Assignment 2

Deconstruct, Reconstruct Web Report

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

Original

Source: Fair share (Ahmed, D.R., Saqib Iqbal, 2020).

Objective

Explain the objective of the original data visualisation and the targetted audience.

The visualisation chosen had the following three main issues:

• The pie chart is not the best choice here for accurate and straghitforward comparisons. Also, one pie chart cannot handle one or more variables at the same time. That is why two pie charts are used in this data visualisation, which is not an optimal option.

• The authors fail to label all sectors with their percentages, which provides less information for audiences. Additionally, there is negative value (energy earning) in the original data, but it is somehow still put in pie chart.

• Colour issues: The background colours do not provide sufficient contrast so it is hard for audiences to read the numbers on the pie charts. The colours of different sectors of pie charts are so similar such that audiences cannot easily distinguish them.

Reference

• Ahmed, D.R., Saqib Iqbal (2020). Detached from reality? Why U.S. stocks just hit a new high. Reuters. [online] Retrieved September 21, 2020: https://www.reuters.com/article/us-usa-stocks-s-p500-analysis/detached-from-reality-why-u-s-stocks-just-hit-a-new-high-idUSKCN25E1ZZ

Code

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

library(ggplot2)
newdf <- data.frame(measure=rep(c("Share of Market Capitalization", "Share of Earnings"),11),
  sector=rep(c("Consumer Discretionary", "Consumer Staples", 
                               "Energy", "Financials", "Health Care","Industrials","Materials",
                               "Real Estate", "Technology","Communication Services",
                               "Utilities"), each=2),
                  
                  percentage=c(11, 4, 7, 9, 2, -4,10,15,14,25,8,2,3,3,3,3,28,27,11,12,3,4))

p1<-ggplot(data=newdf, aes(x=reorder(sector,percentage), y=percentage, fill=measure)) +
  geom_bar(stat="identity", position=position_dodge())+
  geom_text(aes(label=percentage), vjust=-0.25,
            position = position_dodge(0.9), size=3.5)+
  scale_fill_brewer(palette="Paired")+
  theme_minimal()+
  labs(title = "Share-weighted percentages of S&P by sector, 2020 Q2",
       y = "Percentage",
       x = "Market sectors")+
  theme(legend.position="top")+
  theme(axis.text.x=element_text(angle=45,hjust=1)) 
p1

Data Reference

• IBES data from Refinitive. (2020). Refinitiv Proprietary Research. Retrieved September 21, 2020: https://www.trpropresearch.com/pdf/This_Week_In_Earnings.pdf/
• S&P 500 website. (2020) - S&P Dow Jones Indices. Retrieved September 21, 2020:: https://www.spglobal.com/spdji/en/indices/equity/sp-500/#data.

Reconstruction

The following plot fixes the main issues in the original.