Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.

Original


https://howmuch.net/articles/worlds-economic-programs-against-coronavirus


Objective

The original visualization compares the massive stimulus packages for each country to that country’s Gross Domestic Product (GDP) using data from International Monetary Fund (IMF) and United Kingdom’s Office of National Statistics resources. The author of this visualization has plotted the data in regional map for each country with a fiscal stimulus package.

The objective of this visualization is to help researchers, economists, statisticians, investment professionals and the policy makers across the world to take significant decisions for assisting the small and medium businesses with relief packages and reopen the economies which have been worst hit by Covid-19 pandemic.

The targeted audience are economists, researchers, statisticians, investment professionals and policy makers around the world in the purpose of taking necessary decisions regarding the reopening of economies in the wake of Covid-19.

The visualisation chosen had the following three main issues:

  • First issue I identified is, a map is not quite a adequate way to depict any information related to monetary decisions or comparison of GDP or Fiscal Aid by each country. An ideal way to represent the comparison is by using a Bar graph or Line graph.

  • The original visualization is attractive and eye-catching. However human eye has a hard time appreciating regular areas and sizes. Second the issue is confusion with the size of each country and stimulus package related to it. For example, the United States has delivered the highest stimulus package of 2.3 trillion dollars and is shown the biggest in the map. Germany has delivered the second highest stimulus package but the size of that country is shown similar to China, Canada or Australia. So, it creates confusion whether the size of country shown in map describes the stimulus package.

  • The third issue is the colour scheme used to show the GDP percentage in both the maps and legend. Colour scheme used is hard to differentiate between GDP percentages of countries and makes the visualization uncertain. Similarly, colour schema used for legend does not clearly give what information is encoded in colour and in size.

Reference

• Dataset Source:- Visualizing Coronavirus Stimulus Program Around the World – Worlds Economic Programs Against the coronavirus, Total Fiscal Stimulus Packages Implemented to Fight the covid-19 in G20 countries - https://howmuch.net/articles/worlds-economic-programs-against-coronavirus.

• Original dataset source:- International Monetary Fund https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19 and United Kingdom Office of National Statistics https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/abmi/qna

Code

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

library(tidyr)
library(dplyr)
library(ggplot2)

dataset1 <- data.frame(Fiscal_Aid_Billion_Dollars = c(0.00,2.22,4.45,5.15,8.92,11.16,13.04,21.04,24.57,26.42,27.84,39.94,51.21,54.14,64.65,133.50,145.45,169.68,189.30,2300),
Country=c("South Africa","Indonesia","Argentina","Japan","Mexica","Turkey","South Korea","Saudi Arabia","Russian Federation","India","Italy","European Union","United Kingdom","France","Brazil","Australia","Canada","China","Germany","United States"),Percent_Contribution_of_Countrys_GDP=c(0.00,0.20,1.00,0.10,0.70,1.50,0.80,2.70,1.50,0.90,1.40,0.30,1.87,2.00,3.50,9.70,8.40,1.20,4.90,11.00))

dataset1 <- dataset1 %>% mutate(GDP_percent_bracket = factor(c("Less than 1%","Less than 1%","1 to 4.99%","Less than 1%","Less than 1%","1 to 4.99%","Less than 1%","1 to 4.99%","1 to 4.99%","Less than 1%","1 to 4.99%","Less than 1%","1 to 4.99%","1 to 4.99%","1 to 4.99%","5 to 9.99%","5 to 9.99%","1 to 4.99%","1 to 4.99%","10% and more"), levels = c("Less than 1%","1 to 4.99%","5 to 9.99%","10% and more")))
                                
data1 <- dataset1 %>% gather(Information,Value,-Country,-GDP_percent_bracket)


data1$Country <- factor(data1$Country,levels = c("South Africa","Indonesia","Argentina","Japan","Mexica","Turkey","South Korea","Saudi Arabia","Russian Federation","India","Italy","European Union","United Kingdom","France","Brazil","Australia","Canada","China","Germany","United States"),labels=c("South Africa","Indonesia","Argentina","Japan","Mexica","Turkey","South Korea","Saudi Arabia","Russian Federation","India","Italy","European Union","United Kingdom","France","Brazil","Australia","Canada","China","Germany","United States"))

facetplot1 <- ggplot(data = data1,
                      aes(y = Value,x = Country, fill = GDP_percent_bracket)) +
  geom_bar(stat = "identity") + coord_flip() +
  facet_grid(.~Information, scales = "free")

facetplot1 <- facetplot1 + theme_light()

colorfill <- c('#ff1493','#20b2aa','#3b3bff','#4b0082')

facetplot1 <- facetplot1 + labs(title = "Worlds Economic Programs against the Coronavirus", subtitle = "Total Fiscal Stimulus Package Implemented to fight Covid-19 in G20 countries", caption = "https://howmuch.net/articles/worlds-economic-programs-against-coronavirus",fill = 'Stimulus package As % of GDP',x='Country') +
  theme(plot.title = element_text(size = 100))+
  theme(plot.subtitle = element_text(size = 90))+
  theme(plot.caption = element_text(size = 80))+
  geom_bar(stat = "identity",colour = "black", width = 0.9)+
  geom_text(aes(label=paste0(Value)), size = 22, position = position_dodge(width = 0.4),hjust = -0.10)+
  theme(legend.position = "bottom")+
  theme(axis.title.x = element_text(size = 85))+
  theme(axis.title.y = element_text(size = 85))+
  theme(axis.text.x = element_text(size = 85, face = "bold"))+
  theme(axis.text.y = element_text(size = 85,face = "bold"))+
  scale_fill_manual(values = colorfill)+
  theme(legend.title = element_text(color = "black", size = 85),
          legend.text = element_text(color = "black", size = 85))+
  theme(strip.text.x = element_text(size = 90,colour = "black"))+
  theme(strip.background = element_rect(fill = "white"))

Data Reference • Dataset Source:- Visualizing Coronavirus Stimulus Program Around the World – Worlds Economic Programs Against the coronavirus, Total Fiscal Stimulus Packages Implemented to Fight the covid-19 in G20 countries - https://howmuch.net/articles/worlds-economic-programs-against-coronavirus.

• Original dataset source:- International Monetary Fund https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19 and United Kingdom Office of National Statistics https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/abmi/qna

Reconstruction

The following plot fixes the main issues in the original. I have converted into a facet barchart which shows both stimulus package delivered (USDollars Billions) by every country in G20 countries and GDP percentage share from the Country’s total GDP in the stimulus program. A darker shade of red on the country indicates the stimulus plan represents a larger percentage of GDP.