DATA VISUALIZATION WITH TABLEAU | Blog PUCP

DATA VISUALIZATION

WITH TABLEAU

RAUL CHOQUE LARRAURI

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

1 DATA VISUALIZATION WITH TABLEAU RAUL CHOQUE LARRAURI


DATA

VISUALIZATION

WITH TABLEAU



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#!/

mailto:[email protected]

https://public.tableau.com/profile/raul.choque#!/


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 [email protected] 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.

mailto:[email protected]


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


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.


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.

https://www.digitalstudies.org/ojs/index.php/digital_studies/article/viewFile/305/439/2592


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

https://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak#/media/File:Snow-cholera-map-1.jpg

https://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak#/media/File:Snow-cholera-map-1.jpg


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.

https://en.wikipedia.org/wiki/Charles_Joseph_Minard#/media/File:Minard.png


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

http://www.sci.utah.edu/~kpotter/Library/Papers/wilkinson:2009:HCHM/wilkinson_2009_HCHM_01.png

http://www.sci.utah.edu/~kpotter/Library/Papers/wilkinson:2009:HCHM/wilkinson_2009_HCHM_01.png


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

https://www.pinterest.com/pin/112027109452289534


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.

https://www.flickr.com/photos/accurat/8249052633/in/set-72157632185046466


IV. SOURCE DATA:

Source Information Link

The World Bank - World Development Indicators - Statistical Capacity Indicators - Education Statistics – All Indicators - Health Nutrition and Population Statistics

http://databank.worldbank.org/data/home.aspx

Inter Parliamentary Union

- Women in National Parliaments - Statistical data from 1997

http://www.ipu.org/wmn-e/classif.htm

Scimago Journal & Country Rank

- Journal Rankings - Country Rankings

http://www.scimagojr.com/countryrank.php

Institute of International Education (Open Doors Data Portal)

- International students and scholars in the United States

- American students studying abroad for academic credit

https://www.iie.org/Research-and-Insights/Open-Doors/Data/

Institute for Health Metrics and Evaluation

- Policy reports of Health - Country Profiles of Health

http://www.healthdata.org/policy-report/financing-global-health-2016-development-assistance-public-and-private-health-spending

Worldmeters - World Population - Government & Economics - Society & Media - Environment, Food, Water, Energy and Health

http://www.worldometers.info/

Data OKFN - Government - Social - Economics

http://data.okfn.org/data/core/population

OECD Organization for Economic Cooperation and Development

- Social - Economic - Demographic

http://stats.oecd.org/

Global Health Observatory

- Health - Profile in Health of Countries

http://www.who.int/gho/database/en/

http://databank.worldbank.org/data/home.aspx

http://www.ipu.org/wmn-e/classif.htm

http://www.scimagojr.com/countryrank.php






http://www.worldometers.info/

http://data.okfn.org/data/core/population

http://stats.oecd.org/

http://www.who.int/gho/database/en/


Source Information Link

The International Organization of Motor Vehicle Manufactures

- World Motor Vehicle Production - Information by country and type

http://www.oica.net/category/production-statistics/

Centers for Disease Control and Prevention

- Health

https://www.cdc.gov/datastatistics/index.html

Inter-American Development Bank

- Economics - Development

https://data.iadb.org/

United Nations Data - Data in different areas http://data.un.org/

United Nations Population Division

- Population - Age composition of the population

https://esa.un.org/unpd/wpp/

NCD Risk Factor Collaboration (NCD-RisC)

- Non-communicable diseases http://www.ncdrisc.org/

United Nations Development Program

- Human Development Data http://hdr.undp.org/en/data

INEI PERU - Population http://www.inei.gob.pe/biblioteca-virtual/publicaciones-digitales/

Canadian Institute for Health Information

- Health https://www.cihi.ca/en

Public Health Informatics Institute

- Health http://www.phii.org/

Data UNESCO - Education http://data.uis.unesco.org/

FAO DATA - Food and Agriculture http://www.fao.org/faostat/en/#home

http://www.oica.net/category/production-statistics/

https://www.cdc.gov/datastatistics/index.html

https://data.iadb.org/

http://data.un.org/

https://esa.un.org/unpd/wpp/

http://www.ncdrisc.org/

http://hdr.undp.org/en/data

http://www.inei.gob.pe/biblioteca-virtual/publicaciones-digitales/

http://www.inei.gob.pe/biblioteca-virtual/publicaciones-digitales/

https://www.cihi.ca/en

http://www.phii.org/

http://data.uis.unesco.org/

http://www.fao.org/faostat/en/#home


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.


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


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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/PieMap.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/PieMap.xlsx


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.


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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/06/Birthdays-USA-1973-1999.xls

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/06/Birthdays-USA-1973-1999.xls


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.


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


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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Donut.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Donut.xlsx


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.


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:

Faculty Career Condition Year of Process

Number of People

Architecture Architecture Applicant 2009-I 406

Architecture Architecture Applicant 2009-II 344














Number of People



Science Computer's Science Applicant 2009-I 0

Science Computer's Science Applicant 2009-II 0














Science Physics Applicant 2009-I 37

Science Physics Applicant 2009-II 33














Environmental Engineering

Environmental Engineering Applicant 2009-I 0


Environmental Engineering Applicant 2009-II 0











Number of People



















Civil Engineering Civil Engineering Applicant 2009-I 1280

Civil Engineering Civil Engineering Applicant 2009-II 971















Industrial Security Engineering Applicant 2009-I 63


Industrial Security Engineering Applicant 2009-II 59


Industrial Security 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

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/06/Statistics-UNI.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/06/Statistics-UNI.xlsx


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.


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.


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.


We need the database with the following information.

Category Path Order

Product ID

Row ID

Value

Professional 0 1 1 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


Category Path Order

Product ID

Row ID

Value

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


















Technical 1 18 18 68












Auxiliary 1 30 30 234



Category Path Order

Product ID

Row ID

Value
















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()

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/RadialBar.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/RadialBar.xlsx


Step 2: Create the following Parameters: Name: RADIAL_INNER

Name: RADIAL_OUTER


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.


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


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)


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 Country 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

8900453 9006263 9070867 9154302 9269612

United Arab

Emirates Middle East

42096739 42539925 42981515 43417765 43847430 Argentina The Americas

2881922 2893509 2906220 2916950 2924816 Armenia Asia


Year 2012 Year 2013 Year 2014 Year 2015 Year 2016 Country Region

55230 55307 55437 55537 55599

American

Samoa Oceania

96777 97824 98875 99923 100963

Antigua and

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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/2016.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/2016.xlsx


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:


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.


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




Professor Department of Economics











Associate Professor Department of Marketing


Position Department



Assistant Professor Department of Marketing












Professor Department of Marketing

Professor Department of Marketing




Associate Professor Department of Tourism

Assistant Professor Department of Tourism










Assistant Professor Department of Journalism

Assistant Professor Department of Journalism

Associate Professor Department of Journalism




Professor Department of Journalism








Position Department





Professor Department of Nutrition


Assistant Professor Department of Nutrition








Associate Professor Department of Nutrition





Professor Department of Nursing

Associate Professor Department of Nursing




Assistant Professor Department of Nursing














Assistant Professor Department of Statistics




Associate Professor Department of Statistics


Position Department








Professor Department of Statistics








Assistant Professor Department of Chemistry

Associate Professor Department of Chemistry




Professor Department of Chemistry













Associate Professor Department of Entomology

Assistant Professor Department of Entomology










Position Department




Professor Department of Entomology

Professor Department of Entomology




Associate Professor Department of History

Assistant Professor Department of History













Associate Professor Department of English



Professor Department of English













Assistant Professor Department of English




Professor Department of Physiology


Position Department




Associate Professor Department of Physiology



Assistant Professor Department of Physiology
























Assistant Professor Department of Computation

Associate Professor Department of Computation








Professor Department of Computation





Position Department





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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/UnitChart.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/UnitChart.xlsx


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 Name Destination University Name

Year 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



Year 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



Year 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



Year 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



Year 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



Year 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

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Sankey.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Sankey.xlsx


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:


Rank 1


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.


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.


Step 19: Relation in a Dashboard of segment reference and Sankey. Click on Dashboard and select Actions. In Add Action select Highlight.


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

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/06/Data-Book-Sankey.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/06/Data-Book-Sankey.xlsx


The steps should be the same as that in the simple kind. Path Size using Padded.


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


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



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)

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Sankey2.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Sankey2.xlsx


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 <SUM(Rank)>.<Region> Step 16: In X and Curve deselect Show Header.



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 Name Gender

2012 [YR2012]

2013 [YR2013]

2014 [YR2014]

2015 [YR2015]

2016 [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


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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Gender.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Gender.xlsx


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.


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


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


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

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/BarMap.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/BarMap.xlsx


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.


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%



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%



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%



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%

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Waffle1.xlsx



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 <Hoja1 (Parliamentary).SUM(% Representatives)>. This will be very big. 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.




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


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

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Rings.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Rings.xlsx


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


For PI

For Value (Windows Sum)


For Max Value

For Rank


In X for INDEX

For PI


For Value(Windows Sum)

For Max Value


For Rank


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:

Position Department Path 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.



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



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

Professor Epidemiology and Biostatistics 5 567

Associate Professor Epidemiology and Biostatistics 5 890

Assistant Professor Epidemiology and Biostatistics 5 900

Professor Family Science 5 789

Associate Professor Family Science 5 890



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

Professor Management and Organization 5 990

Associate Professor Management and Organization 5 990

Assistant Professor Management and 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



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

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/RadialStacked.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/RadialStacked.xlsx


Name: bar_spacing

Name: r_inner

Name: r_outer


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


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.


Step 13: Select plot_x on Columns and select Table Calculation. Apply the following settings to each calculation.


partition_size

index_pathorder


value_partition

value_segment_max


value_segment

radial_angle


index_segment

Step 14: Select plot_y on Rows and select Table Calculation. Apply the following settings to each calculation. partition_size


index_pathorder

value_partition


value_segment_max

value_segment


radial_angle

index_segment

Step 15: Edit Color, size, etc.


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:

Latitude Longitude

Number

of Records Park Name Point ID Polygon ID

51.860969 -2.952126 1 Brecon Beacons National Park 1 12






Latitude Longitude

Number of

Records Park Name Point ID Polygon ID
























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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/07/Parks-in-England.xls

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/07/Parks-in-England.xls


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.


Data: ID Pro vincia

ID Distrito Region Provincia Distrito Path Shapeid Longitud Latitud

1 1 Amazonas Bagua Aramango 305 0 -78.6107712 -5.27538919




































The complete data can be downloaded from the USB with this manual. Name of file: Distritos Peru 2017.


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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/07/Lista-de-Distritos-para-Trabajar-2017.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/07/Lista-de-Distritos-para-Trabajar-2017.xlsx


Step 14: From Dimensions, drag % Poverty inf. to Color Step 15: Edit Color.


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


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

090723 HUANCAVELICA TAYACAJA SANTIAGO DE 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

090310 HUANCAVELICA ANGARAES SAN ANTONIO DE ANTAPARCO -74.43066764800 -13.06552118610

030611 APURIMAC CHINCHEROS LOS CHANKAS -73.79139393500 -13.39015278580

230111 TACNA TACNA LA YARADA LOS PALOS -70.43903952990 -18.20042568870

100608 HUANUCO LEONCIO PRADO CASTILLO GRANDE -76.03797690950 -9.20807808980

100113 HUANUCO HUANUCO SAN PABLO DE 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

220709 SAN MARTIN PICOTA TINGO DE 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

250105 UCAYALI CORONEL 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

030215 APURIMAC ANDAHUAYLAS SANTA MARIA DE CHICMO -73.54626986790 -13.66115711730

050403 AYACUCHO HUANTA HUAMANGUILLA -74.15894644920 -12.99265488410


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

120413 JUNIN JAUJA LEONOR 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

211001 PUNO SAN ANTONIO DE PUTINA PUTINA -69.83448632370 -14.70916613040

250107 UCAYALI CORONEL 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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Distritos-Peru-2017.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Distritos-Peru-2017.xlsx


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.


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 Name Country Code

2010 [YR2010]

2011 [YR2011]

2012 [YR2012]

2013 [YR2013]

2014 [YR2014]

2015 [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


Country Name Country Code

2010 [YR2010]

2011 [YR2011]

2012 [YR2012]

2013 [YR2013]

2014 [YR2014]

2015 [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.

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Internet.xlsx

http://blog.pucp.edu.pe/blog/raulchoque/wp-content/uploads/sites/905/2017/08/Internet.xlsx


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.


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.


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.

DATA VISUALIZATION WITH TABLEAU | Blog PUCP

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