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

Original


Source: ESPNcricinfo story section (2019).


Objective

The objective of this visualisation was to compare the top 10 scorers in the 2019 Cricket World Cup by contribution to their team runs. The targeted audience was cricket and sport fans worldwide.

The visualisation chosen had the following three main issues:

*A pie chart is used to represent percentages of a whole at a set point in time. The categories which represent the scorers percentages are from their own individual teams, as each slice in the pie chart represents a percentage from a different time and team, this doesn’t make a whole which adds up to 1. Therefore, defeating the purpose of a pie chart. Instead we could use a bar chart to compare proportions.

*The colour is inconsistent and makes it difficult to compare segments. The different colours don’t have a pattern or a similar hue, some colours are much darker than others where the text colour label also had to change from black to white based on its background colour, this makes it difficult for the audience to compare scorers as the colours are cluttered shifting our focus unnecessarily. As there is no legend to justify the colour, no consistent hue or contrasting saturation, the use of colour to differentiate segments has been done poorly. Instead, a bar chart can be used to differentiate scorers through the x-axis labels and their own individual bars that vary in length, basically by their position.

*The size and angle of each segment represents proportion, but as the proportions are similar it makes it harder to judge and compare segments. As differences in proportion are easier to see when large, the similar proportions make it harder to compare scorers like Rohit to Barbar. Also the angle at which we view the proportions makes it difficult to accurately judge, for example its relatively easier to compare the size of Rohits proportion to Pooran, but not from Rohit to Warner because of the angle.

Reference

*Muthu, A., 2019. Kane Williamson’s is the hand that steadies New Zealand’s ship. [online] ESPNcricinfo. Available at: https://www.espncricinfo.com/story/kane-williamson-s-is-the-hand-that-steadies-new-zealand-s-ship-1193354 [Accessed 3 May 2022].

Code

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

library(ggplot2)
library(dplyr)
topscorers <- data.frame(Scorers = c("Rahmat", "Kusal", "Root", "Pooran","Plessis","Babar","Warner",
                                 "Shakin", "Rohit","Williamson"),
                      Perc = c(14.8, 18.16,19.07,20.01,21.06,24.51,25.02,28.25,29.05,30.23),
                      stringsAsFactors = TRUE)

topscorers$Scorers <- topscorers$Scorers %>%
  factor(levels = topscorers$Scorers[order(-topscorers$Perc)])

p1 <- ggplot(topscorers ,aes(x = Scorers, y = Perc))

p2 <- p1 + geom_bar(stat="identity") + theme(axis.text.x=element_text(angle=45,hjust=1)) +
  labs(title = "The World Cups Big Guns: Percentage of Team Runs Scored by Top Scorers",
       y = "Percentage of Team Runs Scored",
       x = "Cricket Players") +
geom_text(aes(label=round(Perc,2)), vjust = -0.5,size = 3)

Data Reference

Reconstruction

The following plot fixes the main issues in the original.