Data Visualization

Case Studies
Client
Project type
Case Studies
Project year
2023
Time Frame
3 Months

Introduction

In this project, I presented detailed and rounded research with data visualizations analyzing the ‘World Bank Data of countries. The ideal focal point of this analysis was to dive into the relationship between the GDP, GDP per capita, and the health expenditures of all the countries around the world for a span of 22 years.

I found the dataset on Kaggle and exported it to Excel. Where I cleaned the data and used Tableau Public to create my visualizations.

Tableau Link

Background and reason for choosing the topic

I chose this topic because the Gross Domestic Product (GDP) and economic growth of all countries have long been subjects of fascination to me. As the world becomes increasingly interconnected, the importance of comprehending these indicators cannot be overstated. The GDP and economic growth rates of all countries are integral components of the global economic landscape. Their significance extends beyond economics, influencing social, environmental, and personal aspects of our lives.

Questions asked

  1. What are the countries with the lowest health expenditure, and what are some policies that could be implemented?
  2. What are the countries that need support unemployment-wise
  3. What is the annual growth of the GDP of each country in the world from 2000 to 2022
  4. Which is the country with the highest health expenditure in the year 2022
  5. What is the relation between the health expenditure of countries with the GDP of countries

Tools

Kaggle- Kaggle Link

I got my dataset from Kaggle. It is called World Bank Data on countries and consists of the GDP, inflation rates, Health expenditure (% in GDP), Education expenditure (% in GDP), Export (% in GDP), Import (% in GDP), Net Trade, Agricultural Activities (% in GDP), Service Activities (% in GDP), Industrial Activities (% in GDP), Research And Development (R&D), GDP Per Capita, Population, and Population Density from the year 2000 to 2022. 

Tableau

 Tableau is a leading data visualization tool used for data analysis and business intelligence. Once I exported my data to Excel and cleaned it, I was able to use this data in Tableau. Here I create all my data visualizations. As this was my first time using Tableau, I spent a lot of time going through each of the options and playing around with the visualizations in order to create good, meaningful visualizations. Tableau is a very easy and efficient tool to use.

Methods and Process

After finding my dataset from Kaggle, the first step in my process was to export this data from Kaggle to Excel. Based on all the data that I collected, my interest lay in the GDP of all countries, GDP per capita, and Health expenditure. This way I was able to clean the data and sort it out. Once I cleaned the data, I used Tableau to create my visualizations. 

(Fig 1)

1. GDP PER CAPITA OF COUNTRIES

I started my visualizations out with a simple easy to easy-to-understand graph. This showed the GDP per capita of each continent over the span of 22 years.

(Fig 2 GDP per capita of continents graph)

I started off by placing the Years and the AVG(GDP per capita) in the Columns and Rows respectively. My decision to use a line graph was so that I had a clean, clear visualization of the change in the GDP per capita of the continents through the years. I was also able to add color to the Continent names in order to differentiate them. 

(Fig 3 creating graph by placing years in columns and AVG GDP per capita in Rows)
(Fig 4  Adding in colors for each of the continents for better understanding)

On hovering over each point in the graph, the Name of the continent, the Year, and the average GDP per capita for that particular year are visible in the tooltip. 

(Fig 5 Hovering over a point in the graph gives information on the continent name, year, and average GDP per capita)

2. AVERAGE GDP AND AVERAGE GDP PER CAPITA OF COUNTRIES FROM 2000 TO 2022

Once I got my graph on the Average GDP per capita was finalized, I became interested in understanding the relationship between the average GDP per capita and the average GDP of each of the countries. In this visualization, I wanted to make sure that it was for each country, not continents, therefore I decided to use maps for my visualization. 

(Fig 6 visualization depicting Avg GDP, Avg GDP per capita, and Net trade between countries)
(Fig 7)

‍

By adding a color filter to the average GDP per capita with blue being the lowest and green being the highest average GDP per capita, I was able to showcase the different GDPs for each of the countries around the world. In this visualization, I also showcased the change in average GDP per capita over the years to give a clear indication of the growth in each of the countries. I also added an indication showing the country with the lowest and highest GDP per capita

(Fig 8 Changing of color of each country based on the GDP per capita of that year through the span of 22 years)

3. AVERAGE POPULATION DISTRIBUTION OF 27 COUNTRIES WITH THE HIGHEST UNEMPLOYMENT RATE

The next visualization of mine was based on the unemployment rate per country versus the population. I made this visualization because I wanted to see if there was a relationship between the unemployment rate and the GDP per capita of countries. 

I put the average population on the Y-axis and the year on the X-axis, creating a bar graph. 

In order to do so, I had to filter out the top 27 countries with the highest unemployment rate. I did this in my Excel spreadsheet. In the ‘Tables’ section in Tableau, I duplicated the country name set in order to bring in the 27 countries. I created a line graph here showing the relation between the 27 countries with the highest unemployment and population over the 22 years.

(Fig 9 Avg population distribution of 27 countries with highest unemployment rate)

I also added a country name set filter which allowed me to filter out countries or search for the countries that I wanted.

(Fig 10 Selection of different countries out of the 27 countries with the highest unemployment rate)

4. AVERAGE HEALTH EXPENDITURE VERSUS AVERAGE HEALTH EXPENDITURE (% GDP)

For the final visualization, I used a split bar graph to represent the average health expenditure by percentage of GDP. The average health expenditure is on the left-hand side and the average health expenditure (% GDP) is on the right. 

The average health expenditure (% GDP) refers to the total amount of money a country spends on healthcare as a percentage of its Gross Domestic Product (GDP). The names of the countries were added on the Y-axis while the average health expenditure and average health expenditure (% GDP) were added on the X-axis.

(Fig 11)

I also added a continent name set filter which allowed me to filter out continents in order to find different countries easily.

(Fig 12 Change in average health expenditure and average health expenditure (% GDP) for countries in the span of 22 years)

Conclusion

I was successfully able to clean the dataset from Kaggle and create four different visualizations that tell a story using Tableau Public. I tried to ensure that all my visualizations were clearly labeled and understandable for the users. I found the color scheme for the ‘Average GDP, Average GDP per capita, and Net trade of the countries from 2000-2022’ extremely important in order for the users to understand the visualization. As this was my first time working with Tableau, there definitely was a learning curve. I found it to be a very powerful tool for visualizations and extremely easy to use. Through making these visualizations, I was able to get a better understanding of the relationship between the GDP, the GDP per capita, the unemployment rate, and the health expenditure of countries.

These visualizations help in understanding the overall growth of GDP in countries from the year 2000 to 2022. It could also be used to understand the future change in the GDP and GDP per capita of countries all over the world.

Tableau Link