The World’s Billionaires list is an annual listing of the world’s wealthiest people based on their wealth conducted by Forbes. Forbes calculated the net worth of individuals and ranked 2905 of them in the final list this year. The net worth of the world’s billiaonaires is $8 trillion, which is $700 billion less than last year due to the fall of the stock market caused by the global pandemic. Upon looking at the list of billionaires every year, the common characteristic that can be found is that most of them are males. Out of the 2905, only 241 are female billionaires whose total net worth is under $1 trillion.

Most of the visualization illustrates the world’s richest men. This visualization illustrates the world’s richest women, their worth and their industries.

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

Original


Source: howmuch.net, Forbes (2020).


Objective

The objective of this visualization would be to shine some light on the women who are achieving in the business sector. It will inspire aspiring women entrepreneurs by also conveying the fields in which most of the women have achieved.

The visualisation chosen had the following three main issues:

  • The visualization uses a deceptive method of using size to depict the net worth of the women.
  • It also uses colours to indicate the net worth which is a redundant way to represent the net worth. Both methods reduce the accuracy and make it hard to compare values among different circles and across industries.
  • The arrangement of the entire visualisation does not make it very hard to interpret but the text containing rank, network, name, industry defeats the goal of the visualization as the audience could simply read the data from a table. The background contains the continent of origin of the women which is hidden by the circles, making it useless. The arrangement is just big and small circles all over the plot surrounded by texts which can be considered as a visual bombardment.

Reference

Code

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

# Importing required libraries
library(knitr)
library(ggplot2)
library(rmarkdown)
library(magrittr)
library(dplyr)

# Reading the dataset
richest = read.csv("Women_billionaire.csv")

# Ordering Names based on Net Worth
richest <- transform(richest, Name = reorder(Name, Net.Worth))

# Factorising Industry
richest$Industry <- factor(richest$Industry, levels = c("Fashion & Retail", "Diversified", "Technology", "Food & Beverage", "Real Estate", "Automotive", "Healthcare", "Metals & Mining", "Finance & Investments", "Media & Entertainment","Construction & Engineering","Logistics", "Manufacturing", "Energy"))

# Encoding CoUntry into respective ISO codes
richest$Country <- factor(richest$Country, levels = c("United States", "France", "Germany", "China", "Australia", "Netherlands","Chile","Sweden", "Hong Kong", "Italy", "Switzerland", "Denmark"), labels = c("US", "FR", "DE", "CN", "AU", "NL", "CL", "SE", "HK", "IT", "CH", "DK"))


# Plotting the fixed visualization
p1 <- ggplot(richest, aes(fill = Name, y = Industry, x = Net.Worth) ) +
  geom_bar(position = "dodge", stat = "identity") +
  geom_col(aes(col = Industry), position = "dodge", show.legend = FALSE) +
  geom_text(aes(label = Name), position = position_dodge(width = 0.90), hjust = -0.05, vjust = 0.35, size = 3) +
  geom_text(aes(label = Country), position = position_dodge(width = 0.95), hjust = 1.05, vjust = 0.35, size = 5, fontface = "bold") +
  scale_fill_manual(values = c("mistyrose", "peachpuff", "peachpuff", "#e7298a", "#e7298a", "#e7298a", "#e7298a", "pink", "#67001f", "#f768a1", "#fcc5c0", "#ce1256", "#67001f", "hotpink", "#67001f", "#dd3497", "#67001f", "#e7298a", "#e7298a", "deeppink", "#fa9fb5", "#df65b0", "#df65b0", "#e7298a", "#fa9fb5", "#f768a1", "#ce1256", "#dd3497", "#df65b0", "#e7298a", "#67001f", "#ce1256", "#980043", "#67001f", "#67001f"))+
  scale_color_manual(values = c("black", "black", "black", "black", "black", "black", "black", "black", "black", "black", "black", "black", "black", "black"))+
  scale_x_continuous(limits = c(0, 90), breaks = c(5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60))+
  guides(fill = FALSE, size = FALSE) +
  theme(axis.text=element_text(size = 10))+
  labs( title = "The Wealthiest Women in the World", subtitle = "Top 35's Estimated wealth, Industry and Country (ISO Codes)", y = "Industry of Achievement", x = "Net Worth in Billions")

Data Reference

Reconstruction

The following plot fixes the main issues in the original.