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

Original


Source: All of the World’s Wealth in One Visualization (2019).


Objective

The financial concept of wealth is broad, and it can take many forms. In 2019, total world wealth grew by 2.6% over the previous year. With this visualization we will look at the to 15 richest countries in the Worl which contribute to 84.3% of global wealth.

The visualisation chosen had the following three main issues:

  • Lack of Accuracy:

Unordered pentagon/hexagon shapes in pie chart lack accuracy. Area for each pentagon/hexagon is not divided as per their %. US is appoximately double than china however distribution is deceptive in the visualization.

  • Mismanagement of Small Proportions:

Performed poorly with small proportions of the countries and their labels. There are better ways to show small proportions, like combine them if we are discussing about top 15 countries revenue.

  • Color Scheme for Countries/continents:

For most of the countries, almost same Color scehme is given depending on same continents, however not every case is true. For e.g India, China, South Korea, HongKong, Taiwan belong to Continent Asia and is covered with Blue, however Russia is Europe continent and should take some other color scheme.

Target audience

Target Audience is World Bank which provides loans and grants to the governments of poorer countries for the purpose of pursuing capital projects and all financial firms across globe. This visualization will help them channelize the money for needy countries.

Reference

Code

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

Let’s take a look at the 15 countries that hold the most wealth, according to Credit Suisse.

#Installing the packages for visualization and designing

library(ggplot2)

library(forcats)

library(extrafont)

#Importing the file and assigning it to 'gw'
gw <- read.csv("GWealth.csv")  


#Understanding the structure of the dataset
str(gw)        
## 'data.frame':    16 obs. of  5 variables:
##  $ Rank                  : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Country               : Factor w/ 16 levels "All Other Countries",..: 16 4 9 6 15 5 7 8 3 12 ...
##  $ Region                : Factor w/ 6 levels "","Asia-Pacific",..: 6 3 2 4 4 4 5 4 6 4 ...
##  $ Total.Wealth..B..2019.: Factor w/ 16 levels "105,990 ","11,358 ",..: 1 12 7 6 5 4 3 2 16 15 ...
##  $ X..Global.Share       : num  29.4 17.7 6.9 4.1 4 3.8 3.5 3.1 2.4 2.2 ...
#Renaming column names for ease of plotting
colnames(gw) [4] <- 'TotalWealth' 

colnames(gw) [5] <- 'GlobalShare'


#Correcting and redordering the ranks for Global Share.
gw$Country <- fct_reorder(gw$Country, gw$GlobalShare)

Reconstruction

The following plot fixes the main issues in the original. “ggplot” provides access to wider range of plots and unlimited customization.

ggplot(data = gw, aes(x = Country, y = GlobalShare)) + geom_bar(stat ="identity") + ylim(0,32) + coord_flip() + labs(x = "Country", y ="Percentage", title = "Global Share of World's Wealth in 2019(amount in billions)")+ geom_text(aes(label =TotalWealth),size=3,position = position_dodge(width = 0.4),hjust=-0.10) + theme(plot.background = element_rect(),
        legend.background = element_rect(),
        text=element_text(family=""),
        title = element_text(),
        legend.title = element_blank())

Data Reference

it’s worth mentioning that Credit Suisse, the authors of the Global Wealth Report 2019 and the source of all this data, notes that the 1.2% increase has not been adjusted for inflation.

Findings

Leading the pack is the United States, which holds ~$106.0 trillion of the world’s wealth — equal to a 29.4% share of the global total. Interestingly, the United States economy makes up 23.9% of the size of the world economy in comparison. Impressively, the combined wealth of the U.S. and China is more than the next 13 countries in aggregate — and almost equal to half of the global wealth total.

With the new visualization, we are able to fix the issues below:

  • Whole visualization is divided into top 15 countries with highest wealth plus rest of the countries. Graph is giving accuracy in terms of bar size. It is really easy for global financial firms to refer to the visualization and invest accordingly.

  • Loud color patterns are removed. We have a visualization ready for professional use. It looks quite user friendly and intuitive with clear comparisons.

  • Information is plotted against x-axis and y-axis. To show the additional information, we added total wealth next to each bar. hence, visualization is giving information about top 15 countries(in periodic order), their global share in total wealth and the total wealth of their assets.