Tag: data-science

My partner recently wrote an article for Changing Times, a grassroots newspaper that focuses on social change. Her article, Who’s not voting? Engaging with First Nations voters and young voters, is about voter turn-out in Australia and an important read.

While doing research for the article, she wanted to know which electorates in Australia had the highest proportion of young voters. Fortunately the Australian Electoral Commission (AEC) keeps a detailed record of the number of electors in each electorate available here. The records list the number of voters of a given sex and age bracket in each of Australia’s 153 electorates. To calculate and sort the proportion of young voters (18-29) in each electorate using the most recent records, I wrote the below R code for her:

library(tidyverse)

voters <- read_csv("elector-count-fe-2022.csv", 
                   skip = 8,
                   col_names = c("Description",
                                 "18-19", 
                                 "20-24", 
                                 "25-29", 
                                 "30-34", 
                                 "35-39", 
                                 "40-44", 
                                 "45-49", 
                                 "50-54", 
                                 "55-59", 
                                 "60-64", 
                                 "65-69", 
                                 "70+",  
                                 "Total"),
                   col_select = (1:14),
                   col_types = cols(Description = "c",
                                    .default = "n"))


not_electorates = c("NSW", "VIC", "ACT", "WA", "SA", "TAS", 
                    "QLD", "NT", "Grand Total","Female", 
                    "Male", "Indeterminate/Unknown")

electorates <- voters %>% 
  filter(!(Description %in% not_electorates),
         !is.na(Description))  

young_voters <- electorates %>% 
  mutate(Young = `18-19` + `20-24`,
         Proportion = Young/Total,
         rank = min_rank(desc(Proportion))) %>% 
  arrange(rank) %>% 
  select(Electorate = Description, Total, Young, Proportion, rank) 

young_voters

To explain what I did, I’ll first describe the format of the data set from the AEC. The first column contained a description of each row. All the other columns contained the number of voters in a given age bracket. Rows either corresponded to an electorate or a state and either contained the total electorate or a given sex.

For the article my partner wanted to know which electorate had the highest proportion of young voters (18-24) in total so we removed the rows for each state and the rows that selected a specific sex. The next step was to calculate the number of young voters across the age brackets 18-19 and 20-24 and then calculate the proportion of such voters. Once ranked, the five electorates with the highest proportion of young voters were

Electorate	Proportion of young voters
Ryan	0.146
Brisbane	0.132
Griffith	0.132
Canberra	0.131
Kooyong	0.125

In Who’s not Voting?, my partner points out that the three seats with the highest proportion of voters all swung to the Greens at the most recent election. To view all the proportion of young voters across every electorate, you can download this spreadsheet.