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

Original


A Less Victorian Top 20. From “The 2022 AFL Draft: Where they came from, the sliders and bolters, the father-son picks and more”, by C.Atkinson and S.Lawson. (2022). https://www.abc.net.au/news/2022-11-30/this-is-what-happened-at-the-2022-afl-draft/101715168


Objective

The objective of this data visulation is to highlight the staggering difference betweeen the amount of draft prospects that have been drafted to AFL listed teams from the years 2000-2022, from different states. The data groups all Australian states and territories besides Victoria, South Australia and Western Australia as “Other” as collectively they represent a small proportion of the data set. The target audience is AFL supporters and those that take an interest in the new players that are being recruited into the league.

The visualisation chosen had the following three main issues:

  • The choice by the Author to use a stacked area chart is a deceptive design choice for this visual, primarily due to the data set having multiple “0” values which may not be clearly perceived by the audience.

  • The data used source that has been referenced to as being used to produce this visual is a secondary data source that has only collated data from other sources. No where on the webpage does it reference how the data was collected or where it was sourced from originally.

  • This purpose of this data set is to compare the amount of draftees from different states, however since each category does not share a common baseline to be measured from, similar to a pie chart, it can be difficult for the viewer to distiniguish between proportions of the data set which are similar in size.

Reference

Code

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

library(ggplot2)
setwd("C:/Users/PC/Downloads")

df <- read.csv("A2_data.csv")

new_plot <- ggplot(df, aes(x=X.1)) + 
  geom_line(aes(y=Victoria, color="Victoria"), linewidth=1) + 
  geom_line(aes(y=South.Australia, color="South Australia"), linewidth=1) + 
  geom_line(aes(y=Western.Australia, color="Western Australia"), linewidth=1) +
  geom_line(aes(y=Other, color="Other"), linewidth=1) +
  labs(x="Year of Draft", y="Number of Draftees", color="State") +
  scale_color_manual(values=c("Victoria"="blue", "South Australia"="red", "Western Australia"="gold", "Other"="cyan"),
                     breaks=c("Victoria","South Australia","Western Australia","Other")) +
  ggtitle("Amount of AFL Draftees by State from 2000-2022") +
  scale_y_continuous(breaks=seq(0, 20, by=2))

Data Reference

Reconstruction

The following plot fixes the issues outlined earlier. The “0” values can now be easily observed by creating clear separation between each category.The common baseline also allows for the values that were similar in proportion to be clearly seen as different. The data has also been verified by two primary sources from the AFL and The Age.

Data Verification References