DATA VISUALIZATION WITH TABLEAU
Pie Map with Axis, Heat Map, Donut Pie Chart, Funnel Chart, Radial Bar Chart, Tree Map, Unit Chart, Sankey, Sankey Ranking, Divergent Chart, Bar Map, Waffle, Rings, Radial Stacker Bar, Polygon Map and Bubble Map
RAUL CHOQUE LARRAURI
RAUL CHOQUE LARRAURI
DATA VISUALIZATION WITH TABLEAU
1 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
RAUL CHOQUE LARRAURI
Postdoctoral studies in Humanitarian Action from Groningen University in The Netherlands; Doctor in Education from National University of San Marcos of Peru; Master in Communication and Education from University of Barcelona in Spain; Master in Social Project and Program Management from University Cayetano Heredia of Peru; Bachelor degree in Mathematics Education from National University of San Marcos of Peru.
He is a professional with more than 15 years of experience working in different national and international organizations in Peru, Spain, The Netherlands and The United States of America.
In his professional experience, he has implemented the use of information systems and data visualization in different programs and projects using different tools, among them Tableau. He implemented the Balanced Scorecard in organizations as an information system for key performance indicators.
E-mail: [email protected]
Public Profile in Tableau: https://public.tableau.com/profile/raul.choque#!/
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DATA VISUALIZATION WITH TABLEAU First Edition. Lima, August 2017.
© Raúl Choque Larrauri © Ediciones Murrup E.I.R.L. 2017 Calle Loma de Las Magnolias 171, Santiago de Surco, Lima 33, Peru
Made Legal Deposit in Peru 2017-09876. National Library of Peru
All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information and storage retrievals system, without written permission from the copyright owner.
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INDEX
I. Presentation……………………………………………………………….. 05
II. History of Data Visualization………………………………………… 06
III. Steps for Data Visualization…………………………………………. 11
IV. Source Data……………………………………………………………….. 12
V. Data Format……………………………………………………………….. 14
VI. Specific Shapes…………………………………………………………… 14
1. Pie Map with Axis………………………………………………….. 15
2. Heat Map……………………………………………………………… 18
3. Donut Pie Chart…………………………………………………….. 20
4. Funnel Chart………………………………………………………….. 23
5. Radial Bar Chart…………………………………………………….. 28
6. Tree Map……………………………………………………………….. 36
7. Unit Chart……………………………………………………………… 40
8. Sankey…………………………………………………………………. 47
9. Sankey Ranking……………………………………………………… 60
10. Divergent Chart………………………………………………………. 65
11. Bar Map………………………………………………………………….. 68
12. Waffle Chart………………………………………………………………… 71
13. Rings………………………………………………………………………. 76
14. Radial Stacked Bar……………………………………………………. 84
15. Polygon Map……………………………………………………………. 100
16. Bubble Map……………………………………………………………… 106
17. Timeline Map…………………………………………………………… 110
VII. References ………………………………………………………………….. 113
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I. PRESENTATION
DATA VISUALIZATION WITH TABLEAU
Data visualization is a science, where the objective is to communicate information, using graphics, infographics and shapes about any topic or area. Nowadays, we have a lot of information, especially on the Internet, so it is necessary to systematize and organize the information to share with everyone.
Data visualization allows us to know trends, correlations, projection, statistics, etc., using different tools for understanding any aspect or information in different areas or topics
According to the report of “Digital 2017” produced by We Are Social and Hootsuite, we currently have 3.77 billion global internet users, equaling 50% of penetration; 2.80 billion global social media users, equaling 37% of penetration and 4.92 billion global mobile users, equaling 66% of penetration. So we are living in an interconnected society where the systematization and organization of the information are very important. In this new era, we need to share information and for this purpose it is necessary to use new tools with the goal of showing information in shapes or infographics.
Also, nowadays 500 million Tweets are sent each day; 3.6 billion Facebook messages are posted daily, 40 million Tweets are shared each day and according The Radacati Group 205 billion emails are sent each day. In this new context we need to know how information can be shared using tools and techniques, where readers can understand this information.
There are different tools to share information, but there is a special tool called Tableau, which is very easy to use and we can develop skills to use this tool very quickly. The data visualization needs skills, so in this manual you can learn how you can develop a graphic presentation of your information.
In this manual we are showing the most specialized kind of shapes for data visualization such as Pie Map with Axis, Heat Map, Donut Pie Chart, Funnel Chart, Radial Bar Chart, Tree Map, Unit Chart, Sankey, Sankey Ranking, Divergent Chart, Bar Map, Waffle, Rings, Radial Stacker Bar, Polygon Map and Bubble Map.
For the use of this manual it is necessary to know the basic knowledge of Tableau. This manual was elaborated with specific examples and the aim is that each person understands how to build a shape using Tableau.
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II. HISTORY OF DATA VISUALIZATION
In the history of the humanity, there were different authors and specialists that developed different shapes or initiatives in data visualization. In this part we show these authors and shapes that are very important to know.
The graphic called “Exports and Imports to and from Denmark & Norway from 1700 to 1780”, was created by William Playfair. This author is considered the father of information design. This author is the inventor of the pie chart, the bar graph, and the line graph. These are statistics graphics that we use every day in all areas. In this graphic the author presents gridlines to mark the years and the number of exports and imports. This is the first area chart in the history.
Exports and Imports to and from Denmark & Norway from 1700 to 1780 (William Playfair, 1786).
Source: https://www.digitalstudies.org/ojs/index.php/digital_studies/article/viewFile/305/439/2592
This line chart illustrates the total amount (In Pound Sterling) of imports and exports between England and the Dano-Norwegian Kingdom from 1700 to 1780. Each unit on X-axis represents a period of ten years whereas a unit on Y-axis equals ￡ 10,000. The imports to England are represented by a yellow line and the exports are represented by red line. The shading shows the interaction between two amounts each year – red showing a balance against England and yellow showing a balance against Denmark- Norway.
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LONDON CHOLERA OUTBREAK
In 1854, Dr. John Snow showed a special map of the Cholera Outbreak. He mapped the cases of cholera. The map represented each death as a bar. He also georeferenced every pump location in the map.
Map of Cholera Outbreak in London - 1854
Source: https://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak#/media/File:Snow- cholera-map-1.jpg
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MAP OF NAPOLEON´S DISASTROUS RUSSIAN CAMPAIGN OF 1812. Author: Charles Minard - 1869
Source: https://en.wikipedia.org/wiki/Charles_Joseph_Minard#/media/File:Minard.png
The graphic is notable for its representation in two dimensions of six types of data: the number of Napoleon's troops; distance; temperature; the latitude and longitude; direction of travel; and location relative to specific dates. The numbers of men present are represented by the widths of the coloured zones at a rate of one millimetre per ten thousand men; these are also written beside the zones. Orange designates men moving into Russia, black those on retreat. This is a Sankey graphic.
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HEAT MAP
The heat maps are tables or spreadsheets that have colors instead of numbers. The color of each cell or rectangle corresponds to the magnitude of the cell amount.
Toussaint Loua in his statistical atlas of the population of Paris in 1873 used a shaded matrix to display and summarize the characteristics of 20 districts in Paris. The characteristics that were shown are national origin, professions, social classes, age, etc. using a color scale ranging from white (low) through yellow and blue to red (high).
General Graphic of Statistical Atlas of the Population of Paris - 1873
Source: http://www.sci.utah.edu/~kpotter/Library/Papers/wilkinson:2009:HCHM/wilkinson_2009_HCHM _01.png
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PIE MAP
Charles Minard, Paris 1858. The map was developed using pie charts to represent the cattle sent from all around France for consumption in Paris.
Cattle sent to Paris - 1858
Source: https://www.pinterest.com/pin/112027109452289534
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NEW ERA
Nobel Prizes and Laureates 1901 – 2012
Source: Lupi, Giogia. 2012. "Nobel prizes and laureates 1901-2012." Flickr. Accessed June 23, 2017. https://www.flickr.com/photos/accurat/8249052633/in/set-72157632185046466
III. STEPS FOR DATA VISUALIZATION
There are five main steps that we should know and apply in the elaboration of data analysis:
1. OBJECTIVE. It is necessary to have a clear objective about the data that we need to communicate. 2. KNOW THE DATA. It is necessary to know what data we have now. What are the variables that we are using, what is the information on x-axis and y-axis for building the shape? What is the correlation between variables? etc. 3. MESSAGE of the visualization. We mean that it is necessary to have the clear message of the information for the target audience. 4. DESIGN. It is necessary to use the color, size, labels, shapes, scales, size, etc., to present the information. 5. SHARE the information through different media such as social networks. 11 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
IV. SOURCE DATA:
Source Information Link The World Bank - World Development Indicators http://databank.worldbank.org/data/home.aspx - Statistical Capacity Indicators - Education Statistics – All Indicators - Health Nutrition and Population Statistics Inter Parliamentary - Women in National Parliaments http://www.ipu.org/wmn-e/classif.htm Union - Statistical data from 1997 Scimago Journal & - Journal Rankings http://www.scimagojr.com/countryrank.php Country Rank - Country Rankings Institute of - International students and scholars in the United https://www.iie.org/Research-and-Insights/Open- International States Doors/Data/ Education (Open - American students studying abroad for academic Doors Data Portal) credit Institute for Health - Policy reports of Health http://www.healthdata.org/policy-report/financing- Metrics and - Country Profiles of Health global-health-2016-development-assistance-public-and- Evaluation private-health-spending Worldmeters - World Population http://www.worldometers.info/ - Government & Economics - Society & Media - Environment, Food, Water, Energy and Health Data OKFN - Government http://data.okfn.org/data/core/population - Social - Economics OECD Organization - Social http://stats.oecd.org/ for Economic - Economic Cooperation and - Demographic Development Global Health - Health http://www.who.int/gho/database/en/ Observatory - Profile in Health of Countries
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Source Information Link The International - World Motor Vehicle Production http://www.oica.net/category/production-statistics/ Organization of Motor - Information by country and type Vehicle Manufactures Centers for Disease - Health https://www.cdc.gov/datastatistics/index.html Control and Prevention Inter-American - Economics https://data.iadb.org/ Development Bank - Development United Nations Data - Data in different areas http://data.un.org/
United Nations - Population https://esa.un.org/unpd/wpp/ Population Division - Age composition of the population NCD Risk Factor - Non-communicable diseases http://www.ncdrisc.org/ Collaboration (NCD- RisC) United Nations - Human Development Data http://hdr.undp.org/en/data Development Program INEI PERU - Population http://www.inei.gob.pe/biblioteca-virtual/publicaciones- digitales/ Canadian Institute for - Health https://www.cihi.ca/en Health Information Public Health - Health http://www.phii.org/ Informatics Institute Data UNESCO - Education http://data.uis.unesco.org/
FAO DATA - Food and Agriculture http://www.fao.org/faostat/en/#home
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V. DATA FORMAT
The use of Tableau requires that the information must be formatted using correct parameters.
Date Quarter Gender Location
1/1/2018 Q1 Male 0231
12/30/2018 Q4 Female 0333
Each colum should represent a unique field, where the layout is vertical instead of horizontal.
The title or totals should not be included in the table. The table should be very clean and the information correspond to each column.
Tableau looks at the first row and determines the fields and in the second row it classifies the data. It identifies discrete versus continous and dimension versus measure.
VI. SPECIFIC SHAPES
In the following pages you can get specific shapes using Tableau.
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1. PIE MAP WITH AXIS
Definition:
A map with axis and pie representation in numbers or percentages is an important tool to represent different issues by each city, region, country, etc.
Percentage of Gender Distribution in Parliamentary Assemblies, 2017
Source: Inter Parliamentary Union, 2017
Data:
Country Male Female Bolivia 130 69 Argentina 257 100
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Country Male Female Ecuador 137 52 Peru 130 36 Venezuela 167 37 Uruguay 99 20 Colombia 166 31 Chile 120 19 Paraguay 80 11 Brazil 513 55 Guyana 69 22 Suriname 51 13
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/PieMap.xlsx
Steps to create a Pie Map with percentages:
Step 1: In Tableau, open a new workbook and connect to the file.
Step 2: In Dimensions search the dimension that correspond to the Geographical role (i.e. airport, area code U.S., city, Congressional District U.S., country/region, county, NUTS Europe, state/province, ZIP Code/Postcode or create one that you need). In this dimension activate the geographic role.
Step 3: From Dimensions, drag the data with Geographical role to view area.
Step 4: From Measures, drag Longitude to Columns. So, we should have two identical map views.
Step 5: There are now three drop-downs on the marks card that are the following: one for each map view, and one for both views that is represented by all. These are three separate marks cards that you can use to control the visual detail for each of the map views. It is important to know that if you are using a mark card this will be bold.
Step 6: Work first with the Left Map, so click the Longitude (generated) above on marks card. It will be bold.
Step 7: From Measures, drag the measure that is working to Color.
Step 8: Right-click on above Longitude (generated) from the marks card and edit below in pill SUM Color which should change to Continuous.
Step 9: We should work with right map, so click Longitude (generated) tabs below on marks card. It will be bold.
Step 10: Change the marks type. Click the mark type-down and selected “Pie” from “Automatic” on marks card. 16 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Step 11: Drag the Category from the Dimensions area under the data pane and place it on the “Color” on the marks card.
Step 12: From Measures, drag the measure that is working to Label.
Step 13: Right-click on Longitude (generated) from the marks card and edit in pill Label SUM which should change to continuous.
Step 14: From Measures, drag the measure that is working to Angle.
Step 15: You can change the size of the Pie.
Step 16: On columns shelf, on right longitude click and mark double axis.
CHANGE THE NUMBER TO PERCENTAGE IN PIE
Step 1: Right-click on pill Label SUM (Number…. ) of marks card.
Step 2: Click on ∆ Symbol. Select Add Table Calculation, where you should choose Percent of Total in Calculating Type. After you should choose Compute Using Specific Dimensions and sort order: specific dimensions.
Step 3: Click in Compute Using and click on correct dimension.
Step 4: On the map right click and choose Format. Choose percentage of total SUM and % with or decimal places.
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2. HEAT MAP
Definition:
A heat map is a two-dimensional representation of data. In a heat map we use color to display values. This kind of shape is used to show complex data and frequency of events at a given moment.
Most Common Birthdays in USA 1973-1999
Data Source: Amitabh Chandra, Harvard University.
Data:
Rank Month Day
365 1 1
320 1 2
The complete data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/06/Birthdays-USA-1973-1999.xls
Steps to create a heat map:
Step 1: Connect the Excel sheet to Tableau.
Step 2: Create a calculated field “Months” which corresponds to the month’s name.
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Name: Months If [Month] = 1 then “Jan” ELSEIF [Month] = 2 then “Feb” ELSIEF [Month] = 3 then “Mar” ELSIEF [Month] = 4 then “Apr” ELSIEF [Month] = 5 then “May” ELSIEF [Month] = 6 then “Jun” ELSIEF [Month] = 7 then “Jul” ELSIEF [Month] = 8 then “Aug” ELSIEF [Month] = 9 then “Sep” ELSIEF [Month] = 10 then “Oct” ELSIEF [Month] = 11 then “Nov” ELSIEF [Month] = 12 then “Dec” END
Step 3: Convert the dimension Day to Discrete.
Step 4: From Dimensions, drag Months to Columns.
Step 5: From Dimensions, drag Day to Rows.
Step 6: Change the marks type to Square from Automatic.
Step 7: From Measures, drag Rank to Color.
Step 8: Double click on Color to bring up the edit colors dialog box. Change the color from Green to any color. Mark reversed. Click on Apply and then ok.
Conclusion
The 16th day of September is ranked first, meaning the maximum number of babies were born on 16 September.
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3. DONUT PIE CHART
Definition:
A Donut Pie Chart is a tool that shows the relationship of parts to a whole. A Donut Pie Chart has an area of the center cut out. In the blank center you can display information. You can define the radius hole to any size you need.
Data:
Country Gender Year 2017 Year 2010 Bolivia Male 130 97 Argentina Male 257 158 Ecuador Male 137 84 Peru Male 130 87 Venezuela Male 167 137 Uruguay Male 99 84 Colombia Male 166 145 Chile Male 120 103 Paraguay Male 80 70 Brazil Male 513 469 Guyana Male 69 49 Suriname Male 51 46 Bolivia Female 69 33 Argentina Female 100 99 Ecuador Female 52 40 Peru Female 36 33
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Country Gender Year 2017 Year 2010 Venezuela Female 37 28 Uruguay Female 20 15 Colombia Female 31 21 Chile Female 19 17 Paraguay Female 11 10 Brazil Female 55 44 Guyana Female 22 21 Suriname Female 13 5
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Donut.xlsx
Steps to create a donut chart:
Step 1: In Data Source, in columns, select all columns with the information Year 2017 and Year 2010 using Ctrl.
Step 2: Once all columns with information of Years 2010 and 2017 have been selected, right-click on Year 2010 and select Pivot.
Step 3: From Measures, drag Number of records to Columns two times.
Step 4: Click-right on Number of records on Columns and change to the Attribute in the two Numbers of records.
Step 5: On right Number of records of the Column, click-right and select Dual Axis.
Step 6: The shape is displayed on the entire Screen.
Step 7: In all mark on Marks card change the marks type to Pie from Automatic.
Step 8: On the second mark on Marks card select size and put it small, after this in color you can select white.
Step 9: On the first mark, from Dimensions, drag Gender to Color.
Step 10: On the first mark, from Measures, drag Number of representatives to Size.
Step 11: On the first mark, from Measures, drag Number of representatives to Label.
Step 12: On the first mark, from Measures, drag Number of representatives to Angle.
Step 13: On the second mark, from Measures, drag Number of representatives to Label.
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Step 14: On the first mark, in pill Label: Number of representatives, click right and select Edit Table Calculation, in Calculation Type select Percent of Total, Table (across).
Step 15: From Dimensions, drag Years to Columns.
Step 16: In pills of Columns and Rows, unselect Show Header.
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4. FUNNEL CHART
Definition:
A funnel chart represents the stages in any process and shows the progress in each stage or also the comparison between actions or process.
Number of Applicants and Selected in National University of Engineering Peru 2009-2016
Source: Statistics Office UNI, 2017.
Data:
Year of Number of Faculty Career Condition Process People Architecture Architecture Applicant 2009-I 406 Architecture Architecture Applicant 2009-II 344 Architecture Architecture Applicant 2010-I 557 Architecture Architecture Applicant 2010-II 410 Architecture Architecture Applicant 2011-I 607 Architecture Architecture Applicant 2011-II 476 Architecture Architecture Applicant 2012-I 690 Architecture Architecture Applicant 2012-II 485 Architecture Architecture Applicant 2013-I 644 Architecture Architecture Applicant 2013-II 509 Architecture Architecture Applicant 2014-I 618 Architecture Architecture Applicant 2014-II 450 Architecture Architecture Applicant 2015-I 586
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Year of Number of Faculty Career Condition Process People Architecture Architecture Applicant 2015-II 445 Architecture Architecture Applicant 2016-I 617 Science Computer's Science Applicant 2009-I 0 Science Computer's Science Applicant 2009-II 0 Science Computer's Science Applicant 2010-I 42 Science Computer's Science Applicant 2010-II 34 Science Computer's Science Applicant 2011-I 46 Science Computer's Science Applicant 2011-II 34 Science Computer's Science Applicant 2012-I 38 Science Computer's Science Applicant 2012-II 26 Science Computer's Science Applicant 2013-I 41 Science Computer's Science Applicant 2013-II 34 Science Computer's Science Applicant 2014-I 30 Science Computer's Science Applicant 2014-II 49 Science Computer's Science Applicant 2015-I 44 Science Computer's Science Applicant 2015-II 37 Science Computer's Science Applicant 2016-I 46 Science Physics Applicant 2009-I 37 Science Physics Applicant 2009-II 33 Science Physics Applicant 2010-I 29 Science Physics Applicant 2010-II 19 Science Physics Applicant 2011-I 32 Science Physics Applicant 2011-II 33 Science Physics Applicant 2012-I 35 Science Physics Applicant 2012-II 36 Science Physics Applicant 2013-I 34 Science Physics Applicant 2013-II 31 Science Physics Applicant 2014-I 35 Science Physics Applicant 2014-II 35 Science Physics Applicant 2015-I 49 Science Physics Applicant 2015-II 35 Science Physics Applicant 2016-I 53 Environmental Environmental Engineering Engineering Applicant 2009-I 0 Environmental Environmental Engineering Engineering Applicant 2009-II 0 Environmental Environmental Engineering Engineering Applicant 2010-I 0 Environmental Environmental Engineering Engineering Applicant 2010-II 0 Environmental Environmental Engineering Engineering Applicant 2011-I 0 Environmental Environmental Engineering Engineering Applicant 2011-II 0
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Year of Number of Faculty Career Condition Process People Environmental Environmental Engineering Engineering Applicant 2012-I 150 Environmental Environmental Engineering Engineering Applicant 2012-II 134 Environmental Environmental Engineering Engineering Applicant 2013-I 217 Environmental Environmental Engineering Engineering Applicant 2013-II 170 Environmental Environmental Engineering Engineering Applicant 2014-I 217 Environmental Environmental Engineering Engineering Applicant 2014-II 154 Environmental Environmental Engineering Engineering Applicant 2015-I 204 Environmental Environmental Engineering Engineering Applicant 2015-II 127 Environmental Environmental Engineering Engineering Applicant 2016-I 207 Civil Engineering Civil Engineering Applicant 2009-I 1280 Civil Engineering Civil Engineering Applicant 2009-II 971 Civil Engineering Civil Engineering Applicant 2010-I 1689 Civil Engineering Civil Engineering Applicant 2010-II 1183 Civil Engineering Civil Engineering Applicant 2011-I 1957 Civil Engineering Civil Engineering Applicant 2011-II 1292 Civil Engineering Civil Engineering Applicant 2012-I 1923 Civil Engineering Civil Engineering Applicant 2012-II 1427 Civil Engineering Civil Engineering Applicant 2013-I 1994 Civil Engineering Civil Engineering Applicant 2013-II 1489 Civil Engineering Civil Engineering Applicant 2014-I 1933 Civil Engineering Civil Engineering Applicant 2014-II 1279 Civil Engineering Civil Engineering Applicant 2015-I 1667 Civil Engineering Civil Engineering Applicant 2015-II 1248 Civil Engineering Civil Engineering Applicant 2016-I 1655 Environmental Industrial Security Engineering Engineering Applicant 2009-I 63 Environmental Industrial Security Engineering Engineering Applicant 2009-II 59 Environmental Industrial Security Engineering Engineering Applicant 2010-I 88 Source: Statistics Office UNI, 2017.
The complete data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/06/Statistics-UNI.xlsx
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Steps to create a Funnel Chart:
Step 1: Connect the Excel sheet to Tableau.
Step 2: From Measures, drag Number of persons to Columns two times.
Step 3: From Dimensions, drag Careers to Rows.
Step 4: On Rows in Careers pill, click-right and select Sort, there select Sort order Descending and sort by Field: Number of people.
Step 5: On the left shape, click-right on the axis information below and select Edit Axis, where in Scale select Reversed.
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Step 6: On the second mark, from Measures, drag Number of People to Label.
Step 7: On the second mark, from Dimensions, drag Condition to Color.
Step 8: In all mark on Marks card change the marks type to Area from Automatic.
Step 9: From Dimensions, drag Year of process to Filters.
Step 10: Edit axis, color, label, etc.
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5. RADIAL BAR CHART
Definition: Radial bar chart shows the relationship of parts to a whole. A radial bar chart can contain sub categories for each part of the whole. Each category in the data series that is being plotted in a radial bar chart gets a different color and all the subcategories are given the same color. We can use a radial bar chart when: 1. There is a hierarchy in the data – For example, product category and product sub-category 2. Not more than 7 categories are present per data series 3. Categories represent parts of a whole in each ring
Data:
First, we need to get the data ready. The original data that needs to be plotted has to be duplicated. Introduce an additional field, Path Order, which holds 1 for one set of the data and 0 for the duplicate of the same data. The sample data is attached below. 28 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
We need the database with the following information.
Path Product Row Category Value Order ID ID Professional 0 1 1 0 Professional 0 2 2 0 Professional 0 3 3 0 Professional 0 4 4 0 Professional 0 5 5 0 Professional 0 6 6 0 Professional 0 7 7 0 Professional 0 8 8 0 Professional 0 9 9 0 Professional 0 10 10 0 Professional 0 11 11 0 Professional 0 12 12 0 Professional 0 13 13 0 Professional 0 14 14 0 Professional 0 15 15 0 Professional 0 16 16 0 Professional 0 17 17 0 Technical 0 18 18 0 Technical 0 19 19 0 Technical 0 20 20 0 Technical 0 21 21 0 Technical 0 22 22 0 Technical 0 23 23 0 Technical 0 24 24 0 Technical 0 25 25 0 Technical 0 26 26 0 Technical 0 27 27 0 Technical 0 28 28 0 Technical 0 29 29 0 Auxiliary 0 30 30 0 Auxiliary 0 31 31 0 Auxiliary 0 32 32 0 Auxiliary 0 33 33 0 Auxiliary 0 34 34 0 Auxiliary 0 35 35 0 Auxiliary 0 36 36 0 Auxiliary 0 37 37 0 Auxiliary 0 38 38 0 29 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Path Product Row Category Value Order ID ID Auxiliary 0 39 39 0 Auxiliary 0 40 40 0 Auxiliary 0 41 41 0 Auxiliary 0 42 42 0 Auxiliary 0 43 43 0 Auxiliary 0 44 44 0 Auxiliary 0 45 45 0 Auxiliary 0 46 46 0 Professional 1 1 1 250 Professional 1 2 2 345 Professional 1 3 3 234 Professional 1 4 4 221 Professional 1 5 5 110 Professional 1 6 6 98 Professional 1 7 7 99 Professional 1 8 8 201 Professional 1 9 9 60 Professional 1 10 10 30 Professional 1 11 11 150 Professional 1 12 12 154 Professional 1 13 13 123 Professional 1 14 14 112 Professional 1 15 15 76 Professional 1 16 16 149 Professional 1 17 17 173 Technical 1 18 18 68 Technical 1 19 19 124 Technical 1 20 20 89 Technical 1 21 21 197 Technical 1 22 22 175 Technical 1 23 23 345 Technical 1 24 24 172 Technical 1 25 25 152 Technical 1 26 26 174 Technical 1 27 27 143 Technical 1 28 28 189 Technical 1 29 29 199 Auxiliary 1 30 30 234 Auxiliary 1 31 31 164
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Path Product Row Category Value Order ID ID Auxiliary 1 32 32 235 Auxiliary 1 33 33 112 Auxiliary 1 34 34 192 Auxiliary 1 35 35 234 Auxiliary 1 36 36 187 Auxiliary 1 37 37 135 Auxiliary 1 38 38 193 Auxiliary 1 39 39 323 Auxiliary 1 40 40 234 Auxiliary 1 41 41 221 Auxiliary 1 42 42 221 Auxiliary 1 43 43 156 Auxiliary 1 44 44 99 Auxiliary 1 45 45 98 Auxiliary 1 46 46 121
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/RadialBar.xlsx
Steps to create a Radial Bar Chart:
Step 1: Create the following Calculated Fields:
Name: Calculation1 IIF([Path Order]=1,[Value],NULL)
Name: RADIAL_FIELD [Value]
Name: RADIAL_ANGLE (INDEX()-1) * (1/WINDOW_COUNT(COUNT([RADIAL_FIELD]))) * 2 * PI()
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Step 2: Create the following Parameters:
Name:
RADIAL_INNER
Name: RADIAL_OUTER
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Name: RADIAL_SELECTIVE_LABEL_THRESHOLD
Step 3: Create the following calculated fields:
Name: RADIAL_NORMALISED_LENGTH [RADIAL_INNER] + IIF(ATTR([Path Order]) = 0 , 0 , SUM([RADIAL_FIELD])/WINDOW_MAX(SUM([RADIAL_FIELD])) * ([RADIAL_OUTER]- [RADIAL_INNER])) //[RADIAL_OUTER]
Name: RADIAL_SELECTIVE_LABEL IIF(SUM([RADIAL_FIELD])>[RADIAL_SELECTIVE_LABEL_THRESHOLD], SUM([RADIAL_FIELD]), NULL)
RADIAL_X [RADIAL_NORMALISED_LENGTH] * COS([RADIAL_ANGLE])
RADIAL_Y [RADIAL_NORMALISED_LENGTH] * SIN([RADIAL_ANGLE])
Step 4: From Measures, drag RADIAL_X to Columns.
Step 5: From Measures, drag RADIAL_Y to Rows.
Step 6: In Measures we need to combine the “Category” and “Product ID” fields to create a combined field on which we can perform all the calculations. Click Category then Ctrl+Click item and right-click and select Combine Field.
Step 7: From Dimensions, drag Category to Color.
Step 8: From Dimensions, drag Category & Product ID to Detail.
Step 9: In the Marks choose Line. 33 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Step 10: From Dimensions, drag Path Order to Path. Right-click and change to Dimension.
Step 11: From Dimensions, drag Product ID to Tooltip.
Step 12: From Dimensions, drag RADIAL_SELECTIVE_LABEL to Label.
Step 13: From Measures, drag Calculation 1 to Label.
Step 14: Right click on RADIAL_X in Columns, and select Compute Using combined.
Step 15: Right click on RADIAL_Y in Rows, and select Compute Using combined.
Step 16: In Parameter RADIAL_INNER right-click and select Show Parameter Control. In RADIAL_INNER select 0.3 or other that you need.
Step 17: Edit the Axes so that the range is fixed from -1 to 1.
Step 18: We can change the size of the bars, on the circle, by changing the size slider on the Marks card. Format the chart to remove the Gridlines and zero lines. Also you can drag from Dimensions Category to Filters and right-click to show filters.
With Filters
Name: RADIAL_FIELD CASE [RADIAL_FIELD_USE] WHEN "Sales" THEN [Sales] WHEN "Profit" THEN [Profit] END
PARAMETER
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RADIAL_FIELD_USE Click-right and select Show Parameter Control
Name: RADIAL_SELECTIVE_LABEL
IIF(SUM([RADIAL_FIELD])>[RADIAL_SELECTIVE_LABEL_THRESHOLD], SUM([RADIAL_FIELD]), NULL)
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6. TREE MAP
Definition:
A Tree Map is a method for displaying hierarchical data using rectangles. Each category is assigned a rectangular area with its subcategory rectangles inside of it. The area of a Tree Map is displayed in proportion to the quantity that is assigned to the category and the other quantities within the same parent category in a part to whole relationship. The area of the parent category is the total of its subcategories.
The way rectangles are divided and ordered into sub-rectangles in the shape. A Tree Map is a great tool comparing the proportions between categories via their size.
Population by countries 1960 – 2016
Source: World Bank, 2017.
Data:
Year 2012 Year 2013 Year 2014 Year 2015 Year 2016 C ountry Region 102577 103187 103795 104341 104822 Aruba The Americas 30696958 31731688 32758020 33736494 34656032 Afghanistan Asia 25096150 25998340 26920466 27859305 28813463 Angola Africa 2900401 2895092 2889104 2880703 2876101 Albania Europe 82431 80788 79223 78014 77281 Andorra Europe United Arab 8900453 9006263 9070867 9154302 9269612 Emirates Middle East 42096739 42539925 42981515 43417765 43847430 Argentina The Americas 2881922 2893509 2906220 2916950 2924816 Armenia Asia 36 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Year 2012 Year 2013 Year 2014 Year 2015 Year 2016 C ountry Region American 55230 55307 55437 55537 55599 Samoa Oceania Antigua and 96777 97824 98875 99923 100963 Barbuda The Americas 22728254 23117353 23460694 23789338 24127159 Australia Oceania 8429991 8479375 8541575 8633169 8747358 Austria Europe 9295784 9416801 9535079 9649341 9762274 Azerbaijan Asia 9319710 9600186 9891790 10199270 10524117 Burundi Africa 11128246 11182817 11209057 11274196 11348159 Belgium Europe 9729160 10004451 10286712 10575952 10872298 Benin Africa 16571216 17072723 17585977 18110624 18646433 Burkina Faso Africa 155727053 157571292 159405279 161200886 162951560 Bangladesh Asia 7305888 7265115 7223938 7177991 7127822 Bulgaria Europe 1300217 1315411 1336397 1371855 1425171 Bahrain Middle East
The complete data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/2016.xlsx
Steps to create a Tree Map:
Step 1: Connect the Excel sheet to Tableau.
Step 2: Pivot the data. In Data Source, select all columns with population by year using Ctrl.
Step 3: Once all columns with population by year have been selected, click the drop- down arrow next to the columns name, and then select Pivot. New columns replace the original columns that we selected to create the pivot.
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Step 4: In Data Source change the name of Pivot Fields Name to Years and Pivot Field Values to Population.
Step 5: From Dimensions, drag Region to Color.
Step 6: From Dimensions, drag Country Name to Details.
Step 7: From Measures, drag Population to Size and change the Measure to SUM.
Step 8: From Dimensions, drag Country Name to Label.
Step 9: From Measures, drag Population to Label.
Step 10: From Dimensions, drag Years to Filters.
Step 11: Build the Level of Detail.
Create a new parameter:
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Step 12: Right click on the new parameter and select Show Parameter Control.
Create a Calculated Field:
Name: Detail Level if [Select Region] = [Region] then [Country Name] elseif [Select Region] = 'All' then [Country Name] else [Region] END
Step 13: Delete the two pills Country Name on Marks.
Step 14: From Dimensions, drag Detail Level to Label.
Step 15: When you select Region, the level of detail will be selected.
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7. UNIT CHART
Definition:
Unit charts or pictograms charts display each unit of measure as a single mark or symbol.
Type of Professor at the University
Data:
Position Department Assistant Professor Department of Economics Associate Professor Department of Economics Assistant Professor Department of Economics Associate Professor Department of Economics Associate Professor Department of Economics Professor Department of Economics Professor Department of Economics Professor Department of Economics Professor Department of Economics Associate Professor Department of Economics Associate Professor Department of Economics Associate Professor Department of Economics Professor Department of Economics Associate Professor Department of Economics Assistant Professor Department of Economics Assistant Professor Department of Economics Associate Professor Department of Marketing
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Position Department Associate Professor Department of Marketing Associate Professor Department of Marketing Assistant Professor Department of Marketing Assistant Professor Department of Marketing Assistant Professor Department of Marketing Assistant Professor Department of Marketing Assistant Professor Department of Marketing Assistant Professor Department of Marketing Assistant Professor Department of Marketing Associate Professor Department of Marketing Associate Professor Department of Marketing Associate Professor Department of Marketing Associate Professor Department of Marketing Associate Professor Department of Marketing Professor Department of Marketing Professor Department of Marketing Associate Professor Department of Marketing Assistant Professor Department of Marketing Associate Professor Department of Marketing Associate Professor Department of Tourism Assistant Professor Department of Tourism Associate Professor Department of Tourism Assistant Professor Department of Tourism Assistant Professor Department of Tourism Associate Professor Department of Tourism Associate Professor Department of Tourism Associate Professor Department of Tourism Assistant Professor Department of Tourism Assistant Professor Department of Tourism Associate Professor Department of Tourism Assistant Professor Department of Journalism Assistant Professor Department of Journalism Associate Professor Department of Journalism Associate Professor Department of Journalism Associate Professor Department of Journalism Associate Professor Department of Journalism Professor Department of Journalism Professor Department of Journalism Professor Department of Journalism Associate Professor Department of Journalism Associate Professor Department of Journalism Professor Department of Journalism Professor Department of Journalism
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Position Department Associate Professor Department of Journalism Associate Professor Department of Journalism Professor Department of Journalism Professor Department of Journalism Professor Department of Nutrition Professor Department of Nutrition Assistant Professor Department of Nutrition Assistant Professor Department of Nutrition Assistant Professor Department of Nutrition Professor Department of Nutrition Professor Department of Nutrition Professor Department of Nutrition Professor Department of Nutrition Professor Department of Nutrition Associate Professor Department of Nutrition Associate Professor Department of Nutrition Associate Professor Department of Nutrition Assistant Professor Department of Nutrition Assistant Professor Department of Nutrition Professor Department of Nursing Associate Professor Department of Nursing Associate Professor Department of Nursing Professor Department of Nursing Professor Department of Nursing Assistant Professor Department of Nursing Associate Professor Department of Nursing Professor Department of Nursing Associate Professor Department of Nursing Assistant Professor Department of Nursing Associate Professor Department of Nursing Associate Professor Department of Nursing Associate Professor Department of Nursing Associate Professor Department of Nursing Associate Professor Department of Nursing Professor Department of Nursing Professor Department of Nursing Assistant Professor Department of Nursing Assistant Professor Department of Nursing Assistant Professor Department of Statistics Assistant Professor Department of Statistics Assistant Professor Department of Statistics Assistant Professor Department of Statistics Associate Professor Department of Statistics
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Position Department Assistant Professor Department of Statistics Assistant Professor Department of Statistics Assistant Professor Department of Statistics Assistant Professor Department of Statistics Assistant Professor Department of Statistics Associate Professor Department of Statistics Associate Professor Department of Statistics Professor Department of Statistics Professor Department of Statistics Professor Department of Statistics Assistant Professor Department of Statistics Assistant Professor Department of Statistics Assistant Professor Department of Statistics Associate Professor Department of Statistics Associate Professor Department of Statistics Assistant Professor Department of Chemistry Associate Professor Department of Chemistry Assistant Professor Department of Chemistry Associate Professor Department of Chemistry Associate Professor Department of Chemistry Professor Department of Chemistry Professor Department of Chemistry Professor Department of Chemistry Professor Department of Chemistry Associate Professor Department of Chemistry Associate Professor Department of Chemistry Associate Professor Department of Chemistry Professor Department of Chemistry Associate Professor Department of Chemistry Assistant Professor Department of Chemistry Assistant Professor Department of Chemistry Associate Professor Department of Chemistry Associate Professor Department of Chemistry Associate Professor Department of Entomology Assistant Professor Department of Entomology Assistant Professor Department of Entomology Assistant Professor Department of Entomology Assistant Professor Department of Entomology Assistant Professor Department of Entomology Assistant Professor Department of Entomology Assistant Professor Department of Entomology Associate Professor Department of Entomology Associate Professor Department of Entomology
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Position Department Associate Professor Department of Entomology Associate Professor Department of Entomology Associate Professor Department of Entomology Professor Department of Entomology Professor Department of Entomology Associate Professor Department of Entomology Assistant Professor Department of Entomology Associate Professor Department of Entomology Associate Professor Department of History Assistant Professor Department of History Associate Professor Department of History Assistant Professor Department of History Assistant Professor Department of History Associate Professor Department of History Associate Professor Department of History Associate Professor Department of History Assistant Professor Department of History Assistant Professor Department of History Associate Professor Department of History Assistant Professor Department of History Assistant Professor Department of History Associate Professor Department of History Associate Professor Department of English Associate Professor Department of English Associate Professor Department of English Professor Department of English Professor Department of English Professor Department of English Associate Professor Department of English Associate Professor Department of English Professor Department of English Professor Department of English Associate Professor Department of English Associate Professor Department of English Professor Department of English Professor Department of English Professor Department of English Professor Department of English Assistant Professor Department of English Assistant Professor Department of English Assistant Professor Department of English Professor Department of English Professor Department of Physiology
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Position Department Professor Department of Physiology Professor Department of Physiology Professor Department of Physiology Associate Professor Department of Physiology Associate Professor Department of Physiology Associate Professor Department of Physiology Assistant Professor Department of Physiology Assistant Professor Department of Physiology Professor Department of Physiology Associate Professor Department of Physiology Associate Professor Department of Physiology Professor Department of Physiology Professor Department of Physiology Assistant Professor Department of Physiology Associate Professor Department of Physiology Professor Department of Physiology Associate Professor Department of Physiology Assistant Professor Department of Physiology Associate Professor Department of Physiology Associate Professor Department of Physiology Associate Professor Department of Physiology Associate Professor Department of Physiology Associate Professor Department of Physiology Professor Department of Physiology Professor Department of Physiology Assistant Professor Department of Physiology Assistant Professor Department of Physiology Assistant Professor Department of Physiology Assistant Professor Department of Physiology Assistant Professor Department of Physiology Assistant Professor Department of Computation Associate Professor Department of Computation Assistant Professor Department of Computation Assistant Professor Department of Computation Assistant Professor Department of Computation Assistant Professor Department of Computation Assistant Professor Department of Computation Associate Professor Department of Computation Associate Professor Department of Computation Professor Department of Computation Professor Department of Computation Professor Department of Computation Assistant Professor Department of Computation
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Position Department Assistant Professor Department of Computation Assistant Professor Department of Computation Associate Professor Department of Computation Associate Professor Department of Computation
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/UnitChart.xlsx
Steps to create a Unit Chart:
1. Step 1: From Dimensions, drag Department to Rows.
2. Step 2: Create a calculated field called #Staff. This field should be fixed to Department and Position, so that it is not affected by the data fields used in the view.
Name: #Staff { FIXED [Department]: COUNT([Position])}
3. Step 3: From Dimensions, drag #Staff, to the right of Department on Rows. In this step by default, this creates a bar chart.
4. Step 4: Right-click on the SUM(#Staff) in Rows, and select Discrete to display it as text instead of a bar chart.
5. Step 5: Change the mark of the view to Circle.
6. Step 6: From Dimensions, drag Position to Color.
7. Step 7: From Measures, drag Number of Records to Details. This will create a circle for each person in the university.
8. Step 8: Click the Analysis menu and uncheck Aggregate Measures.
9. Step 9: Click on the drop-down arrow beside the color legend again. This time choose Edit colors according to your preferences.
10. Step 10: Manually reorder the Position color by dragging the names in the correct order.
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8. SANKEY
Definition
A Sankey shape is a specific type of flow diagram. In this kind of shape the width of the arrows is shown proportionally to the flow quantity. This diagram puts a visual emphasis on the major transfer or flows within a system.
We use Sankey chart to present a relationship of two or more situations.
Data:
Country Origin Year Name Destination University Name 2017 RowType China New York University 5,438 Real China University of Southern California 4,234 Real China Arizona State University - Tempe 4,321 Real China Columbia University 3,876 Real China University of Illinois 3,567 Real China Northeastern University 3,453 Real China University of California 3,450 Real China Purdue University 3,300 Real China Boston University 3,200 Real
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Country Origin Year Name Destination University Name 2017 RowType China University of Washington 3,100 Real India New York University 2,616 Real India University of Southern California 2,600 Real India Arizona State University - Tempe 2,545 Real India Columbia University 2,345 Real India University of Illinois 2,300 Real India Northeastern University 2,290 Real India University of California 2,280 Real India Purdue University 2,270 Real India Boston University 2,260 Real India University of Washington 2,250 Real Saudi Arabia New York University 1,500 Real Saudi Arabia University of Southern California 1,490 Real Saudi Arabia Arizona State University - Tempe 1,480 Real Saudi Arabia Columbia University 1,470 Real Saudi Arabia University of Illinois 1,460 Real Saudi Arabia Northeastern University 1,450 Real Saudi Arabia University of California 1,440 Real Saudi Arabia Purdue University 1,430 Real Saudi Arabia Boston University 1,420 Real Saudi Arabia University of Washington 1,410 Real South Korea New York University 1,146 Real South Korea University of Southern California 1,140 Real South Korea Arizona State University - Tempe 1,130 Real South Korea Columbia University 1,120 Real South Korea University of Illinois 1,110 Real South Korea Northeastern University 1,100 Real South Korea University of California 1,090 Real South Korea Purdue University 1,080 Real South Korea Boston University 1,070 Real South Korea University of Washington 1,060 Real Canada New York University 855 Real Canada University of Southern California 840 Real Canada Arizona State University - Tempe 830 Real Canada Columbia University 829 Real Canada University of Illinois 820 Real Canada Northeastern University 815 Real Canada University of California 810 Real Canada Purdue University 805 Real Canada Boston University 804 Real Canada University of Washington 803 Real Vietnam New York University 600 Real Vietnam University of Southern California 598 Real 48 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Country Origin Year Name Destination University Name 2017 RowType Vietnam Arizona State University - Tempe 597 Real Vietnam Columbia University 596 Real Vietnam University of Illinois 595 Real Vietnam Northeastern University 594 Real Vietnam University of California 593 Real Vietnam Purdue University 592 Real Vietnam Boston University 591 Real Vietnam University of Washington 580 Real Taiwan New York University 546 Real Taiwan University of Southern California 543 Real Taiwan Arizona State University - Tempe 542 Real Taiwan Columbia University 541 Real Taiwan University of Illinois 540 Real Taiwan Northeastern University 530 Real Taiwan University of California 529 Real Taiwan Purdue University 528 Real Taiwan Boston University 527 Real Taiwan University of Washington 526 Real Brazil New York University 500 Real Brazil University of Southern California 499 Real Brazil Arizona State University - Tempe 498 Real Brazil Columbia University 497 Real Brazil University of Illinois 496 Real Brazil Northeastern University 495 Real Brazil University of California 494 Real Brazil Purdue University 493 Real Brazil Boston University 492 Real Brazil University of Washington 491 Real Japan New York University 480 Real Japan University of Southern California 470 Real Japan Arizona State University - Tempe 465 Real Japan Columbia University 460 Real Japan University of Illinois 459 Real Japan Northeastern University 548 Real Japan University of California 546 Real Japan Purdue University 455 Real Japan Boston University 454 Real Japan University of Washington 453 Real Mexico New York University 452 Real Mexico University of Southern California 451 Real Mexico Arizona State University - Tempe 450 Real Mexico Columbia University 449 Real Mexico University of Illinois 448 Real 49 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Country Origin Year Name Destination University Name 2017 RowType Mexico Northeastern University 447 Real Mexico University of California 446 Real Mexico Purdue University 445 Real Mexico Boston University 443 Real Mexico University of Washington 442 Real China New York University 5,438 Dummy China University of Southern California 4,234 Dummy China Arizona State University - Tempe 4,321 Dummy China Columbia University 3,876 Dummy China University of Illinois 3,567 Dummy China Northeastern University 3,453 Dummy China University of California 3,450 Dummy China Purdue University 3,300 Dummy China Boston University 3,200 Dummy China University of Washington 3,100 Dummy India New York University 2,616 Dummy India University of Southern California 2,600 Dummy India Arizona State University - Tempe 2,545 Dummy India Columbia University 2,345 Dummy India University of Illinois 2,300 Dummy India Northeastern University 2,290 Dummy India University of California 2,280 Dummy India Purdue University 2,270 Dummy India Boston University 2,260 Dummy India University of Washington 2,250 Dummy Saudi Arabia New York University 1,500 Dummy Saudi Arabia University of Southern California 1,490 Dummy Saudi Arabia Arizona State University - Tempe 1,480 Dummy Saudi Arabia Columbia University 1,470 Dummy Saudi Arabia University of Illinois 1,460 Dummy Saudi Arabia Northeastern University 1,450 Dummy Saudi Arabia University of California 1,440 Dummy Saudi Arabia Purdue University 1,430 Dummy Saudi Arabia Boston University 1,420 Dummy Saudi Arabia University of Washington 1,410 Dummy South Korea New York University 1,146 Dummy South Korea University of Southern California 1,140 Dummy South Korea Arizona State University - Tempe 1,130 Dummy South Korea Columbia University 1,120 Dummy South Korea University of Illinois 1,110 Dummy South Korea Northeastern University 1,100 Dummy South Korea University of California 1,090 Dummy South Korea Purdue University 1,080 Dummy 50 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Country Origin Year Name Destination University Name 2017 RowType South Korea Boston University 1,070 Dummy South Korea University of Washington 1,060 Dummy Canada New York University 855 Dummy Canada University of Southern California 840 Dummy Canada Arizona State University - Tempe 830 Dummy Canada Columbia University 829 Dummy Canada University of Illinois 820 Dummy Canada Northeastern University 815 Dummy Canada University of California 810 Dummy Canada Purdue University 805 Dummy Canada Boston University 804 Dummy Canada University of Washington 803 Dummy Vietnam New York University 600 Dummy Vietnam University of Southern California 598 Dummy Vietnam Arizona State University - Tempe 597 Dummy Vietnam Columbia University 596 Dummy Vietnam University of Illinois 595 Dummy Vietnam Northeastern University 594 Dummy Vietnam University of California 593 Dummy Vietnam Purdue University 592 Dummy Vietnam Boston University 591 Dummy Vietnam University of Washington 580 Dummy Taiwan New York University 546 Dummy Taiwan University of Southern California 543 Dummy Taiwan Arizona State University - Tempe 542 Dummy Taiwan Columbia University 541 Dummy Taiwan University of Illinois 540 Dummy Taiwan Northeastern University 530 Dummy Taiwan University of California 529 Dummy Taiwan Purdue University 528 Dummy Taiwan Boston University 527 Dummy Taiwan University of Washington 526 Dummy Brazil New York University 500 Dummy Brazil University of Southern California 499 Dummy Brazil Arizona State University - Tempe 498 Dummy Brazil Columbia University 497 Dummy Brazil University of Illinois 496 Dummy Brazil Northeastern University 495 Dummy Brazil University of California 494 Dummy Brazil Purdue University 493 Dummy Brazil Boston University 492 Dummy Brazil University of Washington 491 Dummy Japan New York University 480 Dummy 51 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Country Origin Year Name Destination University Name 2017 RowType Japan University of Southern California 470 Dummy Japan Arizona State University - Tempe 465 Dummy Japan Columbia University 460 Dummy Japan University of Illinois 459 Dummy Japan Northeastern University 548 Dummy Japan University of California 546 Dummy Japan Purdue University 455 Dummy Japan Boston University 454 Dummy Japan University of Washington 453 Dummy Mexico New York University 452 Dummy Mexico University of Southern California 451 Dummy Mexico Arizona State University - Tempe 450 Dummy Mexico Columbia University 449 Dummy Mexico University of Illinois 448 Dummy Mexico Northeastern University 447 Dummy Mexico University of California 446 Dummy Mexico Purdue University 445 Dummy Mexico Boston University 443 Dummy Mexico University of Washington 442 Dummy
This data is only an example. The numbers are just a projection.
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Sankey.xlsx
Steps to Create a Sankey Chart:
Step 1: Create the following Calculated Fields:
Name: T (INDEX()-25)/4
Name: Sigmoid 1/(1+EXP(1)^-[T])
Name: To Pad if [RowType]="Real" then 1 else 49 end
Step 2: Create a Bins of To Pad. Click right on To Pad and select Bins.
Name: Padded
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Step 3: Create the following Calculated Fields:
Name: Sizing for Years RUNNING_AVG(sum([Year 2017]))
Name: Rank1 RUNNING_SUM(Sum([Year 2017]))/total(sum([Year 2017]))
Name: Rank2 RUNNING_SUM(Sum([Year 2017]))/total(sum([Year 2017]))
Name: Curve [Rank1]+(([Rank2]-[Rank1])*[Sigmoid])
Step 4: From Measures, drag T to Columns.
Step 5: From Measures, drag Curve to Rows.
Step 6: On Marks change to Lines.
Step 7: From Dimensions, drag Country origin to Color.
Step 8: From Measures, drag Sizing for Year 2017 to Size. Click right and select Compute using Padded.
Step 9: Drag from Dimensions Padded to Detail.
Step 10: Drag from Dimensions Country Origin to Detail.
Step 11: Drag from Dimensions University Destination name to Detail.
Step 12: Drag from Dimensions Padded to Path.
Step 13: On T, click right and select Compute using Padded.
Step 14: On Curve, click right and select the following:
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Rank 1
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Rank 2
Step 15: On the Marks Card we should have the following actions:
Step 16: Edit Axis Curve 0 to 1 and Reversed. Edit Axis T -5, 5. On T and Curve click right and select No Show Header.
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Step 17: In other sheet, create a segment reference of Country of Origin. From Measures, drag Year 2017 to Rows. From Dimensions, drag Country Origin to Color and Country Origin to Label. From Measures, drag Year 2017 to Rows.
Step 18: In other sheet, create a segment reference of University of Destination. From Measures, drag University Destination to Rows. From Dimensions, drag University Destination to Color and University Destination to Label. From Measures, drag Year 2017 to Rows.
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Step 19: Relation in a Dashboard of segment reference and Sankey. Click on Dashboard and select Actions. In Add Action select Highlight.
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Steps to make this shape with Filters:
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/06/Data- Book-Sankey.xlsx
Create a Parameter:
Create the following Calculated Fields:
Name: Years17-21 CASE [Parameters].[Years] WHEN '2017' THEN SUM([Year 2017]) WHEN '2018' THEN SUM([Year 2018]) WHEN '2019' THEN SUM([Year 2019]) WHEN '2020' THEN SUM([Year 2020]) WHEN '2021' THEN SUM([Year 2021]) END
Name: Rank1 RUNNING_SUM([ Years 17-21])/TOTAL([ Years 17-21])
Name: Rank2 RUNNING_SUM([ Years 17-21])/TOTAL([ Years 17-21])
Name: Curve [Rank1]+(([Rank2] - [Rank1])*[Sigmoid])
Name: Path Size RUNNING_AVG([ Years 17-21])
Gender to Filter Show Parameter Control
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The steps should be the same as that in the simple kind.
Path Size using Padded.
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9. SANKEY RANKING
Definition:
This is a kind of Sankey shape, where you can show the ranking and relationship of different situations.
Data:
REGIONAL COMPETITIVINES PERU 2015 – 2016
Rank Region Year 1 Lima 2015 1 Lima 2016 2 Moquegua 2015 2 Moquegua 2016 3 Arequipa 2015 3 Arequipa 2016 4 Ica 2015 60 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Rank Region Year 4 Ica 2016 5 Tacna 2015 5 Tacna 2016 6 Madre de Dios 2015 6 Madre de Dios 2016 7 Tumbes 2015 7 Tumbes 2016 8 Cusco 2015 10 Cusco 2016 9 Lambayeque 2015 8 Lambayeque 2016 10 La Libertad 2015 9 La Libertad 2016 11 Ancash 2015 11 Ancash 2016 12 Piura 2015 13 Piura 2016 13 Junin 2015 12 Junin 2016 14 San Martin 2015 14 San Martin 2016 15 Apurimac 2015 16 Apurimac 2016 16 Ayacucho 2015 17 Ayacucho 2016 17 Amazonas 2015 21 Amazonas 2016 18 Ucayali 2015 15 Ucayali 2016 19 Huancavelica 2015 19 Huancavelica 2016 20 Cerro de Pasco 2015 18 Cerro de Pasco 2016 21 Huanuco 2015 20 Huanuco 2016 22 Puno 2015 22 Puno 2016 23 Cajamarca 2015 24 Cajamarca 2016 24 Loreto 2015 23 Loreto 2016
The data can be downloaded from:
61 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Sankey2.xlsx
Steps to Create a Sankey Ranking:
Step 1: Create the following calculated fields:
Name: Index INDEX()
Name: X 0.25*[Index]-6.25 Name: Sigmoid 1/(1+EXP(-[X]))
Step 2: Create a Parameter # of points
Step 3: Create the following calculated fields:
Name: Point if [Year]=2015 then 1 ELSE [# of points] END
Name: Change
WINDOW_MIN(IF LAST() =0 THEN MIN([Rank]) END)-
WINDOW_MIN(IF FIRST()=0 THEN MIN([Rank]) END)
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Name: Curve WINDOW_MIN(IF FIRST()=0 THEN MIN([Rank]) END)+[Sigmoid]*[Change]
Step 4: Create a Padded of Point
Step 5: From Measures, drag X to Columns shelf.
Step 6: From Measures, drag Curve to Columns shelf.
Step 7: From Measures, drag Change to Color.
Step 8: In Marks change to Line.
Step 9: From Dimensions, drag Padded to Path.
Step 10: From Measures, drag Rank to Label.
Step 11: From Dimensions, drag Region to Label.
Step 12: From Measures, drag Index to Detail.
Step 13: In Change, Index, X and Curve click-right and select computing using Padded.
Step 14: Edix Axis Curve, as Reversed and Axis X as -12, 12.
Step 15: Edit Label, in Marks to Label as Line Ends and in Label Appearance
Step 16: In X and Curve deselect Show Header.
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10. DIVERGENT CHART
Definition:
A divergent chart is a tool which can be used to that can: - Present two associated measures. - Compared side by side. - Visualize age demographics data. - Visualize gender distribution.
Gender Distribution in South America, 2017
Data:
Country 2012 2013 2014 2015 2016 Name Gender [YR2012] [YR2013] [YR2014] [YR2015] [YR2016] Argentina Female 21503116 21727725 21951299 22172053 22389000 Argentina Male 20592108 20810579 21028727 21244702 21458000 Bolivia Female 5109041 5190255 5271901 5353990 5437000 Bolivia Male 5129721 5209676 5289986 5370715 5452000 Brazil Female 102776771 103754885 104713195 105646790 106555000 Brazil Male 99624813 100504492 101364703 102200738 103013000 Chile Female 8812057 8906407 9000209 9093072 9185000 Chile Male 8576380 8669426 8762438 8855069 8947000 Colombia Female 23779628 24020812 24255888 24485169 24709000 Colombia Male 23101390 23321551 23535505 23743535 23946000 Ecuador Female 7707253 7829381 7951388 8073299 8195000 Ecuador Male 7712240 7831931 7951528 8071064 8190000 Peru Female 15091888 15296074 15500829 15703562 15903000 Peru Male 15066880 15269387 15472319 15673108 15871000 Uruguay Female 1758478 1763941 1769468 1775156 1781000 Uruguay Male 1638275 1644028 1650048 1656399 1663000 Venezuela, RB Female 14967959 15187536 15405120 15620882 15835000
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Venezuela, RB Male 14886279 15088509 15288707 15487201 15684000 Paraguay Female 3141711 3184123 3226980 3269997 3313000 Paraguay Male 3237451 3281546 3325538 3369126 3412000 Guyana Female 378483 379366 380401 381586 383000 Guyana Male 379927 381667 383492 385499 388000 Suriname Female 263538 266050 268511 270937 273000 Suriname Male 264997 267400 269737 272038 274000
Source: World Bank, 2017.
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Gender.xlsx
Steps to create a divergent chart
Step 1: Connect the data to Tableau. We will compare gender by countries.
Step 2: Pivot the data of Years. Change name in Pivot to Years and Population.
Step 3: Create the following calculated fields:
Name: Male IF [Gender] = 'Male' THEN [Population] END
Name: Female IF [Gender] = 'Female' THEN [Population] END
Step 4: From Measures, drag Male and Female to Columns.
Step 5: From Dimensions, drag Country name to Rows.
Step 6: Below Format, select Sort Country name descending by Female.
Step 7: Below of female shape in view, click right and select Edit Axis. Click Reversed and ok.
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Step 8: In All marks, from Dimensions, drag Gender to Color. Edit Color and choose the color by Male and Female.
Step 9: To show the name of the countries in the middle of bars, create an additional calculated field: Name: New Axis 0
Step 10: From Measures, drag New Axis to Columns, between SUM(Female) and SUM(Male).
Step 11: On Marks select SUM(New Axis) and change the Marks type from Automatic to Text.
Step 12: On Marks select SUM(New Axis), and from Dimensions drag Country Name to Text.
Step 13: On Rows, click right on pill Country Name, and deselect Show Header.
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11. BAR MAP
Definition:
This is a representation of a bar in different maps. You can show everything visually. It is very good showing for comparative information.
Gender Representation Parliament South America, 2017
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Data:
Country Male Female Bolivia 130 69 Argentina 257 100 Ecuador 137 52 Peru 130 36 Venezuela 167 37 Uruguay 99 20 Colombia 166 31 Chile 120 19 Paraguay 80 11 Brazil 513 55 Guyana 69 22 Suriname 51 13
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/BarMap.xlsx
Procedures to create a bar map
Step 1: Create Calculated Fields for the Bars
Name: Calculation Male IF [Gender]= "Male" THEN [Number] END
Name: Calculation Female IF [Gender]= "Female" THEN [Number] END
Name: % BAR MALE LEFT ("██████████", ROUND(SUM([Calculation Male])/SUM([Number])*10,0))
Name: % BAR FEMALE LEFT ("██████████", ROUND(SUM([Calculation Female])/SUM([Number])*10,0))
Step 2: Build the Map
Double click Country. This will put Longitude on Columns and Latitude on Rows.
Step 3: Change the Marks dropdown box to Text.
Step 4: Drag your new Bars fields to Text; % Female Bars, % Male Bars
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Step 5: Click Size and make the size as small as possible.
You now have 2 bars on your map representing the % of gender.
Step 6: Format the Text for the Bars
Horizontal bar chart on a map:
Click on Text and select the alignment dropdown. Set Horizontal Alignment to Left. Click on the three dots ... to edit the text box. Select all of the text and set the font to size 8. Highlight each row in the text box and change the color to the color you want the bars to be in the bar chart.
Vertical bar chart on a map:
For vertical bars we simply adjust the text alignment. Click on Text and select the alignment dropdown. Set Text Alignment to Up. Set Vertical Alignment to Bottom.
Stacked bar chart on a map:
For stacked bars, simply put all of the labels on the same line and adjust the text to normal or up for either horizontal or vertical stacked bars.
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12. WAFFLE CHART
This is a shape to display the percentage of information that is complete. This is an important tool to display KPIs, and comparative information.
Data:
Rows Columns Percentage 1 1 1% 1 2 2% 1 3 3% 1 4 4% 1 5 5% 1 6 6% 1 7 7% 1 8 8% 1 9 9% 1 10 10% 2 1 11% 2 2 12% 2 3 13% 2 4 14% 2 5 15% 2 6 16% 2 7 17%
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Rows Columns Percentage 2 8 18% 2 9 19% 2 10 20% 3 1 21% 3 2 22% 3 3 23% 3 4 24% 3 5 25% 3 6 26% 3 7 27% 3 8 28% 3 9 29% 3 10 30% 4 1 31% 4 2 32% 4 3 33% 4 4 34% 4 5 35% 4 6 36% 4 7 37% 4 8 38% 4 9 39% 4 10 40% 5 1 41% 5 2 42% 5 3 43% 5 4 44% 5 5 45% 5 6 46% 5 7 47% 5 8 48% 5 9 49% 5 10 50% 6 1 51% 6 2 52% 6 3 53% 6 4 54% 6 5 55% 6 6 56% 6 7 57% 6 8 58% 6 9 59% 6 10 60%
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Rows Columns Percentage 7 1 61% 7 2 62% 7 3 63% 7 4 64% 7 5 65% 7 6 66% 7 7 67% 7 8 68% 7 9 69% 7 10 70% 8 1 71% 8 2 72% 8 3 73% 8 4 74% 8 5 75% 8 6 76% 8 7 77% 8 8 78% 8 9 79% 8 10 80% 9 1 81% 9 2 82% 9 3 83% 9 4 84% 9 5 85% 9 6 86% 9 7 87% 9 8 88% 9 9 89% 9 10 90% 10 1 91% 10 2 92% 10 3 93% 10 4 94% 10 5 95% 10 6 96% 10 7 97% 10 8 98% 10 9 99% 10 10 100%
The data can be downloaded from:
73 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Waffle1.xlsx
Steps to create a Waffle chart:
Step 1: From Measures, drag Columns to Dimensions and Rows to Dimensions.
Step 2: From Dimensions, drag Columns to Columns and Rows to Rows.
Step 3: From Measures, drag Percentage to Text.
Step 4: In Measures select Percentage, click right and select Default Properties, Number Format, Percentage, and decimal places 0.
Step 5: Sort Rows descending by Percentage.
Step 6: Select in Data, New data Source. (In this step select the data with the information that will be presented in percentage)
Country Gender % Representatives Bolivia Female 35% Argentina Female 28% Ecuador Female 28% Peru Female 22% Venezuela Female 18% Uruguay Female 17% Colombia Female 16% Chile Female 14% Paraguay Female 12% Brazil Female 10% Guyana Female 24% Suriname Female 20% Bolivia Male 65% Argentina Male 72% Ecuador Male 72% Peru Male 78% Venezuela Male 82% Uruguay Male 83% Colombia Male 84% Chile Male 86% Paraguay Male 88% Brazil Male 90% Guyana Male 76% Suriname Male 80%
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The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Waffle2.xlsx
Step 7: In Data select Sheet 1 (Waffle Chart)
Step 8: Create a Calculated Field: Name: Box to Color SUM(([Sheet1 (Parliamentary)].[% Representatives]))>= SUM([Percentage])
Step 9: From Measures, drag Box to Color
Step 10: Drag off the pill Percentage
Step 11: Size to Max and in Color apply color to borders
Step 12: In Columns, double click and add AVG (1)
Step 13: Drag Columns for less size
Step 14: Untick Show Header from Column, Row and AVG
Step 15: In Data Select Sheet 1 (Parliamentary)
Step 16: From Dimensions, drag Country and Gender to Filters
Step 17: From Measures, drag % Representatives to Detail
Step 18: In a Square, click right and select Annotate and Mark
Step 19: Only Select
Step 20: In Measures, select % Representatives, click right and select Default Properties, Number Format, Percentage, Decimal places: 0
Step 21: In the number of %, click right and select Format. Set shading and line to none and drag the number to the center
Step 22: In data pane, click right and select Format. Format Borders and select Sheet, Column Divider and Row Divider.
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13. RINGS
Definition:
This is an important shape where you can show the comparison of data, about of different situations using rings.
Data:
Name Path Value Medicine 1 40 Architecture 1 20 Engineering 1 18 Education 1 12 Communication 1 10 Medicine 270 40 Architecture 270 20 Engineering 270 18 Education 270 12 Communication 270 10
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The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Rings.xlsx
Steps to create a Rings shape
Step 1: Create a Path Bin of Path
Step 2: Create the following Calculated Fields:
Name: Index INDEX()-1
Name: PI PI
Name: Max Value WINDOW_MAX(SUM([Value]))
Name: Value (Windows Sum) WINDOW_MAX(SUM([Value]))
Name: Step Size [Value (Windows Sum)]/ [Max Value]
Name: Rank RANK_UNIQUE([Value (Windows Sum)], 'asc')
Name: Y SIN([Index])+[PI]/180*[Step Size]*[Rank]
Name: X COS([Index]*[PI])/180*[Step Size]*[Rank]
Step 3: From Dimensions, drag Name to Color
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Step 4: From Measures, drag Value (Windows Sum) to Label
Step 5: From Dimensions, drag Name to Label
Step 6: Set the Marks Type to Line
Step 7: From Dimensions, drag Path (bin) to Path
Step 7: From Measures, drag Y to Columns
Step 8: From Measures, drag X to Rows
Step 9: Click right on Y and X, and select Table Calculation……..
For INDEX
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For PI
For Value (Windows Sum)
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For Max Value
For Rank
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In X for INDEX
For PI
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For Value(Windows Sum)
For Max Value
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For Rank
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14. RADIAL STACKED BAR1
Definition
A Radial Stacked Bar is a shape that shows the relationship of categories and subcategories. In this kind of shape you can display the bar chart as absolute values, or as a percent of each segment. This is ideal for comparing the total amount across each segment bar.
Academic Production by Type of Professor at the University, 2017
Data:
Path Position Department Order Articles Professor Accounting 5 10 Associate Professor Accounting 5 40 Assistant Professor Accounting 5 30 Professor Aerospace Engineering 5 200 Associate Professor Aerospace Engineering 5 100 Assistant Professor Aerospace Engineering 5 50 Professor Agricultural 5 600 Associate Professor Agricultural 5 670 Assistant Professor Agricultural 5 890
1 Adapted fromf Ryan Rowland. 84 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Path Position Department Order Articles Professor American Studies 5 121 Associate Professor American Studies 5 122 Assistant Professor American Studies 5 100 Professor Anthropology 5 32 Associate Professor Anthropology 5 32 Assistant Professor Anthropology 5 34 Professor Arabic Studies 5 23 Associate Professor Arabic Studies 5 22 Assistant Professor Arabic Studies 5 3 Professor Architecture 5 345 Associate Professor Architecture 5 434 Assistant Professor Architecture 5 345 Professor Art 5 3 Associate Professor Art 5 56 Assistant Professor Art 5 76 Professor Astronomy 5 456 Associate Professor Astronomy 5 456 Assistant Professor Astronomy 5 323 Professor Biochemistry 5 342 Associate Professor Biochemistry 5 342 Assistant Professor Biochemistry 5 321 Professor Bioengineering 5 324 Associate Professor Bioengineering 5 322 Assistant Professor Bioengineering 5 234 Professor Biological Sciences 5 123 Associate Professor Biological Sciences 5 123 Assistant Professor Biological Sciences 5 234 Professor Biophycis 5 212 Associate Professor Biophycis 5 213 Assistant Professor Biophycis 5 214 Professor Business 5 456 Associate Professor Business 5 657 Assistant Professor Business 5 657 Professor Chemical 5 345 Associate Professor Chemical 5 343 Assistant Professor Chemical 5 323 Professor Civil Engineering 5 800 Associate Professor Civil Engineering 5 900 Assistant Professor Civil Engineering 5 980 Professor Environmental Engineering 5 980 Associate Professor Environmental Engineering 5 990 Assistant Professor Environmental Engineering 5 980 Professor Classics 5 23 85 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Path Position Department Order Articles Associate Professor Classics 5 45 Assistant Professor Classics 5 67 Professor Communication 5 900 Associate Professor Communication 5 900 Assistant Professor Communication 5 800 Professor Comparative Literature 5 34 Associate Professor Comparative Literature 5 56 Assistant Professor Comparative Literature 5 78 Professor Computer Science 5 678 Associate Professor Computer Science 5 678 Assistant Professor Computer Science 5 656 Professor Economics 5 456 Associate Professor Economics 5 456 Assistant Professor Economics 5 657 Professor Education 5 456 Associate Professor Education 5 345 Assistant Professor Education 5 234 Professor Higher Education 5 567 Associate Professor Higher Education 5 567 Assistant Professor Higher Education 5 656 Professor Electrical Engineering 5 456 Associate Professor Electrical Engineering 5 345 Assistant Professor Electrical Engineering 5 345 Professor Computer Engineering 5 234 Associate Professor Computer Engineering 5 345 Assistant Professor Computer Engineering 5 345 Professor English Language 5 23 Associate Professor English Language 5 24 Assistant Professor English Language 5 56 Professor Literature 5 45 Associate Professor Literature 5 67 Assistant Professor Literature 5 66 Professor Entomology 5 678 Associate Professor Entomology 5 666 Assistant Professor Entomology 5 699 Epidemiology and Professor Biostatistics 5 567 Epidemiology and Associate Professor Biostatistics 5 890 Epidemiology and Assistant Professor Biostatistics 5 900 Professor Family Science 5 789 Associate Professor Family Science 5 890
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Path Position Department Order Articles Assistant Professor Family Science 5 900 Professor Finance 5 78 Associate Professor Finance 5 67 Assistant Professor Finance 5 98 Professor Geography 5 67 Associate Professor Geography 5 89 Assistant Professor Geography 5 89 Professor Geology 5 345 Associate Professor Geology 5 345 Assistant Professor Geology 5 323 Professor Government and Politics 5 345 Associate Professor Government and Politics 5 456 Assistant Professor Government and Politics 5 789 Professor Health Services 5 678 Associate Professor Health Services 5 999 Assistant Professor Health Services 5 900 Professor History 5 567 Associate Professor History 5 567 Assistant Professor History 5 879 Professor Human Development 5 657 Associate Professor Human Development 5 657 Assistant Professor Human Development 5 879 Professor Journalism 5 567 Associate Professor Journalism 5 567 Assistant Professor Journalism 5 456 Professor Linguistics 5 34 Associate Professor Linguistics 5 56 Assistant Professor Linguistics 5 78 Professor Logistics 5 65 Associate Professor Logistics 5 78 Assistant Professor Logistics 5 90 Management and Professor Organization 5 990 Management and Associate Professor Organization 5 990 Management and Assistant Professor Organization 5 989 Professor Marketing 5 890 Associate Professor Marketing 5 980 Assistant Professor Marketing 5 980 Professor Philoshophy 5 76 Associate Professor Philoshophy 5 87 Assistant Professor Philoshophy 5 89
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Path Position Department Order Articles Professor Psychology 5 789 Associate Professor Psychology 5 890 Assistant Professor Psychology 5 999 Professor Statistics 5 555 Associate Professor Statistics 5 567 Assistant Professor Statistics 5 657 Professor Teaching and Learning 5 656 Associate Professor Teaching and Learning 5 566 Assistant Professor Teaching and Learning 5 677 Professor Urban Studies 5 565 Associate Professor Urban Studies 5 555 Assistant Professor Urban Studies 5 555 Professor Women´s Studies 5 121 Associate Professor Women´s Studies 5 123 Assistant Professor Women´s Studies 5 123
The data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/RadialStacked.xlsx
Steps to create a Radial Stacked Bar
Step 1: Create the following Parameters.
Name: bar_method
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Name: bar_spacing
Name: r_inner
Name: r_outer
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Step 2: Create the following Calculated Fields:
Name: radial_segment [Department]
Name: radial_partition [Position]
Name: index_pathorder index()
Step 3: Edit PathOrder (bin), select PathOrder on Measures, click right and select Create, after select Bins.
Step 4: Create the following Calculated Fields: index_pathorder index() index_segment index() value_field if [PathOrder] = 1 then 0 else [Articles] end value_partition window_sum(sum([value_field])) value_segment WINDOW_SUM(sum([value_field])) value_segment_max WINDOW_MAX([value_segment]) value_limit case [bar_method] when true then [value_segment_max] else [value_segment] end 90 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI partition_size IF([index_pathorder]=2 or [index_pathorder]=3) THEN RUNNING_SUM([value_partition])/[value_limit] else (RUNNING_SUM([value_partition])-[value_partition]) / [value_limit] end radial_length [r_inner] + ([partition_size]* ([r_outer]-[r_inner])) radial_angle
- IF([index_pathorder]=3 or [index_pathorder]=4) THEN [index_segment] +1 - [bar_spacing] else [index_segment] + [bar_spacing] end * (1/window_max([index_segment])) * 2 * 3.14159265359 + (3.14159265359/2) plot_x [radial_length] * COS([radial_angle]) plot_y [radial_length] * SIN([radial_angle])
Step 5: From Measures, drag plot_x to Columns.
Step 6: From Measures, drag plot_y to Rows.
Step 7: From Dimensions, drag radial_partition to Color.
Step 8: From Dimensions, drag radial_segment to Detail.
Step 9: On Marks change Automatic to Polygon.
Step 10: From Dimensions, drag PathOrder (bin) to Path.
Step 11: From Measures, drag value_partition to Tooltip. On value_partition on Marks, click right and select Compute using PathOrder (bin).
Step 12: From Measures, drag value_segment to Tooltip. On value_segment on Marks, click right and select Table Calculation.
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Step 13: Select plot_x on Columns and select Table Calculation. Apply the following settings to each calculation.
92 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI partition_size
index_pathorder
93 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI value_partition
value_segment_max
94 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI value_segment
radial_angle
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index_segment
Step 14: Select plot_y on Rows and select Table Calculation. Apply the following settings to each calculation. partition_size
96 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI index_pathorder
value_partition
97 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI value_segment_max
value_segment
98 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI radial_angle
index_segment
Step 15: Edit Color, size, etc.
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15. POLYGON MAP
Definition:
Tableau has many great built-in geographic areas available for mapping data of different countries, regions, etc. If you have a special geographic area and this is not available, you can draw maps of any area type using custom shapes with Tableau.
To use a polygon map you need to add the polygon ID and grouping ID to details and the sequenced point ID to path in order to display your own custom group map. It is important to have the Latitude and Longitude to build this kind of map.
National Parks of England
Data:
Number of Latitude Longitude Records Park Name Point ID Polygon ID 51.860969 -2.952126 1 Brecon Beacons National Park 1 12 51.844296 -2.961753 1 Brecon Beacons National Park 2 12 51.840803 -2.980047 1 Brecon Beacons National Park 3 12 51.834348 -2.98402 1 Brecon Beacons National Park 4 12 51.833385 -2.988128 1 Brecon Beacons National Park 5 12
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Number of Latitude Longitude Records Park Name Point ID Polygon ID 51.842197 -3.000822 1 Brecon Beacons National Park 6 12 51.842494 -3.01297 1 Brecon Beacons National Park 7 12 51.837835 -3.01649 1 Brecon Beacons National Park 8 12 51.833499 -3.026243 1 Brecon Beacons National Park 9 12 51.833958 -3.041385 1 Brecon Beacons National Park 10 12 51.829612 -3.045979 1 Brecon Beacons National Park 11 12 51.822979 -3.03589 1 Brecon Beacons National Park 12 12 51.814602 -3.042365 1 Brecon Beacons National Park 13 12 51.800782 -3.026342 1 Brecon Beacons National Park 14 12 51.786901 -3.017214 1 Brecon Beacons National Park 15 12 51.779679 -3.014982 1 Brecon Beacons National Park 16 12 51.776479 -3.020081 1 Brecon Beacons National Park 17 12 51.774664 -3.009948 1 Brecon Beacons National Park 18 12 51.766726 -3.002402 1 Brecon Beacons National Park 19 12 51.760509 -3.003192 1 Brecon Beacons National Park 20 12 51.760973 -2.995889 1 Brecon Beacons National Park 21 12 51.756526 -2.997975 1 Brecon Beacons National Park 22 12 51.742026 -2.99188 1 Brecon Beacons National Park 23 12 51.737429 -2.99307 1 Brecon Beacons National Park 24 12 51.733759 -2.988795 1 Brecon Beacons National Park 25 12 51.729046 -2.99848 1 Brecon Beacons National Park 26 12 51.732023 -3.003079 1 Brecon Beacons National Park 27 12 51.731849 -3.008231 1 Brecon Beacons National Park 28 12
The complete data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/07/Parks-in-England.xls
Steps to create a Polygon Map:
Step 1: In Measures, change Latitude to geographical role of Latitude and change Longitude to geographical role of Longitude.
Step 2: From Measures, drag Latitude to Rows.
Step 3: From Measures, drag Longitude to Columns.
Step 4: On the right select Symbol map on Show me.
Step 5: On Marks Card, change from Automatic to Polygon.
Step 6: From Dimensions, drag Point Id to Path.
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Step 7: From Dimensions, drag Polygon Id to Details.
Step 8: From Dimensions, drag Park Name to Color.
DISTRICTS OF PERU
50 Districts with high Poverty in Peru, 2013
Source: INEI, 2015.
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Data:
ID Pro ID vincia Distrito Region Provincia Distrito Path Shapeid Longitud Latitud 1 1 Amazonas Bagua Aramango 305 0 -78.6107712 -5.27538919 1 1 Amazonas Bagua Aramango 306 0 -78.610611 -5.27105618 1 1 Amazonas Bagua Aramango 307 0 -78.6096115 -5.26666403 1 1 Amazonas Bagua Aramango 308 0 -78.6095505 -5.26325178 1 1 Amazonas Bagua Aramango 309 0 -78.6091385 -5.2603488 1 1 Amazonas Bagua Aramango 304 0 -78.6086426 -5.27589703 1 1 Amazonas Bagua Aramango 310 0 -78.6078339 -5.25528908 1 1 Amazonas Bagua Aramango 303 0 -78.6075592 -5.27637291 1 1 Amazonas Bagua Aramango 311 0 -78.6074371 -5.2520051 1 1 Amazonas Bagua Aramango 312 0 -78.6054306 -5.24678087 1 1 Amazonas Bagua Aramango 302 0 -78.6053696 -5.27787495 1 1 Amazonas Bagua Aramango 301 0 -78.6037521 -5.2794838 1 1 Amazonas Bagua Aramango 313 0 -78.6032639 -5.24359703 1 1 Amazonas Bagua Aramango 300 0 -78.6008377 -5.28381681 1 1 Amazonas Bagua Aramango 314 0 -78.5992966 -5.23914099 1 1 Amazonas Bagua Aramango 299 0 -78.5985184 -5.28869677 1 1 Amazonas Bagua Aramango 315 0 -78.5980454 -5.23678207 1 1 Amazonas Bagua Aramango 316 0 -78.5974121 -5.23600102 1 1 Amazonas Bagua Aramango 317 0 -78.596405 -5.23536682 1 1 Amazonas Bagua Aramango 298 0 -78.5957184 -5.29293823 1 1 Amazonas Bagua Aramango 318 0 -78.5945892 -5.23518705 1 1 Amazonas Bagua Aramango 297 0 -78.5934753 -5.29715014 1 1 Amazonas Bagua Aramango 319 0 -78.59301 -5.23406792 1 1 Amazonas Bagua Aramango 296 0 -78.5922089 -5.29871798 1 1 Amazonas Bagua Aramango 320 0 -78.5908051 -5.22972822 1 1 Amazonas Bagua Aramango 321 0 -78.5893707 -5.22819185 1 1 Amazonas Bagua Aramango 295 0 -78.5878143 -5.30323982 1 1 Amazonas Bagua Aramango 322 0 -78.5876389 -5.22808504 1 1 Amazonas Bagua Aramango 294 0 -78.5868301 -5.30468178 1 1 Amazonas Bagua Aramango 293 0 -78.5857163 -5.30698204 1 1 Amazonas Bagua Aramango 323 0 -78.5851212 -5.22957087 1 1 Amazonas Bagua Aramango 292 0 -78.5843048 -5.3084178 1 1 Amazonas Bagua Aramango 291 0 -78.5827713 -5.30940008 1 1 Amazonas Bagua Aramango 324 0 -78.5827408 -5.23184109 1 1 Amazonas Bagua Aramango 325 0 -78.5818481 -5.2335372 1 1 Amazonas Bagua Aramango 290 0 -78.5811386 -5.31099987
The complete data can be downloaded from the USB with this manual. Name of file: Distritos Peru 2017.
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Steps to create a Polygon Map of Districts of Peru:
Step 1: In Measures, change Latitude to geographical role of Latitude and change Longitude to geographical role of Longitude.
Step 2: From Measures, drag Latitude to Rows.
Step 3: From Measures, drag Longitude to Columns.
Step 4: On the right select Symbol map on Show me.
Step 5: On Marks Card, change from Automatic to Polygon.
Step 6: From Measures, drag Path to Dimensions on Data panel.
Step 7: From Measures, drag Shapeid to Dimensions on Data panel.
Step 8: From Dimensions, drag Path to Path.
Step 9: From Dimensions, drag Shapeid to Details.
Step 10: From Dimensions, drag Distrito to Details.
Step 11: On Data, select New Data Source.
Step 12: Connect the file Distritos Listos para Trabajar 2017. This file can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/07/Lista- de-Distritos-para-Trabajar-2017.xlsx
Step 13: On Data, select Edit Relationships. After select Custom. Select Add and Distrito and Distrito.
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Step 14: From Dimensions, drag % Poverty inf. to Color
Step 15: Edit Color.
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16. BUBBLE MAP
Definition:
A Bubble Map is also called as a cartogram where X and Y values are effectively latitude and longitude coordinates representing a geographic location. The size of the bubble represents a dimension in which you can see the difference of values.
Poverty in the districts of Peru 2013
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DATA:
UBIGEO REGION PROVINCIA DISTRITO LONGITUDE LATITUDE 080914 CUSCO LA CONVENCION MEGANTONI -72.85891839680 -11.75113205110 050511 AYACUCHO LA MAR ORONCCOY -73.39260962730 -13.34632732810 100609 HUANUCO LEONCIO PRADO PUEBLO NUEVO -76.00136850600 -9.08219785100 SANTIAGO DE 090723 HUANCAVELICA TAYACAJA TUCUMA -74.88695871460 -12.31756644470 080601 CUSCO CANCHIS SICUANI -71.10360856780 -14.25101722550 021601 ANCASH POMABAMBA POMABAMBA -77.50652631100 -8.79036933173 120132 JUNIN HUANCAYO SAÐO -75.24688436900 -11.95061794300 220509 SAN MARTIN LAMAS SHANAO -76.57787779750 -6.40809472213 120401 JUNIN JAUJA JAUJA -75.49113778860 -11.77408558210 081003 CUSCO PARURO CCAPI -72.01942537000 -13.86068101050 211202 PUNO SANDIA CUYOCUYO -69.55766801260 -14.52381411750 080708 CUSCO CHUMBIVILCAS VELILLE -71.84916363230 -14.54900438300 080603 CUSCO CANCHIS COMBAPATA -71.33414332930 -14.08814277380 SAN ANTONIO DE 090310 HUANCAVELICA ANGARAES ANTAPARCO -74.43066764800 -13.06552118610 030611 APURIMAC CHINCHEROS LOS CHANKAS -73.79139393500 -13.39015278580 LA YARADA LOS 230111 TACNA TACNA PALOS -70.43903952990 -18.20042568870 CASTILLO 100608 HUANUCO LEONCIO PRADO GRANDE -76.03797690950 -9.20807808980 SAN PABLO DE 100113 HUANUCO HUANUCO PILLAO -75.93844952930 -9.70775509298 140303 LAMBAYEQUE LAMBAYEQUE ILLIMO -79.85336718840 -6.46959393046 050412 AYACUCHO HUANTA CHACA -74.19154679020 -12.78355805830 090721 HUANCAVELICA TAYACAJA ROBLE -74.45786236890 -12.23602889300 030609 APURIMAC CHINCHEROS ROCCHACC -73.61722684630 -13.45556625850 120201 JUNIN CONCEPCION CONCEPCION -75.31351516210 -11.91246163990 TINGO DE 220709 SAN MARTIN PICOTA PONASA -76.21211615710 -6.97034231205 210502 PUNO EL COLLAO CAPAZO -69.70201677170 -17.10984949450 030610 APURIMAC CHINCHEROS EL PORVENIR -73.55513974520 -13.39771964050 080403 CUSCO CALCA LAMAY -71.88108823920 -13.32206092520 CORONEL 250105 UCAYALI PORTILLO YARINACOCHA -74.65669894630 -8.24849719008 100104 HUANUCO HUANUCO CHURUBAMBA -76.26573305080 -9.68525551846 030602 APURIMAC CHINCHEROS ANCO_HUALLO -73.66927131380 -13.54429951600 140119 LAMBAYEQUE CHICLAYO PUCALA -79.51372121560 -6.79463043191 090302 HUANCAVELICA ANGARAES ANCHONGA -74.70603847080 -12.88732407000 050407 AYACUCHO HUANTA SIVIA -73.99644831790 -12.60382286630 211103 PUNO SAN ROMAN CABANILLAS -70.62245736020 -15.85336848990 100103 HUANUCO HUANUCO CHINCHAO -76.11071465710 -9.61537834385 SANTA MARIA DE 030215 APURIMAC ANDAHUAYLAS CHICMO -73.54626986790 -13.66115711730 050403 AYACUCHO HUANTA HUAMANGUILLA -74.15894644920 -12.99265488410
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UBIGEO REGION PROVINCIA DISTRITO LONGITUDE LATITUDE 060111 CAJAMARCA CAJAMARCA NAMORA -78.28407584660 -7.18436407264 090705 HUANCAVELICA TAYACAJA COLCABAMBA -74.70044588510 -12.38509631690 090119 HUANCAVELICA HUANCAVELICA HUANDO -75.01360791020 -12.62728459290 080205 CUSCO ACOMAYO POMACANCHI -71.62389796800 -14.05363713590 220912 SAN MARTIN SAN MARTIN SAN ANTONIO -76.38208966240 -6.39934333867 120402 JUNIN JAUJA ACOLLA -75.56756529840 -11.66266253430 120902 JUNIN CHUPACA AHUAC -75.35155460830 -12.08199942490 080408 CUSCO CALCA YANATILE -72.10508603500 -12.79569713170 090502 HUANCAVELICA CHURCAMPA ANCO -74.56305371900 -12.64353334730 140302 LAMBAYEQUE LAMBAYEQUE CHOCHOPE -79.61358993100 -6.15523258480 211203 PUNO SANDIA LIMBANI -69.60744228130 -13.77844864310 021003 ANCASH HUARI CAJAY -77.13140973350 -9.25703065067 010510 AMAZONAS LUYA LUYA VIEJO -78.11680994070 -6.11249523254 030218 APURIMAC ANDAHUAYLAS TURPO -73.47220652330 -13.79059514300 081205 CUSCO QUISPICANCHI CCATCA -71.50577025840 -13.60038809860 LEONOR 120413 JUNIN JAUJA ORDOÐEZ -75.43138729660 -11.86913306650 220105 SAN MARTIN MOYOBAMBA SORITOR -77.05414251640 -6.27274272661 100105 HUANUCO HUANUCO MARGOS -76.53319332780 -10.06324028680 010706 AMAZONAS UTCUBAMBA LONYA GRANDE -78.41068051110 -6.06348043759 SAN ANTONIO DE 211001 PUNO PUTINA PUTINA -69.83448632370 -14.70916613040 CORONEL 250107 UCAYALI PORTILLO MANANTAY -74.50916082430 -8.53844581264 050114 AYACUCHO HUAMANGA VINCHOS -74.45646620900 -13.30628293970 050112 AYACUCHO HUAMANGA SOCOS -74.28442711310 -13.26145680390 051101 AYACUCHO VILCAS HUAMAN VILCAS HUAMAN -73.89632122070 -13.65570147430 080401 CUSCO CALCA CALCA -71.95777704860 -13.26872231420 010522 AMAZONAS LUYA TINGO -77.94352618720 -6.38686436807 120501 JUNIN JUNIN JUNIN -76.01192355670 -11.18881438240
The complete data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Distritos-Peru-2017.xlsx
Steps to create a Bubble Map:
Step 1: In Measures, change Latitude to geographical role of Latitude and change Longitude to geographical role of Longitude.
Step 2: From Measures, drag Latitude to Rows.
Step 3: From Measures, drag Longitude to Columns.
Step 4: On the right select Symbol map on Show me.
Step 5: In Dimensions, select Distrito and change to Geographical role of County. 108 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Step 6: From Dimensions, drag Distrito to Details.
Step 7: In Dimensions, select Pobreza, right click and select Change Data Type and select Number (decimal)
Step 8: From Dimensions, drag Pobreza to Color. Right click and select Dimension, Continuous.
Step 9: Edit color, size of the bubble.
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17. TIMELINE MAP
Definition:
This kind of map is about chronological events or information about any theme, which is show in a map of the World, region or country.
Internet users per 100 People 1990-2015
Source: World Bank, 2017.
Data:
Country 2010 2011 2012 2013 2014 2015 Country Name Code [YR2010] [YR2011] [YR2012] [YR2013] [YR2014] [YR2015] Aruba ABW 62 69 74 78.9 83.78 88.66123 Afghanistan AFG 4 5 5.454545 5.9 7 8.26 Angola AGO 2.8 3.1 6.5 8.9 10.2 12.4 Albania ALB 45 49 54.65596 57.2 60.1 63.25293 Andorra AND 81 81 86.43442 94 95.9 96.91 Arab World ARB 24.53578 26.54999 29.9543 32.3499 36.00713 39.94832 United Arab Emirates ARE 68 78 84.99999 88 90.4 91.24341 Argentina ARG 45 51 55.8 59.9 64.7 69.40092 Armenia ARM 25 32 37.5 41.9 54.62281 58.24933 Antigua and Barbuda ATG 47 52 58 63.4 64 65.2 Australia AUS 76 79.4877 79 83.4535 84 84.56052 Austria AUT 75.17 78.73999 80.02999 80.6188 81 83.9263 Azerbaijan AZE 46 50 54.2 73 75.00002 77 Burundi BDI 1 1.11 1.22 1.264218 1.38 4.866224 110 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI
Country 2010 2011 2012 2013 2014 2015 Country Name Code [YR2010] [YR2011] [YR2012] [YR2013] [YR2014] [YR2015] Belgium BEL 75 81.61 80.71999 82.1702 85 85.0529 Benin BEN 3.13 4.148323 4.5 4.9 6 6.787703 Burkina Faso BFA 2.4 3 3.725035 9.1 9.4 11.38765 Bangladesh BGD 3.7 4.5 5 6.63 13.9 14.4 Bulgaria BGR 46.23 47.97999 51.89999 53.0615 55.49 56.6563 Bahrain BHR 55 76.99997 88 90.00004 90.50313 93.4783 Bahamas, The BHS 43 65 71.7482 72 76.92 78
The complete data can be downloaded from: http://blog.pucp.edu.pe/blog/raulchoque/wp- content/uploads/sites/905/2017/08/Internet.xlsx
Steps to create a Timeline Map:
Step 1: Connect the Excel sheet to Tableau.
Step 2: Pivot the data. In Data Source, select all columns with Internet users per 100 people using Ctrl.
Step 3: Once all columns with Internet users per 100 people have been selected, click the drop-down arrow next to the columns name, and then select Pivot. New columns replace the original columns that we selected to create the pivot.
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Step 4: In Data Source change the name of Pivot Fields Name to Years and Pivot Field Values to Internet per 100 People.
Step 5: In Dimensions, select Country name and change to geographical role to Country/Region.
Step 6: From Dimensions, drag Country name to the Data Pane and after select on right in Show Me Filled maps.
Step 7: In Dimensions, select Internet per 100 People, right-click and select Change Data Type to Number (decimal).
Step 8: From Dimensions, drag Country name to Details.
Step 9: From Dimensions, drag Internet per 100 People to Color, right-click and select Continuous.
Step 10: From Dimensions, drag Years to Pages.
Step 11: On the right side, click in Show history.
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VII. REFERENCES
Acharya, Seema and Chellappan, Subhashini. PRO TABLEAU. India. Apress 2017.
Dabney, Alan and Klein, Grady. THE CARTOON INTRODUCTION TO STATISTICS. FSGBOOKS.COM. USA, 2013.
Harris, Robert. INFORMATION GRAPHICS. A COMPREHENSIVE ILLUSTRATED REFERENCE. Oxford University Press. USA, 1999.
Institute for Health Metrics and Evaluation. University of Washington. FINANCING GLOBAL HEALTH 2016. USA, 2017.
Johnson, Steven. THE GHOST MAP. Riverhead Books. USA, 2007.
Li Carrillo, Víctor. LA “GESTALPYCHOLOGIE” Y EL CONCEPTO DE ESTRUCTURA. Revista Venezolana de Filosofía. Número 8. Caracas 1978.
Tufte, Edward. THE VISUAL DISPLAY OF QUANTITATIVE INFORMATION. Second Edition. Graphics Press LLC. USA, 2007.
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DATA VISUALIZATION WITH TABLEAU
Data visualization is a science, where the objective is to communicate information, using graphics, infographics and shapes about any topic or area. Nowadays, we have a lot of information, especially on the Internet, so it is necessary to systematize and organize the information to share with everyone.
Population of the World, 2016
Source: World Bank, 2017.
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