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

Original


Source: World most admired companies (2018)


Objective

The objective of the original data visualisation is to rate the strength of each of the most admired companies in the world. The targetted audience is the companies and customers. The companies can build a solid reputation regardless of industry. The map contains the top 50 all-star companies from the Fortune’s list of the most admired companies in the world and their logos that are kept in a circle.The company’s total market capitalization is represented by the size of the circle, with the largest circles representing companies valued at over $500B. The different colour represents the different industry, and then the companies of same industries are grouped together.

The visualisation chosen had the following three main issues:

  • Comparison is difficult: The map tells that the size of the circle is the company’s total market capitalization. But looking at the size, it is difficult to predict the value. We have to read the value written in the circle to know the size.
  • Size proportion is incorrect: For example, consider Alphabet($341) and Apple($905) companies. By looking into values, the size of the circle of the Apple company must be almost three times bigger than the Alphabet company. But it is not the case.
  • Visualisation is cluttered: It is difficult to interpret by looking into the map. And also, there is too much information wirtten as labels in the circle like logo, company name and market capitalization value.

Reference

Code

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

library(readr)     # to import datasets 
library(dplyr)     # for pipe operator 
library(ggplot2)   # for display of plots 
library(magrittr) 


Admired_Companies <- read.csv("Admired_Companies_by_Fortune.CSV", stringsAsFactors = FALSE) 
Admired_Companies$Company_Country <- paste(Admired_Companies$Company,Admired_Companies$Location,Admired_Companies$Country, sep = ",")   # to compaine company and country 
Admired_Companies <- Admired_Companies %>% select("Company_Country","Industry","Market_Cap") %>% filter(Market_Cap != 0)

# Group and split combanies based on Industry

Admired_Companies1 <- Admired_Companies %>% select("Company_Country","Industry","Market_Cap") %>% filter(Industry =='Financial Services'| Industry =="Computers/Software/IT"  | Industry == "Insurance"  | Industry == "Insurance" | Industry == "Internet" | Industry =="Petroleum"| Industry == "Pharmaceuticals")

Admired_Companies2 <- Admired_Companies %>% select("Company_Country","Industry","Market_Cap") %>% filter(Industry =='Aerospace/Airlines'| Industry =='Apparel'| Industry =="Hotels" | Industry =="Construction" | Industry == "Entertainment" | Industry == "Transportation" )

Admired_Companies3 <- Admired_Companies %>% select("Company_Country","Industry","Market_Cap") %>% filter(Industry =='Medical Products and Equipment'| Industry =='Merchandiser/Retailers'| Industry =="Motor Vehicles" | Industry =='Food & Beverages'   | Industry == "Soaps and Cosmetics" | Industry == "Telecomm" | Industry =="Industrial Machinery"  )


# plotting based on above split
p1<-ggplot(Admired_Companies1, aes(Industry, Market_Cap, fill = Company_Country)) +  geom_bar(position="dodge",stat="identity")  + theme(axis.text.x = element_text(angle=40,hjust = 1) ) + ggtitle("World most admired companies (2018)")
 
 p2<-ggplot(Admired_Companies2, aes(Industry, Market_Cap, fill = Company_Country)) +  geom_bar(position="dodge",stat="identity")  + theme(axis.text.x = element_text(angle=40,hjust = 1) ) + ggtitle("World most admired companies (2018)")
  
  p3<-ggplot(Admired_Companies3, aes(Industry, Market_Cap, fill = Company_Country)) +  geom_bar(position="dodge",stat="identity")  + theme(axis.text.x = element_text(angle=40,hjust = 1) )+ ggtitle("World most admired companies (2018)")

Data Reference

Reconstruction

The following plot fixes the main issues in the original.