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

Original


Source: Illion & Alpha Beta Australia (2020).


Objective

The objective of the original data visualisation was to highlight patterns in consumer spending attributed to superannuation withdrawals as a result of the Federal Government’s COVID-19 early release scheme. The intended audience is government, business, economists and public policy professionals. Illion is a credit ratings agency whilst Alpha Beta is an economics consultancy.

The visualisation chosen had the following three main issues:

  • The use of a pie chart is problematic as it relies on two inferior perceptual mechanisms; area and angle. In the pie chart, it is difficult to intuitively distinguish proportional differences between the categories of spending as well as between the differences between the two withdrawal rounds. In order to compensate, the visualisation extensively uses labeling to display information, without which you would not be able to see the differences, this highlights the weakness of pie charts and how a simple table would be more informative.
  • The choice of the three spending categories (Debt repayment, Essential, and Discretionary) are ineffective as they aggregate too much information. The visualisation suggest that there was little difference in the spending patterns between the super withdrawal rounds. However, the data has enough detail to create more granular groups which highlight differences in spending which are currently overlooked. The negative effect of this is that interesting differences are currently being overlooked.
  • The blue tone colour scheme used in the visualisation suggests that there is an ordered relationship between the categories. A discreet colour scheme would have been more appropriate to reflect the unorded relationship between the variables. The negative impact of this it does not reflect the true relationship between the categories.

Reference

Code

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

library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
library(readxl)

super <- read_excel("super.xlsx")

super$round %<>% factor( c("Round 2: $3618", "Round 1: $2855"), ordered = T)

super$agg_group %<>% factor()

super$per_labels <- c("13.76%", "16.95%", "18.45%", "12.98%", "14.75%",  "8.20%", "14.91%", "12.43%", "13.90%", "22.62%", "10.70%", "16.22%", "12.07%", "12.05%")

#Graph

s <- ggplot(data = super, aes(x = per_spend, y = round, fill = agg_group))

s1 <- s + geom_bar(stat = "identity") +
  scale_fill_manual(values = c('#66c2a5','#fc8d62','#8da0cb','#e78ac3',           '#a6d854','#ffd92f','#e5c494')) +
  theme_minimal() +
  theme(legend.position = "top", legend.title = element_blank()) +
  labs(title = "The second round of superannuation withdrawals were less of a hangover!",
       subtitle = "Share of extra spending in the fortnight after superannuation withdrawal by category,\ninset with % of total",
       x = "Percentage",
       y = NULL,
       caption = "Source: Illion & Alpha Beta Australia (2020)") +
  geom_text(aes(label = per_labels), size = 2, position = position_stack(vjust = 0.5))

Data Reference

  • Illion & Alpha Beta Australia, 2020, The second round of super withdrawals has continued to fund discretionary purchases, viewed on 1 September 2020,

Reconstruction

The following plot fixes the main issues in the original.