Statements menu

5.3 Organizing documents (Raw Data)
5.3.1

Deleting and navigating through documents

All your imported documents are listed in the upper middle table of the DNA main window. If you
click on an article, its corresponding text (i. e. the speech) will be displayed in the text area below
the document table. By clicking on, for example, the entry 109-1: Callahan, Kateri-NGO-Y you
open the speech of Kateri Callahan, a representative of the Alliance to Save Energy. You can adjust
the size of the document table (by clicking on the bar above the text area and moving it vertically
with your cursor) or its colums (by clicking on the edge of the column and moving it horizontally
with your cursor). You can also customize the meta information, which are displayed in the document
table: Just right click on any document and use the appearing context menu to (un-)check the
boxes of the information you (don't) want to be displayed (see Figure 28, step 1 ). A structured (and
customised) overview of your raw data is essential for detecting missing information and thus eciently
controlling, organizing and coding your data. For example, if you display the meta information Type
(by checking the respective box in the context menu), you can see that the type of all documents from
the sample.dna le is not listed.
The same context menu can be used to delete documents from your database by selecting the
documents you want to delete (pressing and holding the Ctrl key for selecting multiple documents),
opening the context menu with a right click and choosing the option Delete selected documents .
5.3.2

Editing the documents' meta data (author, time etc...)

DNA allows you to edit, delete or complement the descriptive information related to your raw data
(title, date, author, source, section and type of document). Similiar to the procedures outlined in Section 5.2 there is a manual as well as a semi-automatic way to adjust the meta data of your documents.
39

Figure 29: Meta information recode window
Editing the documents' meta data manually. The most basic way to edit your documents'
meta data is to select the document, of which you want to edit the information (by left-clicking
on it) and adjusting the values in the Document properties submenu on the middle left of the

DNA main window (see Figure 28, step 2 ) by either manually typing in the relevant information or by
selecting an already specied author, a source, a section or a type from the drop-down menu on
the right of the respective meta eld. For example, in Figure 28 (step 2 ) Kateri Callahans speech was
selected, and the value NGO (for Non-Governmental Organisation) was manually specied as Type
of document by entering it in the eld Type of the Document properties submenu. Do not forget
to press the button Save in the submenu (see Figure 28, step 2 ) to conrm your edits.

Please note, that you can manually only edit the meta data of one document at one time. If
you try to select multiple documents for editing, the Document properties submenu will disappear,
returning (No document or permission).
However if you want to adjust the
meta data of a greater number of articles, it quickly becomes tedious to manually edit information
about each document. This is why DNA also oers a semi-automatic way to edit, delete or complement
the descriptive information related to your documents. In order to edit your documents' meta data
semi-automatically, click on the index tab Documents and select the option Batch-recode metadata (see Figure 28, step 3 ). As a result, a pop-up window similiar to Figure 29 will open. In the
upper half of this pop-up window you nd nine elds, which can be congured in order to adjust the
meta data for multiple documents at once:
Editing the documents' meta data semi-automatically.

The eld Target eld: species, which kind of meta information (i. e. title, author, source,
section, type, notes) should be adjusted by choosing the respective meta data category from the
slide-down menu (which you open by clicking the arrow on the right of the target eld).

The eld Source eld: species, where the data you want to use for adjusting the target eld
is stored. For example, if you simply want to delete or correct (e. g. misspelled) title-, author-,
source-, section-, type- or notes-metadata, you usually choose the same eld as source eld as
you have chosen as target eld, since you want to adjust the data already stored in this eld.
However, if you want to add new data to a (maybe empty or incomplete) target eld, you have
to choose the part of the meta information as source eld, which contains the information, from
which you want to derive the new data. As the document title should contain all relevant meta
information, Title is usually used as source eld for the latter case.
The eld Matching on target regex allows you to automatically delimit the documents
which you want to adjust, based on the information stored in the document's target eld. Similiar
to all regex implementations in DNA you can either use search terms or regular expressions to lter
the documents. If you, for instance, misspelled the author Ralf Neukirch sometimes as Ralf
Neunkirch, you can correct all your misspellings by simply selecting Author as Target eld,
entering Ralf Neunkirch in the eld Matching on target regex: and the correct version (Ralf
Neukirch) in the eld New target eld. As Matching on target regex automatically deselects
all non-matching cases (here: All documents, who do not have Ralf Neunkirch specied as their
author), the meta information (here: Author) remains the same for all other documents.
The eld Matching on source regex similarly allows you to automatically lter the documents of which you want to alter the meta data, based on the information stored in the document's source eld. For example, if you realise that Ralf Neukirch does not write for SPON
International (as you erroneously specied), but for THE GUARDIAN, you can simply correct all your misspecications by rst selecting Source as the Target eld and Author as
the Source eld, secondly entering Ralf Neukirch in the eld Matching on source regex and
then specifying THE GUARDIAN as New target eld.
The eld %target regular expression allows you to specify/match a part of the target eld,
which you want to use as new information in the same eld. For example, if the eld Author
somehow contains the full document titles you can reduce the information in the eld Author
to just the name of the respective author by entering the regular expression (?<=.+?-).+?(?=
-) (see Figure 25 or regex cheatsheet) in the eld %target regular expression and entering
%target in the eld New target eld. Please note, that if you do not use this function,
you should not change the preset value .+ in this eldbecause if you do, your recoding
might not obtain the expected results.
The eld %target replacement similarly to the elds New target eld and %source
replacementdenes a new value for the information in the target eld. If you use %target
as New target eld, you have to specify the new/additional/corrected/reduced information in
this eld.
The eld %source regular expression allows you to specify/match a part of the source eld,
which you want to use as new information in the target eld. For example, if your source eld
is Title and the titles of your documents have the recommended format (i. e. DD.MM.YYYY
- Author - Source - TYPE.txt with blanks before and after the minuses; see Section 5.2.2) you
can automatically specify the meta information for the eld Author by (1.) choosing Author as the Target eld and Title as the Source eld, (2.) entering the regular expression
(?<=.+?-).+?(?= -) (see Figure 25 or regex cheatsheet) in the eld %source regular expression and (3.) entering %source in the eld New target eld. Please note, that if
you do not use this function, you should not change the preset value .+ in this
eldbecause if you do, your recoding might not obtain the expected results.

The eld %source replacement similarly to the elds New target eld and %target
replacementdenes a new value for the information in the target eld. If you use %source
41

as New target eld, you have to specify the new/additional/corrected/reduced information in
this eld.
The eld New target eld denes the new/corrected/reduced/additional data, which is entered in your target eld (see examples above ). Please note, that this eld has to be set on
%source (preset value) if you use the functions source regular expression or source replacement and has to be set on %target if you use the functions target regular expression or
target replacement. Otherwise, the respective functions will not work.
The lower half of the Recode document meta-data pop-up window (see Figure 29) displays a table
with four columns and a row for each of your documents, which help you to preview, control and trace
back your changes to the meta data:
The column ID contains the individual ID of each of your documents. This column can
be particularly helpful if you specify a recoding procedures for a certain set of documents. If
you know the ID of a few exemplary documents from this set, you can quickly trace back and
understand the consequences of your recoding specications by scrolling down to the respective
IDs and taking a look at the other columns of these documents.
The column Source eld displays the eld, from which you get the meta data for recoding
the target eld. It is particularly helpful to understand the sequence of information in the source
eld, if you want to specify a %source regular expression or use Matching on source regex
(for example, if only some source elds contain the relevant information).
The column Old target eld shows the meta data in the target elds prior to your adjustments. It is particularly helpful if you want to use %target regular expression or use Matching
on target regex (for example, if you only want to change the value of a certain set of target
elds).
The column New target eld displays the consequences of your adjustment. It is particular
helpful to check if your recoding will be successful or if some recoding outcomes are actually
undesired (for example, if the target eld already contained the relevant information, but is
recoded nevertheless).
Your recodings are only applied, if you press the button Recode (on the lower right of the Recode
document meta data window, see Figure 29). Once this is applied, it cannot be undone! So
please control the consequences of your recodings by using the table at the lower half of the window.
However, before pressing the Recode button, you can revert all adjustments by pressing the button
Revert changes and therefore are able to experiment with the meta data (regex) specications.
As noted previously, all documents from the le sample.dna do not specify any meta data concerning the type of the respective document. Both Figure 29 and Figure 30 illustrate an exemplary
semi-automatic procedure for complementing this information based on the information stored in the
document title (here: The organisation, to which the respective speaker belongs to). Thus in both
examples, Type is selected as Target eld, while Title is selected as Source eld.
The example in Figure 29 uses manual search terms to specify the meta information for the
document type. By entering NGO in the eld Matching on source regex the adjustments are
limited to the documents, which contain NGO in the document title. By entering NGO in the
eld New target eld, the new value for Type is specied for the selected documents. As you can
see in the table on the lower half of the Recode meta-data window, this very simple procedure is
insofar successful, as only the target elds of documents containing hearings of NGO-representatives
are changed and the target elds of all other documents (including those with already correct Type

Figure 30: Meta information recode window (regex explained)
information) remain unchanged. However, this procedure would have to be repeated for each kind of
organisation from the sample (NGO, GOV, BUS).
The more elegant way of semi-automatically specifying meta information is depicted in Figure 30,
which uses the Regex-syntax. Here, by entering ? in the eld Matching on target regex, only
those documents are selected for amendment, which do not already contain any information about
the document type (therefore excluding those documents with already correct Type information).
By specifying (?<=.+?-)[A-Z]+ as %source regular expression (and accordingly %source as New
target eld), DNA is instructed to lter any string of upper-case characters before a minus in the
document title and set it as a new value for Type. Thus you can recode the document type for all
documents at once, ensuring that already specied values are not overwrittenas evident from the
table in the lower half of the window.

Using DNA: Coding the Data

Coming soon...

Using DNA: Exporting the coded Data

Coming soon...

rDNA: Using DNA from R

Philip Leifeld
DNA can be connected to the statistical computing environment R (R Core Team 2014) through the
rDNA package (Leifeld 2017). There are two advantages to working with R on DNA data.
The rst advantage is replicability. The network export function of DNA has many options. Remembering what options were used in an analysis can be dicult. If the analysis is executed in R,
commandsrather than mouse clicksare used to extract networks or attributes from DNA. These
commands are saved in an R script le. This increases replicability because the script can be re-used
many times. For example, after discovering a wrong code somewhere in the DNA database, it is sucient to x this problem in the DNA le and then re-run the R script instead of manually setting all the
options again. This reduces the probability of making errors and increases replicability.
The second advantage is the immense exibility of R in terms of statistical modelling. Analysing DNA
data in R permits many forms of data analysis beyond simple visualization of the resulting networks.
Examples include cluster analysis or community detection, scaling and application of data reduction
techniques, centrality analysis, and even statistical modelling of network data. R is also exible in
terms of combining and matching the data from DNA with other data sources.

8.1 Getting started with rDNA
The rst step is to install R. Installing additional R packages for network analysis and clustering, such
as statnet (Goodreau et al. 2008; Handcock et al. 2008, 2016), xergm (Leifeld et al. 2017a,b), igraph
(Csardi and Nepusz 2006), and cluster (Maechler et al. 2017), is recommended. Moreover, it is
necessary to install the rJava package (Urbanek 2016), on which the rDNA package depends, and the
devtools package (Wickham and Chang 2016), which permits installing R packages from GitHub (see
Section 3.4).

install.packages("statnet")
install.packages("xergm")
install.packages("igraph")
install.packages("cluster")
install.packages("rJava")
install.packages("devtools")
After installing these supplementary packages, the rDNA package can be installed from GitHub. The
devtools package contains a function that permits easy installation of R packages from GitHub and
can be used as follows to install rDNA:

library("devtools")
install_github("leifeld/dna/rDNA")
Once installed, the rDNA package must be attached to the workspace:

library("rDNA")
To ensure that the following results can be reproduced exactly, we should set the random seed in R:
45

set.seed(12345)
Now we are able to use the package. The rst step is to initialize DNA. Out of the box, rDNA does not
know where the DNA .jar le is located. We need to register DNA with rDNA to use them together. To
do that, one needs to save the DNA .jar le to the working directory of the current R session and then
initialize DNA as follows (with dna-2.0-beta20.jar in this example):

dna_init("dna-2.0-beta20.jar")
After initializing DNA, we can open the DNA graphical user interface from the R command line:

dna_gui()
Alternatively, we can provide the le name of a local DNA database as an argument, and the database
will be opened in DNA. For example, we could open the sample.dna database that is provided for
download on GitHub under Releases:

dna_gui("sample.dna")
For this to work, the database le has to be saved in the working directory of the R session, or the
path needs to be provided along with the le name.
In addition to opening the GUI, we will want to retrieve networks and attributes from DNA. For this
to happen, a connection with a DNA database must rst be established using the dna_connection
function:

conn <- dna_connection("sample.dna")
The dna_connection function accepts a le name of the database including full or relative path (or,
alternatively, a connection string to a remote MySQL database) and optionally the login and password
for the database (in case a remote MySQL database is used). Details about the connection can be
printed by calling the resulting object called conn.
After initializing DNA and establishing a connection to a database, we can now retrieve data from DNA.
We will start with a simple example of a two-mode network from the sample database. To compute
the network matrix, the connection that we just established must be supplied to the dna_network
function:

nw <- dna_network(conn)
The resulting matrix can be plotted using visualization functions from the statnet suite of packages:

library("statnet")
gplot(nw)

It is also easily possible to retrieve the attributes of a variable, for example the colours and types of
organizations, using the dna_attributes function:

at <- dna_attributes(conn)
The result is a data frame with organizations in the rows and one column per organizational attribute.
The next section will provide usage examples of both the dna_network and the dna_attributes
functions.

8.2 Retrieving networks and attributes
This section will explore the dna_network function and facilities for retrieving attributes in more detail.
The dna_network function has a number of arguments, which resemble the export options in the DNA
export window. The help page for the dna_network function provides details on these arguments. It
can be opened using the command

help("dna_network")
We will start with a simple example: a one-mode congruence network of organizations in a policy
debate. The sample.dna database is a small excerpt from a larger empirical research project that tries
to map the ideological debates around American climate politics in the U.S. Congress over time. Details
about the dataset from which this excerpt is taken are provided by Fisher et al. (2013a,b). Here, it
suces to say that the sample.dna le contains speeches from hearings in the U.S. Congress in which
interest groups and legislators make statements about their views on climate politics. Accordingly, one
should expect to nd a polarized debate with environmental groups on one side and industrial interest
groups on the other side. To compute a one-mode congruence network, the following code can be used:

congruence <- dna_network(conn,
networkType = "onemode",
statementType = "DNA Statement",
variable1 = "organization",
variable2 = "concept",
qualifier = "agreement",
qualifierAggregation = "congruence",
duplicates = "document")
The result is an organization × organization matrix, where the cells represent on how many concepts
any two actors (i e., the row organization and the column organization) had the same issue stance (by
values of the qualier variable agreement).
47

The arguments of the dna_network function resemble the options in the DNA export window. Details
on the various arguments of the function can be obtained by displaying the help page (?dna_network).

statementType = "DNA Statement" indicates which statement type should be used for the network
export. In this case, the statement type DNA Statement contains the variables organization, concept,
and agreement. The argument qualifierAggregation = "congruence" causes rDNA to count how
often the unique elements of variable1 have an identical value on the qualifier variable (here:
agreement) when they refer to a concept (variable2).
If the algorithm nds duplicate statements within documentsi. e., statements containing the same organization, concept, and agreement pattern, only one of them is retained for the analysis (duplicates
= "document").
The resulting matrix can be converted to a network object and plotted as follows:

nw <- network(congruence)
plot(nw,
edge.lwd = congruence^2,
displaylabels = TRUE,
label.cex = 0.5,
usearrows = FALSE,
edge.col = "gray"
)
U.S. Public Interest Research Group
Energy and Environmental Analysis, Inc.

Alliance to Save Energy

National Petrochemical & Refiners Association

Senate

Sierra Club
Environmental Protection Agency

Here, we used the edge.lwd argument of the plot.network function to make the line width proportional to the strength of congruence between actors. We used squared edge weights to emphasize the
dierence between low and high edge weights. We also displayed the labels of the nodes at half the
normal size, suppressed arrow heads, and changed the colour of the edges to grey. More information
about the visualization capabilities of the network and sna packages are provided by Butts (2008a,b,
2015).
The network is not particularly polarized. We can suspect that some of the concepts are not very
contested. If they are supported by all actors, this may mask the extent of polarization with regard to
the other concepts. From our experience with the dataset, we can tell in this particular case that the
concept There should be legislation to regulate emissions. is in fact very consensual. If everybody
agrees to this concept, it obfuscates the real structure of the network. Therefore we should exclude it
from the congruence network. To do that, we need to export and plot the congruence network again
and use the excludeValues argument this time:

congruence <- dna_network(conn,
networkType = "onemode",
statementType = "DNA Statement",
48

variable1 = "organization",
variable2 = "concept",
qualifier = "agreement",
qualifierAggregation = "congruence",
duplicates = "document",
excludeValues = list("concept" =
"There should be legislation to regulate emissions."))

nw <- network(congruence)
plot(nw,
edge.lwd = congruence^2,
displaylabels = TRUE,
label.cex = 0.5,
usearrows = FALSE,
edge.col = "gray"
)

Environmental Protection Agency

Sierra Club
National Petrochemical & Refiners Association

Energy and Environmental Analysis, Inc.
U.S. Public Interest Research Group

Senate

Alliance to Save Energy

This reveals the structure of the actor congruence network. There are two camps revolving around environmental groups on the right and industrial interest groups and state actors on the left, with Energy
and Environmental Analysis, Inc. taking a bridging position. The strongest belief congruence ties
can be found within, rather than between, the coalitions.
Next, we should tweak the congruence network further by changing the appearance of the nodes. We
can use the colours for the organization types saved in the database and apply them to the nodes in
the network. We can also make the size of each node proportional to its activity. The dna_attributes
function serves to retrieve these additional data from DNA. The result is a data frame with the relevant
data for each organization in the colour and frequency columns:

at <- dna_attributes(conn,
statementType = "DNA Statement",
variable = "organization")
at
##
##
##
##
##

1
2
3
4

id
value
16
Alliance to Save Energy
7
Energy and Environmental Analysis, Inc.
14
Environmental Protection Agency
25 National Petrochemical & Refiners Association
49

color
type alias note
#00CC00
NGO
#FF9900
Business
#000000 Government
#FF9900 Business

##
##
##
##
##
##
##
##
##
##
##

5 11
Senate #000000 Government
6 19
Sierra Club #00CC00
NGO
7 22
U.S. Public Interest Research Group #00CC00
NGO
frequency in dataset in network
1
2
TRUE
TRUE
2
3
TRUE
TRUE
3
1
TRUE
TRUE
4
1
TRUE
TRUE
5
2
TRUE
TRUE
6
4
TRUE
TRUE
7
5
TRUE
TRUE

To use these data in the congruence network visualization, we can use the plotting facilities of the
plot.network function:

plot(nw,
edge.lwd = congruence^2,
displaylabels = TRUE,
label.cex = 0.5,
usearrows = FALSE,
edge.col = "gray",
vertex.col = at$color,
vertex.cex = at$frequency
)
National Petrochemical & Refiners Association
Senate

Environmental Protection Agency

Energy and Environmental Analysis, Inc.

Alliance to Save Energy

U.S. Public Interest Research Group

Sierra Club

This yields a clear visualization of the actor congruence network, with simultaneous display of the
network structure including its coalitions, the actors' activity in the debate, and actor types.
Another way to visualize a discourse network is a two-mode network visualization. To compute a
two-mode network of organizations and concepts, the following code can be used:

affil <- dna_network(conn,
networkType = "twomode",
statementType = "DNA Statement",
variable1 = "organization",
variable2 = "concept",
qualifier = "agreement",
qualifierAggregation = "combine",
duplicates = "document")
This creates a 7×6 matrix of organizations and their relations to concepts. The argument networkType
= "twomode" is necessary because the rows and columns of the affil matrix should contain dierent variables. The arguments variable1 = "organization" and variable2 = "concept" dene
which variables should be used for the rows and columns, respectively. The arguments qualifier =
"agreement" and qualifierAggregation = "combine" dene how the cells of the matrix should be
populated: agreement is a binary variable, and the combine option causes a cell to have a value of
0 if the organization never refers to the concept, 1 if the organization refers to the respective concept
exclusively in a positive way, 2 if the organization refers to the concept exclusively in a negative way,
and 3 if there are both positive and negative statements by the organization about the concept. rDNA
reports on the R console what each combination means.
As in the previous example, the resulting network matrix can be converted to a network object (as
dened in the network package). The colours of the edges can be stored as an edge attribute, and the
resulting object can be plotted with dierent colours representing positive, negative, and ambivalent
mentions.

nw <- network(affil, bipartite = TRUE)
colors <- as.character(t(affil))
colors[colors == "3"] <- "blue"
colors[colors == "2"] <- "red"
colors[colors == "1"] <- "green"
colors <- colors[colors != "0"]
set.edge.attribute(nw, "color", colors)
plot(nw,
edge.col = get.edge.attribute(nw, "color"),
vertex.col = c(rep("white", nrow(affil)),
rep("black", ncol(affil))),
displaylabels = TRUE,
label.cex = 0.5
)

Alliance to Save Energy

National Petrochemical & Refiners Association

Climate change is caused by greenhouse gases (CO2).
There should be legislation to regulate emissions.

Sierra Club
Environmental Protection Agency
Climate change is real and anthropogenic.
CO2 legislation will not hurt the economy.

EmissionsSenate
legislation should regulate CO2.

U.S. Public Interest Research Group

Energy and Environmental Analysis, Inc.
Cap and trade is the solution.

In this example, we rst converted the two-mode matrix to a bipartite network object, then created
a vector of colours for the edges (excluding zeros), and inserted this vector into the network object
as an edge attribute. It was necessary to work with the transposed affil matrix (using the t function) because the set.edge.attribute function expects edge attributes in a row-wise order while the
as.character function returns them in a column-wise order based on the affil matrix. Finally, we
plotted the network object with edge colours and labels. In the visualization, we used white nodes for
organizations and black nodes for concepts.
We can now see the opinions of all actors on the various concepts. The blue edge indicates that Energy
and Environmental Analysis, Inc. has both positive and negative things to say about the concept
Emissions legislation should regulate CO2. This is why this organization acts as a bridge
between the two camps in the congruence network. Furthermore, we can now see more clearly that
the concept we omitted in the congruence network, There should be legislation to regulate
emissions, is viewed positively by four organizations, but still receives a negative mention by one
actor. The green edges span both camps, and this caused additional connections between the two
groups. The negative tie is ignored in the construction of the congruence network because conicts
are not counted and there is no second red tie to that concept.

References
Butts, C. T. (2008a). Social network analysis with sna.

Journal of Statistical Software,

Butts, C. T. (2008b). network: A package for managing relational data in R.
Software, 24(2):136.
Butts, C. T. (2015). network: Classes
org). R package version 1.13.0.

for Relational Data.

24(6):151.

Journal of Statistical

The Statnet Project (http://statnet.

Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research.
Journal, Complex Systems, 1695(5):19.

Inter-

Fisher, D. R., Leifeld, P., and Iwaki, Y. (2013a). Mapping the ideological networks of American climate
politics. Climatic Change, 116(3):523545.
Fisher, D. R., Waggle, J., and Leifeld, P. (2013b). Where does political polarization come from? Locating polarization within the U.S. climate change debate. American Behavioral Scientist, 57(1):7092.
Goodreau, S. M., Handcock, M. S., Hunter, D. R., Butts, C. T., and Morris, M. (2008). A statnet
tutorial. Journal of Statistical Software, 24(9):126.
52

Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., Krivitsky, P. N., Bender-deMoll, S.,
and Morris, M. (2016). statnet: Software Tools for the Statistical Analysis of Network Data. The
Statnet Project (http://www.statnet.org). R package version 2016.9.
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., and Morris, M. (2008). statnet:
Software tools for the representation, visualization, analysis and simulation of network data. Journal
of Statistical Software, 24(1):111.
Leifeld, P. (2017). rDNA: R

Discourse Network Analyzer. R package version 2.0.1.

bindings for the

Leifeld, P., Cranmer, S. J., and Desmarais, B. A. (2017a).
Models with btergm: Estimation and Bootstrap Condence

Temporal Exponential Random Graph

Intervals.

Leifeld, P., Cranmer, S. J., and Desmarais, B. A. (2017b). xergm:
Graph Models. R package version 1.8.2.

Forthcoming.

Extensions of Exponential Random

Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., and Hornik, K. (2017). cluster:
Analysis Basics and Extensions. R package version 2.0.6.

R Core Team (2014). R: A Language and
Statistical Computing, Vienna, Austria.
Urbanek, S. (2016). rJava:

Low-Level

Environment for Statistical Computing.

Java

Interface.

Wickham, H. and Chang, W. (2016). devtools:
package version 1.12.0.

R Foundation for

R package version 0.9-8.

Tools to Make Developing

Cluster

Packages Easier.

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