Original


Source: https://howmuch.net/articles/the-world-economy-2019


Objective

The original data visualization was posted as part of an article on the source website in September 2020. The visualization shows the variation of the world economy among different countries belonging to their specific regions based on the gross domestic product (GDP) for the year 2019.

It is an interesting visualization which provides readers with a better understanding of the distribution of wealth and economic power among nations especially just before the COVID disruption, thereby showing peak financial growth of countries.

The main aim is to highlight the most important economies in the world, along with their relative contributions to the world economy.

The targetted audience are :

  • Enterprise personnel that need to make well-informed choices on investments and business strategies in many nations, including executives and investors.

  • Government officials and international organisations that make policy decisions must be aware of how various nations’ economies are performing in order to make wise choices about trade policies and other economic matters.

  • Researchers, economists, and academics interested in exploring and understanding developments in the world economy.

The visualization chosen had the following three main issues:

  • Issue 1: Difficulty in finding countries and analyzing

Just looking at the data visualization, it is hard to find a country especially with the uneven shapes and random positioning of countries in the visualization, unless the country has a high GDP value of course.

This also makes it difficult for the viewer to compare and analyze the GDP of a country with other countries and the distribution of wealth across a region. For instance, it might be somewhat okay to find the top 5 countries with the highest GDP in the world but anything more than that is difficult to distinguish.

  • Issue 2: Data integrity with respect to GDP

The GDP value of most countries shown in the visualization for the year 2019 is moderately inaccurate. For instance, the GDP value of Japan is shown to be 5.08 Trillion in the visualization which depicts it covers 5.79% of the world economy, but in fact when I checked the original source of the data (World Bank) to which the visualization draws its source as well, it specifies the GDP recorded for Japan is 5.12 Trillion for the year 2019 which covers 5.91% of the world economy.

That’s a misrepresentation of 40 Billion USD and therefore deceiving the reader with incorrect information.

  • Issue 3: Unclear regional depiction and lousy color choice

The visualization shows different regions and their respective country’s GDP but what’s unclear here is what accounts for the rest of the world region. Australia is depicted in the visualization as a region which is incorrect, it should be Oceania and should include the relevant countries in this region to give the viewer clarity about the GDP of countries within this region. In general, there are no countries outside of Oceania, Latin America, North America, Africa, Europe, the Middle East, or Asia, therefore the rest of the world region in this visualization should at least show the countries that have high GDP value that play a significant role in the world GDP, which are not displayed for the viewer in the current visualization.

These regions are differentiated with variety of colors including green and red. At a first glance, the visualization can give the viewer a wrong image that European countries which are green in color are all wealthy countries and strong contributors to the world economy, on the other hand Australia in color red can signify as a weak contributor or a poor country in terms of GDP. Also green and red are a poor color choice considering the viewers who may have color blindness.

References

Code

The following code was used to fix the issues identified in the original.

library(ggplot2)

# The data set was downloaded from the source and some pre processing was done before importing the data set here to bring the data in the right format for reconstructing the visualization

df <- read.csv('mod_country.csv')

# Define custom colors for each region

colors <- c("North America" = "#F8766D", "Asia" = "skyblue", "Europe" = "purple3", "Latin America" = "brown4", "Oceania" = "violet", "Africa" = "yellow", "Middle East" = "deeppink3", "Rest of the world" = "orange")

# Data frame is reordered based on the GDP and the data is visualized with each region as a different facet

p2 <- ggplot(df, aes(x = reorder(Country.Name,-GDP), y = GDP, fill = Region)) +
  geom_bar(stat = "identity", color = "black") +
  facet_grid(Region ~ ., scales = "free_x", space = "free_x") +
  scale_fill_manual(values = colors) +
  labs(x = "Country", y = "GDP (in USD trillion)", title = "The World Economy: Gross Domestic Product (GDP) by Country 2019") +
  theme_bw() +
  geom_text(aes(label = paste(sprintf("%.2f", GDP_Percent), "%")), vjust = -0.5, size = 4.0) +
  theme(legend.position = "none", axis.text.x = element_text(angle = 45, hjust = 1), text = element_text(size = 22))

The data set used in the code is collected from the data bank of World Bank. The World Bank’s main collection of development indicators, the World Development Indicators (WDI), is compiled from officially recognized international sources.

Data Reference

Reconstruction

The following plot fixes the main issues in the original.

( Kindly use full screen mode in your browser and slightly zoom into the plot for a clearer view, thanks! )