Homework answers / question archive / Tableau: Gapminder Animation Data Require data pertaining to the following parameters : Life Expectancy in years The income per person (GDP/capita) Population, Total Regions of the World We will use the basic dataset that comes with Tableau, but if you want to download the full data set from the year 1800 to 2018 from the Gapminder site here (Links to an external site

Tableau: Gapminder Animation Data Require data pertaining to the following parameters : Life Expectancy in years The income per person (GDP/capita) Population, Total Regions of the World We will use the basic dataset that comes with Tableau, but if you want to download the full data set from the year 1800 to 2018 from the Gapminder site here (Links to an external site

Business

Share With

---

Tableau: Gapminder Animation

Data

Require data pertaining to the following parameters :

• Life Expectancy in years
• The income per person (GDP/capita)
• Population, Total
• Regions of the World

We will use the basic dataset that comes with Tableau, but if you want to download the full data set from the year 1800 to 2018 from the Gapminder site here (Links to an external site.)or a Github Repository (Links to an external site.) for easy access.

Task

You will need to create two visuals:

1. Recreate the animation made by Hans Rosling using Tableau's world indicators data set to show how life expectancy and GDP per capita change together around the globe.
2. Create a new visualization using the same data set to show a different area of interest.
   • For example, you may show:
     • C02 emissions across countries
     • Birth rate changes over time
     • Mobile phone usages
     • Infant mortality rates across regions
       
       Context

Lesson modified from "Recreating Gapminder in Tableau: A Humble tribute to Hans Rosling" by Parul Pandey (Links to an external site.) on Medium

“My interest is not data, it’s the world. And part of world development you can see in numbers.” — Hans Rosling

Hans Rosling was a visionary. He had a way with numbers. A physician, teacher, and statistician, he challenged millions of peoples’ biased notions about basic issues like poverty and population growth. He did not achieve this by giving mundane lectures or boring presentations but by using clever visualizations, which ushered in an era of smart data visualization techniques. Rosling, together with his son and daughter-in-law, co-founded the Gapminder Foundation to develop Trendalyzer, a software to convert international statistics into moving, interactive graphics.

A small clip from ‘The Joy of Stats’ Documentary (Links to an external site.)

In the above video, Hans Rosling takes us through 200 years of global development. In this spectacular section of ‘The Joy of Stats’, he tells the story of the world’s 200 countries over 200 years using 120,000 numbers — in just four minutes. Plotting life expectancy against income for every country since 1810, Hans showed how the world we live in is radically different from the world most of us imagine it to be. This will a small tribute to the master storyteller who passed away on 7 February 2017.

Today we will analyze how Life Expectancy in years (health) and GDP per capita (wealth) have changed over time in the world for various countries by updated with data from 2000 - 2012.

Evaluation

The visualizations may take many different forms. If you decide to make a commonly used graph, you should make sure to meet the following criteria described below: graph mechanics, communication, and graph choice. If you want to do a more experimental graph, I will apply only the criteria that are relevant to that graphic. Be sure to revisit your textbook and previous assignments:

GRAPH MECHANICS:

• Descriptive title
  • Should 1) be in the form of a statement, 2) mention the subject, 3) include appropriate variables, and 4) include relevant details about the experiment that will help readers understand the take-home message
• Label for the x-axis (e.g., time)
  • Should be appropriate and descriptive for the experiment. For graphs with categorical independent variables, there needs to be a label under each set of data and a larger label under all data plotted.
• Label for the y-axis (e.g., graduation rate)
  • Should be appropriate and descriptive for the experiment. If the data are manipulated (average, change, percentage, etc.), then that should be indicated on the y-axis.
• Units for the x-axis and/or y-axis (e.g., years, percent)
  • Should be appropriate and descriptive for the data displayed.
• Scale (appropriate intervals and range for data)
  • It should be appropriate for the data displayed such that the increments are clear and without clutter and should include appropriate significant figures. If the scale is discontinuous or does not start at the origin, it should be indicated by a break in the axis
• Key (defines different data sets that are plotted only if multiple data types, e.g. may use color to separate types)
  • Should be appropriate and descriptive for the data displayed. It should include: 1) descriptions of different colors (if applicable), 2) the sample size, and 3) the number of trials

COMMUNICATION:

• Ease of understanding – Aesthetics
  • The graph is aesthetically pleasing if 1) the data plotted to take up sufficient room in the Cartesian plane, 2) a legible size font is used, 3) the lines of the x- and y-axes are clear and legible 4) data are displayed in an appropriate number of bars and lines, and 5) there are no “junk” elements such as distracting background colors, patterns, and dark gridlines.
• Ease of understanding – Take-home message
  • If the graph has sound construction and mechanics that allow for clear sorting of trends and take-home message.

GRAPH CHOICE:

• Graph type (bar, line, scatter, dot, box and whisker)
  • If data displayed in a graph are appropriate for both independent and dependent experimental variables (i.e., categorical and continuous) and data. (Referring to the data form.)
• Data displayed (raw, averages, changes, percentage)
  • If the graph indicates the type of data (e.g., raw, averages, etc.) that are plotted. There should be a clear distinction between raw data and manipulated data based on the information presented in the key (i.e., sample size and the number of trials) and axis label. If the graph is showing averages, then these should also be accompanied by SD or error bars.
• Alignment (the graph presented aligns with the research question. Other graphs can be exploratory.)
  • If the graph is completely aligned with the research question and/or hypothesis. In other words, the independent and dependent variables and information about the experiment are explicit.

Criteria for an effective graph are modified from the following publication:

Angra, A., & Gardner, S. M. (2018). The Graph Rubric: Development of a Teaching, Learning, and Research Tool. CBE–Life Science Education 17(4).

Key Findings by Parul Pandey (Links to an external site.):

Let us get to know what are the key findings from this graph that makes it so important to the world.

• We have an axis for health life i.e Life Expectancy from 25 years to 75 years and another axis for wealth i.e income per person. This implies countries lying at the bottom left of the graph are poor and sick and the ones at the top right are rich and healthy.
• Contrary to the normal misconception, the world is not divided into 2 categories ie. developed and developing worlds. In fact, there are 4 income levels and the majority of the population lives in the middle. As we can see, the two large circles denote the Asian Giants: India and China which lie in Income level 2 and Income level 3 and they are slowly inching towards the 4th Income level.
• That huge historical gap between the west and the rest is now closing and we have become an entirely new Converging world. There is a clear trend into the future with aid, trade, and green technology that it is fully possible that everyone can make it to the healthy wealthy corner.

Conclusion

Data Visualisation is not merely a tool, it's an art of storytelling. A story told with data can change the way we see the world, creating a conviction that may even call us to action. The goal should be to use data and present it in the form of a story that has a profound effect and put it in the hands of decision-makers who can affect outcomes.