Manual De Tableau
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Authors: Maila Hardin, Daniel Hom, Ross Perez, & Lori Williams Which chart or graph is right for you? 2 You’ve got data and you’ve got questions. Creating a chart or graph links the two, but sometimes you’re not sure which type of chart will get the answer you seek. This paper answers questions about how to select the best charts for the type of data you’re analyzing and the questions you want to answer. But it won’t stop there. Stranding your data in isolated, static graphs limits the number of questions you can answer. Let your data become the centerpiece of decision making by using it to tell a story. Combine related charts. Add a map. Provide filters to dig deeper. The impact? Business insight and answers to questions at the speed of thought. Which chart is right for you? Transforming data into an effective visualization (any kind of chart or graph) is the first step towards making your data work for you. In this paper you’ll find best practice recommendations for when to create these types of visualizations: 1. 2. 3. 4. 5. 6. 7. 8. 9. Bar chart Line chart Pie chart Map Scatter plot Gantt chart Bubble chart Histogram chart Bullet chart 10. 11. 12. 13. Heat map Highlight table Treemap Box-and-whisker plot 3 1. Bar chart Bar charts are one of the most common ways to visualize data. Why? It’s quick to compare information, revealing highs and lows at a glance. Bar charts are especially effective when you have numerical data that splits nicely into different categories so you can quickly see trends within your data. When to use bar charts: • Comparing data across categories. Examples: Volume of shirts in different sizes, website traffic by origination site, percent of spending by department. Also consider: • Include multiple bar charts on a dashboard. Helps the viewer quickly compare related information instead of flipping through a bunch of spreadsheets or slides to answer a question. • Add color to bars for more impact. Showing revenue performance with bars is informative, but overlaying color to reveal profitability provides immediate insight. • Use stacked bars or side-by-side bars. Displaying related data on top of or next to each other gives depth to your analysis and addresses multiple questions at once. • Combine bar charts with maps. Set the map to act as a “filter” so when you click on different regions the corresponding bar chart is displayed. • Put bars on both sides of an axis. Plotting both positive and negative data points along a continuous axis is an effective way to spot trends. 4 Are Film Sequels Profitable? Box Office Stats For Major Film Franchises Select Movie Franchise: All Click to Highlight Average: Estimated Budget Profit Original Sequel 2nd Sequel 3rd Sequel 4th Sequel 5th Sequel 6th Sequel $0M $50M $100M $150M Combined Bar Length = Avg. U.S. Gross $200M How much does a budget increase affect a sequel's box office? Figure 1: Tell stories with bar charts Are film sequels profitable? In this example of a bar chart, you quickly get a sense of how $400M profitable sequels are for box office franchises. Select the chart and use the drop-down Profit U.S. Gross $400M filter to see the profit for your favorite movie franchise. Public Pension Funding Ratios Nationwide Funding Ratio and Unfunded Liability by State $200M Public Pension Funding Ratios Nationwide $200M Click to filter list below Funding Ratio and Unfunded Liability by State $0M Click to filter list below $0M $0M $100M $200M Estimated Budget 48% $300M $0M $100M $200M Estimated Budget Data: Internet Movie Database, Box Office Mojo. 48% About Tableau maps: www.tableausoftware.com/mapdata Funding Ratio 29% 59% About Tableau maps: www.tableausoftware.com/mapdata Unfunded liability $3B Funding Ratio 29% $200B Unfunded liability $3B 59% Funding Ratio and Unfunded Liability by Plan Click to highlight state Contra Costa County Funding Ratio and Unfunded Liability by Plan California Teachers CA 48% CA CA 45% 50% California LA CountyTeachers ERS CA CA 47%53% California PERFCity & County San Francisco CA CA 48% 58% San Diego County CA LA County ERS CA 50% 60% 53% San Francisco City & County CA $457B 40% Funding ratio $234B $6B $8B 58% 20% $400B $6B $165B CA Contra Costa County San Diego County 0% $457B $300B 47% Click to highlight California PERF state 20% $400B $100B $200B 45% CA 0% $300B $100B 40% Funding ratio 60% $165B $33B $234B $11B $8B $0B $100B $200B $33B Unfunded liability $300B $11B $0B Grand Total $100B $200B Unfunded liability Grand Total 48% $457B Grand Total Grand Total 48% Figure 2: Combine bar charts and maps $300B $457B Don’t settle for a bar chart that leaves you scrolling to find the answers you seek. By combining a bar chart with a map, this dashboard showing public pension funding ratios in the U.S. provides rich information at a glance. When California is selected, for example, the bar chart filters to show state-specific information. Check out another state to see their funding ratio. $300M The surgical service teams at Seattle 2 : : Some kind of Header Here place, the person who makes sense of the data first is going to win. 5 5 Tableau is one of the best tools out there for creating reallyresults powerful and insightful visuals. Speed: Get We’re usingtimes it for faster analytics that require great data 10 to 100 Tableau is fast analytics. In visuals helpteams us attell the stories we’re trying tothe person who mak place, The surgicalto service Seattle tell to our executive management team. first is going to win. 2 : : Some kind of Header Here – Dana Zuber, Vice President - Strategic Planning Manager, Wells Fargo 6 2. Line chart Line charts are right up there with bars and pies as one of the most frequently used chart types. Line charts connect individual numeric data points. The result is a simple, straightforward way to visualize a sequence of values. Their primary use is to display trends over a period of time. When to use line charts: Viewing trends in data over time. Examples: stock price change over a five• year period, website page views during a month, revenue growth by quarter. Also consider: Combine a line graph with bar charts. A bar chart indicating the volume sold • per day of a given stock combined with the line graph of the corresponding stock price can provide visual queues for further investigation. Shade the area under lines. When you have two or more line charts, fill the space under the respective lines to create an area chart. This informs a viewer about the relative contribution that line contributes to the whole. Black Friday Now Bigger than Thanksgiving 'Black Friday' & 'Thanksgiving': Comparing Search Term Popularity Search Volume Index 15 Amount spent (in bil#) • Mouse-over for score 10 Since 2008, 'Black Friday' has been a more popular search term than 'Thanksgiving.' 5 $40.0B Which coincides with the increase in total amount spent over Black Friday weekend $30.0B 2004 2005 Color Legend: 'Black Friday' 'Thanksgiving' Total Amount Spent (in $bil) 2006 2007 2008 2009 2010 2011 In the beginning of Nov. 2011, 'Black Friday' was already a more searched term on Google than 'Thanksgiving.' Filter Years: 1/4/2004 to 11/13/2011 Data: Google Trends, National Retail Federation. SVI score averaged overreveal the 2 weeks prior, after and including Thanksgiving/Black Friday Figure 3: isBasic lines powerful insight These two line charts illuminate the increasing popularity of “Black Friday” as an epic event in the United States. It’s quick to see that Thanksgiving lost ground to the popular shopping period in 2008. 7 Tech Leads Capital Raised in 2011 Running Total of Capital Raised (by Industry in Descending Order) $35,000 Running Sum of Offer Amount (in mil) $30,000 Select an industry to view individual companies Click here to clear filter $25,000 Technology $20,000 Energy $15,000 Health Care Consumer $10,000 Business Services Real Estate $5,000 $0 Jan 1 Mar 1 May 1 Jul 1 Sep 1 Nov 1 Jan 1 Filter by IPO Date: 1/13/2011 to 12/16/2011 Facebook Figure 4: Transform line charts into area charts HCA Holdings, Inc. Often when you haveInc.two or more sets of data in a line chart it can be helpful to shade the area Kinder Morgan, under theNielsen line.Holdings In thisB.V. chart, it’s easy to tell that companies in the technology sector raised more Industry Color Legend: Technology Yandex N.V. capital than real estate in 2011. Arcos Dorados Holdings, Inc. Energy Zynga Inc. Health Care GE Stock Trend Analysis Consumer Michael Kors Air Lease Corp. Select date range to update trend line: Business Services Freescale Holdi.. 6/18/2010 toSemiconductor 6/27/2011 Real Estate Renren Inc. Transportation BankUnited, Inc. Industrial Groupon Inc. $20.00 Financial The Carlyle Group LP PetroLogistics LP Materials $0 $5,000 $10,000 Offer Amount (in mil) $15,000 200M Communications Data: Hoovers Inc., SEC, Renaissance Capital $15.00 Volume Adj Close 150M $10.00 100M $5.00 50M $0.00 0M Jun 1, 10 Aug 1, 10 Oct 1, 10 Dec 1, 10 Date Feb 1, 11 Apr 1, 11 Jun 1, 11 Figure 5: Combine line charts with bar and trend lines Line charts are the most effective way to show change over time. In this case, GE’s stock performance over a one-year period is joined with trading volume during the same time frame. At a glance you can tell there were two significant events, one resulting in a sell-off and the other a gain for shareholders. Click the graph and use the filter to select a different date range. 8 3. Pie chart Pie charts should be used to show relative proportions – or percentages – of information. That’s it. Despite this narrow recommendation for when to use pies, they are made with abandon. As a result, they are the most commonly mis-used chart type. If you are trying to compare data, leave it to bars or stacked bars. Don’t ask your viewer to translate pie wedges into relevant data or compare one pie to another. Key points from your data will be missed and the viewer has to work too hard. When to use pie charts: Showing proportions. Examples: percentage of budget spent on different • departments, response categories from a survey, breakdown of how Americans spend their leisure time. Also consider: Limit pie wedges to six. If you have more than six proportions to communicate, • consider a bar chart. It becomes too hard to meaningfully interpret the pie pieces when the number of wedges gets too high. • Overlay pies on maps. Pies can be an interesting way to highlight geographical trends in your data. If you choose to use this technique, use pies with only a couple of wedges to keep it easy to understand. Worldwide Oil Rigs Land Offshore Rig Locations Select Region Africa Asia Pacific Canada Europe Latin America Middle East Russia and Caspian US Rig Count 2 1,000 2,000 3,000 4,000 5,101 About Tableau maps: www.tableausoftware.com/mapdata Country Trends 200 150 100 50 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 Figure 6: Use pies only to show proportions Pie charts give viewers a fast way to understand proportional data. Using pie charts on this map shows the distribution of oil rigs on land vs. offshore in Europe. 2010 9 4. Map When you have any kind of location data – whether it’s postal codes, state abbreviations, country names, or your own custom geocoding – you’ve got to see your data on a map. You wouldn’t leave home to find a new restaurant without a map (or a GPS anyway), would you? So demand the same informative view from your data. When to use maps: Showing geocoded data. Examples: Insurance claims by state, product export • destinations by country, car accidents by zip code, custom sales territories. Also consider: Use maps as a filter for other types of charts, graphs, and tables. Combine a • map with other relevant data then use it as a filter to drill into your data for robust investigation and discussion of data. • Layer bubble charts on top of maps. Bubble charts represent the concentration of data and their varied size is a quick way to understand relative data. By layering bubbles on top of a map it is easy to interpret the geographical impact of different data points quickly. Which U.S. State is the Greenest? LEED Buildings by state per million people Color Scale: 0.027 CT 0.0 0.5 1.0 1.5 Where are the LEED buildings in your state? 1.774 Select a state: Connecticut Search for a city: Filter by cert. level: All About Tableau maps: www.tableausoftware.com/mapdata Data: US Green Building Council Note: All individual addresses were geocoded using Google Maps data Figure 7: Provide street-level data on a map Maps are a powerful way to visualize data. In this visualization you can zero in on every LEED certified building in the United States based on their street address. Select any state or city to find the greenest buildings in that area. 10 5. Scatter plot Looking to dig a little deeper into some data, but not quite sure how – or if – different pieces of information relate? Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to where you want to focus your investigation efforts further. When to use scatter plots: Investigating the relationship between different variables. Examples: Male • versus female likelihood of having lung cancer at different ages, technology early adopters’ and laggards’ purchase patterns of smart phones, shipping costs of different product categories to different regions. Also consider: Add a trend line/line of best fit. By adding a trend line the correlation among • your data becomes more clearly defined. • Incorporate filters. By adding filters to your scatter plots, you can drill down into different views and details quickly to identify patterns in your data. • Use informative mark types. The story behind some data can be enhanced with a relevant shape 11 Demographics and Premium Forecasting Filter by Avg. Total Paid: $49 to $172 Avg. Total Paid $140 Filter by Avg. Total Claim: $93 to $228 $120 Select Age Group: 30-39 $100 Select Region: All -2K 0K 2K 4K 6K Total Incidents Loss Codes for None 8K 10K 12K None, employer cost ratio: 0.71 Figure 8: Who is most expensive to insure? Use an informative icon or “mark type” such as the female and male icons for additional detail in your scatter plot. Select the graph and filter to see how demographics change insurance Select Employer Ratio: premium forecasting for an employer. 0.71 Claimant Correlation and Fraud Analysis Filter Incident Count: 10 to 1,561 $120,000 Filter Total Claimed: $0 to $245,764 $100,000 Filter Total Paid: $0 to $163,775 Total Claim $80,000 Select Region: Midwest - East North Central $60,000 Select Threshold: 0.64 $40,000 Above Threshold? False True Total Payout $180 $20,000 $20,000 $40,000 $60,000 $0 0 100 200 300 400 Distinct count of INCID 500 600 $84,587 Figure 9: Can you spot the fraud? Using scatter plots is a quick, effective way to spot outliers that might warrant further investigation. By creating this interactive scatter plot, an insurance investigator can quickly evaluate where they might have fraudulent activity. The surgical service teams at Seattle 2 : : Some kind of Header Here place, the person who makes sense of the data 5 first is going to win. 12 Visualizing using color, shapes, positions on Speed: Getdata results X axes, bar faster charts, pie charts, whatever 10and to Y 100 times Tableau is fast analytics. In you use, makes it instantly visible and instantly place, the person who mak The surgical service teams at Seattle significant to the people who are looking atfirstit.is going to win. 2 : : Some kind of Header Here – Jon Boeckenstedt, Associate Vice President Enrollment Policy and Planning, DePaul University 13 6. Gantt chart Gantt charts excel at illustrating the start and finish dates elements of a project. Hitting deadlines is paramount to a project’s success. Seeing what needs to be accomplished – and by when – is essential to make this happen. This is where a Gantt chart comes in. While most associate Gantt charts with project management, they can be used to understand how other things such as people or machines vary over time. You could use a Gantt, for example, to do resource planning to see how long it took people to hit specific milestones, such as a certification level, and how that was distributed over time. When to use Gantt charts: Displaying a project schedule. Examples: illustrating key deliverables, owners, • and deadlines. • Showing other things in use over time. Examples: duration of a machine’s use, availability of players on a team. Also consider: • Adding color. Changing the color of the bars within the Gantt chart quickly informs viewers about key aspects of the variable. • Combine maps and other chart types with Gantt charts. Including Gantt charts in a dashboard with other chart types allows filtering and drill down to expand the insight provided. 14 Software Project Management Resource Status George S Jill S Sarah F Roy D Terry U 0 50 100 150 200 250 Hours Completed by Detailed Task 300 Remaining work 350 Work Completed by Start Date 1 Project 1 Project 1 1.1 High-level task 1 High-level task 1 1.1.1 Detailed task 1 Detailed task 1 1.1.2 Detailed task 2 Detailed task 2 1.2 High-level task 2 High-level task 2 1.2.1 Detailed task 3 Detailed task 3 Really detailed task 1 Really detailed task 1 18.104.22.168 Really detailed task 2 Really detailed task 2 2 Project 2 Project 2 22.214.171.124 Remaining hours legend Remaining schedule hours Roy D is in trouble: too much work for scheduled hours. Roy D Roy D George S George S Sarah F Sarah F George S George S Roy D 2.1 High-level task 3 High-level task 3 2.1.1 Detailed task 4 Detailed task 4 2.1.2 Detailed task 4 Detailed task 4 2.1.3 Detailed task 4 Detailed task 4 Jill S 2.2 High-level task 4 High-level task 4 Terry U 3 Project 3 Project 3 Sarah F 3.1 High-level task 5 High-level task 5 Roy D 3.1.1 Detailed task 7 3.1.2 Detailed task 8 Jill S Detailed task 7 George S Detailed task 8 0 Task details legend Actual hours Sarah F Jill S 50 Aug 20 100 150 Hours Scheduled hours Terry U Today Gantt chart legend Amount complete Aug 30 Days  Freeze Sep 9 Sep 19 Length of task Figure 10: Manage project effectively A Gantt chart is the centerpiece of this dashboard, providing a complete overview of tasks, owners, due dates, and status. By providing a menu of tasks at the top, a project manager can drill down as needed to make informed decisions. Top 25 Longest Serving Senators Click a State to see Senators About Tableau maps: www.tableausoftware.com/mapdata Sort by Length of Service Robert C. Byrd Strom Thurmond Edward M. Kennedy Carl T. Hayden John Stennis Ted Stevens Ernest F. Hollings Richard B. Russell Russell Long 1906 1946 1986 Data source: http://www.senate.gov/senators/Biographical/longest_serving.htm Figure 11: Who served the longest? With a quick glance, this Gantt chart lets you know which U.S. senator held office the longest and which side of the aisle they represented. Select the visualization and use the drop down menu to see criteria such as party. 15 7. Bubble chart Bubbles are not their own type of visualization but instead should be viewed as a technique to accentuate data on scatter plots or maps. Bubbles are not their own type of visualization but instead should be viewed as a technique to accentuate data on scatter plots or maps. People are drawn to using bubbles because the varied size of circles provides meaning about the data. When to use bubbles: Showing the concentration of data along two axes. Examples: sales • concentration by product and geography, class attendance by department and time of day. Also consider: Accentuate data on scatter plots: By varying the size and color of data points, • a scatterplot can be transformed into a rich visualization that answers many questions at once. • Overlay on maps: Bubbles quickly inform a viewer about relative concentration of data. Using these as an overlay on map puts geographically-related data in context quickly and effectively for a viewer. 16 Game Play Analysis Character Types Assassins & Fighters 6 Highlight Damagers & Tanks Tier A Tier B Tier C 5 KDA Tier D Choose C 4 Game Average Aldon Alekim Angok 3 Angust Arir Atril 2 Hybrid Characters 0.4 0.5 0.6 Healers Game Average 0.7 0.8 0.9 1.0 Avg. Win-Loss Ratio Brybur 1.1 1.2 1.3 1.4 Cereck Chyden 1.5 Drasayo Eldwori Summary Statistics Figure 12: Add data Enur Win-Loss Popularity Ratio depth with bubbles Matches KDA Avg Kills Avg Deaths Faor Avg Assists Garler 1.41 50,542 1.52 3.77 In thisSacha scatter plot accentuated with2.12% bubbles, the varied size3.18 and color of circles make it quick 10.86 Geess Joen 1.40 2.07% 49,493 3.695 2.5 3.91 10.21 Ghaia Hoet 1.39 1.89% 45,042 3.13 4.04 4.54 7.26 Hoet 3.08 4.37 9.51 6.04 5.71 7.07 Year Tier A to see how the game’s players compare. Click this dashboard then scroll over the bubbles to get instant access to more detailed information about each character. Turden 1.29 1.84% 43,876 3.465 Warhis Tier B Angok Sagha 1.24 1.89% 45,200 3.865 Crude1.22Net Balance by Country for 4.67 2009, 1.58% 37,699 4.54 Normalized by None 1.19 1.00% 23,975 3.955 4.81 5.27 5.49 Jitin 10.28 2009 9.27 Joen Kalldel Kelech Kelque Region All Net Exporte Net Importe Type Crude Normalization None Imports, Export Net Balance Figure 13: Oil imports and exports at a glance It’s easy to tell whowww.tableausoftware.com/mapdata buys and sells the most oil with green bubbles for net exporters and red About Tableau maps: details about consumption history. United States 9,609 -8.1% Saudi Arabia 6,824 -12.2% Russia 4,545 6.2% Japan 4,031 -13.3% o.. Do.. Do.. Do.. forThousands net importers overlaid on this map. Select a country on the Last mapDecade and the dashboard reveals of Barrels Latest to Prior Reserves 0 0 0 17 8. Histogram chart Use histograms when you want to see how your data are distributed across groups. Say, for example, that you’ve got 100 pumpkins and you want to know how many weigh 2 pounds or less, 3-5 pounds, 6-10 pounds, etc. By grouping your data into these categories then plotting them with vertical bars along an axis, you will see the distribution of your pumpkins according to weight. And, in the process, you’ve created a histogram. At times you won’t necessarily know which categorization approach makes sense for your data. You can use histograms to try different approaches to make sure you create groups that are balanced in size and relevant for your analysis. When to use histograms: Understanding the distribution of your data. Examples: Number of customers • by company size, student performance on an exam, frequency of a product defect. Also consider: • Test different groupings of data. When you are exploring your data and looking for groupings or “bins” that make sense, creating a variety of histograms can help you determine the most useful sets of data. Add a filter. By offering a way for the viewer to drill down into different categories of data, the histogram becomes a useful tool to explore a lot of data views quickly. King Co. SFH Sales Histogram [Sold 2012-06] 300 275 250 225 Number of Sales 200 175 150 125 100 75 50 Sale Month: Sold 2012-06 County: King Distress: All Pierce Snohomish Over $1,000k $900k to <$950k $950k to <$1,000k $850k to <$900k $800k to <$850k $750k to <$800k $700k to <$750k $650k to <$700k $600k to <$650k $550k to <$600k $500k to <$550k $450k to <$500k $400k to <$450k $350k to <$400k $300k to <$350k $250k to <$300k $200k to <$250k $0 to <$150k 25 0 $150k to <$200k • Distress Status Short Sale Bank Owned Non-Distressed Figure 14: Which houses are selling? This histogram shows which houses are seeing the most sales in a month. Explore for yourself how the histogram changes when you select a different month, county, or distress level. 18 9. Bullet chart When you’ve got a goal and want to track progress against it, bullet charts are for you. At its heart, a bullet graph is a variation of a bar chart. It was designed to replace dashboard gauges, meters and thermometers. Why? Because those images typically don’t display sufficient information and require valuable dashboard real estate. Bullet graphs compare a primary measure (let’s say, year-to-date revenue) to one or more other measures (such as annual revenue target) and presents this in the context of defined performance metrics (sales quota, for example). Looking at a bullet graph tells you instantly how the primary measure is performing against overall goals (such as how close a sales rep is to achieving her annual quota). When to use bullet graphs: Evaluating performance of a metric against a goal. Examples: sales quota • assessment, actual spending vs. budget, performance spectrum (great/good/poor). Also consider: Use color to illustrate achievement thresholds. Including color, such as • red, yellow, green as a backdrop to the primary measure lets the viewer quickly understand how performance measures against goals. • Add bullets to dashboards for summary insights. Combining bullets with other chart types into a dashboard supports productive discussions about where attention is needed to accomplish objectives. Quota Dashboard Company Total $0 $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 Regional Total (click to see salespeople in region) $12,000,000 $14,000,000 $16,000,000 Stats- All Central East West $0K $1,000K $2,000K $3,000K $4,000K $5,000K $6,000K $7,000K $8,000K Sales # of Sales People # Hitting Quota % Hitting Quota % of Sales by Quota Hitters Quota $ Sales $ Avg. Quota Avg. Sales per Person 41 27 65.9% 86.6% $11,825K $15,603K $275K $380,558 Salespeople in All Region: Sales ($) Barbara Davis Betty Clark Carol Allen Charles Lee Christopher Wright Daniel Gonzalez David Thompson Deborah Adams Donald Mitchell Donna Walker Dorothy Harris Elizabeth Miller Helen Rodriguez James Williams Jennifer Anderson Jessica Baker John Jones View by Quota (%) or Sales ($) % $ Hit Quota No Yes 0K 200K 400K 600K 800K Achievement: Quota (%) or Sales ($) 1000K 1200K Figure 15: Have you hit your quota? Tracking a sales team’s progression to hitting its quota is a critical element to managing success. In this quota dashboard, a sales manager can quickly select to view her team’s performance by quota percentage or sales amount as well as zero in on regional achievement. The surgical service teams at Seattle 2 : : Some kind of Header Here place, the person who makes sense of the data first is going to win. 5 19 Tableau has many great visualization capabilities. We useGet a lotresults of mapping, not only to show Speed: the geographnical location, but also to do aTableau lot is fast analytics. In 10 to 100 times faster of andatwe map relationships with place, the person who mak Thegeocoding surgical service teams Seattle first is going to win. geocoding the distances. 2 : : Some kind of Header Here – Marta Magnuszewska, Intelligence Data Analyst, Allstate Insurance 20 10. Heat maps Heat maps are a great way to compare data across two categories using color. The effect is to quickly see where the intersection of the categories is strongest and weakest. When to use heat maps: Showing the relationship between two factors. Examples: segmentation • analysis of target market, product adoption across regions, sales leads by individual rep. Also consider: Vary the size of squares. By adding a size variation for your squares, heat • maps let you know the concentration of two intersecting factors, but add a third element. For example, a heat map could reveal a survey respondent’s sports activity preference and the frequency with which they attend the event based on color, and the size of the square could reflect the number of respondents in that category. Using something other than squares. There are times when other types of Book Survey marks help convey your data in a Preference more impactful way. Favorite Type of Book by Age Favorite Type of Book by Income Category 8% % of Total Sum of Response % of Total Sum of Response • 6% 4% 2% 0% 40% 20% 0% 30 Select book type: Children's 40 50 60 70 80 <$50K <$100K <$250K <$500K <$750K $750K+ Highlight book type: Children's % of Total Weight by Age and Assets 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 <$50K <$100K <$250K <$500K <$750K $750K+ Figure 16: Who buys the most books? In this market segmentation analysis, the heat map reveals a new campaign idea. Highincome households of people in their sixties buy children’s books. Perhaps it’s time for a new grandparent-oriented campaign? 21 11. Highlight table Highlight tables take heat maps one step further. In addition to showing how data intersects by using color, highlight tables add a number on top to provide additional detail. When to use highlight tables: Providing detailed information on heat maps. Examples: the percent of a • market for different segments, sales numbers by a reps in a particular region, population of cities in different years. Also consider: Combine highlight tables with other chart types: Combining a line chart with • a highlight table, for example, lets a viewer understand overall trends as well as quickly drill down into a specific cross section of data. Comparing the 2012 Budget Proposals Select Item to Compare: Interest Highlight Budget: Obama Ryan $0.8T $0.6T $0.4T $0.2T $0.0T Program 2011 2012 Medicaid 1 10 2013 2014 2015 27 2016 106 151 179 Medicare -75 -92 -100 Interest -7 -16 3 30 48 63 Security 104 80 110 144 159 163 199 -107 -112 -119 -131 76 Non Sec. -69 -23 9 35 57 Other 2017 2018 2019 2020 228 248 272 2021 301 -139 -147 -151 -161 83 103 131 157 193 173 184 185 193 201 89 104 115 124 129 521 198 209 194 276 351 392 413 440 455 483 Soc Sec 0 1 3 6 7 8 9 7 5 5 6 Revenue -56 76 99 211 250 302 353 414 447 442 466 460 483 513 545 640 725 2320 2755 3244 Deficit 209 95 148 272 410 Nat Debt 486 434 560 797 1067 1312 1615 1950 Ryan more -21.8% What is the Spending Difference? Obama more 65.9% Figure 17: Highlight table shows spending difference This highlight table compares two 2012 budget proposals for the U.S. Click the table to learn more. 22 12. Treemap Looking to see your data at a glance and discover how the different pieces relate to the whole? Then treemaps are for you. These charts use a series of rectangles, nested within other rectangles, to show hierarchical data as a proportion to the whole. As the name of the chart suggests, think of your data as related like a tree: each branch is given a rectangle which represents how much data it comprises. Each rectangle is then sub-divided into smaller rectangles, or sub-branches, again based on its proportion to the whole. Through each rectangle’s size and color, you can often see patterns across parts of your data, such as whether a particular item is relevant, even across categories. They also make efficient use of space, allowing you to see your entire data set at once. When to use treemaps: Showing hierarchical data as a proportion of a whole: Examples: storage • usage across computer machines, managing the number and priority of technical support cases, comparing fiscal budgets between years Also consider: Coloring the rectangles by a category different from how they are • hierarchically structured • Combining treemaps with bar charts. In Tableau, place another dimension on Rows so that each bar in a bar chart is also a treemap. This lets you quickly compare items through the bar’s length, while allowing you to see the proportional relationships within each bar. 23 Support Case Overview Document Priority: P4 10,963 Document Priority: P3 3,987 Usability Priority: P4 2,721 Maintenance Priority: P4 7,845 Usability Priority: P3 2,680 Priority P1 P2 P3 P4 P5 Pre-Support Document Priority: P1 1,433 Document Priority: P2 Feedbacks Priority: P1 5,693 Feedbacks Priority: P4 2,854 Feedbacks Priority: P3 2,647 Support Priority: P1 4,261 Usability Priority: P2 Maintenance Feedbacks Priority: Feature Priority: P5 P2 4,680 2,230 Help Request Feedbacks Priority: P4 2,105 Support Support Priority: P3 Priority: Help Request Priority: P3 2,770 P2 1,995 2,132 Support Priority: P4 3,952 Other Priority: P5 4,870 #N/A Priority: P5 1,788 #N/A Help Request Priority: P2 1,144 Customer Services Priority: Setup Priority: P5 897 Figure 18: Support Cases at a Glance This treemap shows all of a company’s support cases, broken by case type, and also priority level. You can see that Document, Feedback, Support and Maintenance make up the lion share of support cases. However, in Feedback and Support, P1 cases make up the most number of cases, whereas most other categories are dominated by relatively mild P4 cases. World GDP Through Time 2001 2002 2003 Select Region All Highlight Region The Americas 2004 Europe Asia Middle East 2005 Oceania Africa 2006 Other Click Bar for Details 2007 2008 2009 2010 Figure 19: Visualizing World GDP In this treemap-bar chart combination chart, we can see how overall GDP has grown over time (with the exception of 2009, when GDP fell), but also which regions and countries comprised most of the world’s GDP. Since 2001, the region ‘The Americas’ made up most of the world’s GDP, until 2007 for three years. You can also see that GDP for ‘The Americas’ is made up of largely one rectangle (one country), whereas ‘Europe’ is made up of rectangles that are more similar in size. Click a rectangle to see which country it represents and how much GDP was produced (and how much per capita). 24 13. Box-and-whisker Plot Box-and-whisker plots, or boxplots, are an important way to show distributions of data. The name refers to the two parts of the plot: the box, which contains the median of the data along with the 1st and 3rd quartiles (25% greater and less than the median), and the whiskers, which typically represents data within 1.5 times the Inter-quartile Range (the difference between the 1st and 3rd quartiles). The whiskers can also be used to also show the maximum and minimum points within the data. When to use box-and-whisker plots: Showing the distribution of a set of a data: Examples: understanding your • data at a glance, seeing how data is skewed towards one end, identifying outliers in your data. Also consider: Hiding the points within the box. This helps a viewer focus on the outliers. • • Comparing boxplots across categorical dimensions. Boxplots are great at allowing you to quickly compare distributions between data sets. Two Weeks of Home Sales $4,500,000 Filter Date Range 9/16/13 to 10/1/13 $4,000,000 Filter by Home Type Condo/Coop Multi-Family (2-4 Unit) Multi-Family (5+ Unit) Parking Single Family Residential Townhouse Vacant Land $3,500,000 $3,000,000 $2,500,000 Homes Sold by City $2,000,000 Chicago $1,500,000 Los Angeles $1,000,000 Seattle $500,000 Washington DC $0 San Francisco Chicago Los Angeles San Francisco Seattle Washington DC 0 100 200 300 400 # of Homes Sold Figure 20: Comparing the sales prices of homes For this time period, the median prices of homes sold were highest in San Francisco, but the distribution was wider for Los Angeles. In fact, the most expensive home in Los Angeles was sold at several times greater than the median. Hover over a point to see its geographic location and how much it sold for. 25 About Tableau Tableau Software helps people see and understand data. Tableau helps anyone quickly analyze, visualize and share information. More than 15,000 customer accounts get rapid results with Tableau in the office and on-the-go. And tens of thousands of people use Tableau Public to share data in their blogs and websites. See how Tableau can help you by downloading the free trial at www.tableausoftware.com/trial. Additional Resources Download Free Trial Related Whitepapers Why Business Analytics in the Cloud? 5 Best Practices for Creating Effective Campaign Dashboards See All Whitepapers Explore Other Resources · Product Demo · Training & Tutorials · Community & Support · Customer Stories · Solutions Tableau and Tableau Software are trademarks of Tableau Software, Inc. All other company and product names may be trademarks of the respective companies with which they are associated.
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