day 1: part to whole

library(tidyverse)
library(tidytuesdayR)
library(tidyplots)
library(janitor)
library(ggeasy)
library(Manu)
library(here)


# choosing a dataset randomly

set.seed(1)
ttyears <- c(2018:2025)
ttweeks <- c(1:52)

# choose a year at random
chosen_year <- sample(ttyears, size = 1)

# choose at week at random
chosen_week <- sample(ttweeks, size = 1)

# read the data from that year/week

df <- tidytuesdayR::tt_load(chosen_year, chosen_week)

# print dataset
print(df)

salaries <- df[[1]]

The data for this plot comes from a 2018 TidyTuesday challenge. The Week 4 data that year was about Australian Salaries. I was interested in differences in the average salary of men and women in different occupations. The 30DayChartChallenge theme today is part to whole, so I decided to plot the proprotion of occupations in which women are paid more than men. It would be interesting to know whether this stat has improved since 2018.

data prep

# make new column flagging occupations where women get paid more

salaries_gender <- salaries %>%
  select(-1, -2, -individuals) %>%
  pivot_wider(names_from = gender, values_from = average_taxable_income) %>%
  rowwise() %>%
  mutate(salary_diff = Female - Male) %>%
  ungroup() %>%
  mutate(bias = case_when(salary_diff > 0 ~ 1, 
                          salary_diff < 0 ~ 0)) %>% # tag occupations where women are paid more on average with 1
  mutate(direction = case_when(bias == 0 ~ "Men > Women", 
                               bias == 1 ~ "Women > Men"))  |> 
  filter(!is.na(direction)) 

# count occupations where women are paid more, set up labels, remove NAs
summary <- salaries_gender %>%
  count(bias) %>%
  mutate(bias = factor(bias,
    levels = c(1, 0),
    labels = c("Women > Men", "Men > Women")
  )) %>%
  na.omit() 

ggplot

summary %>%
  ggplot(aes(x = "", y = n, fill = bias)) +
  geom_col(width = 0.7) +
  labs(
    x = NULL,
    y = "Number of occupations",
    fill = NULL,
    title = "Gender Pay Gaps in Australia", 
    subtitle = "Of the 1105 occupations in this dataset, only 7% pay women on average \nmore than men", 
    caption = "TidyTuesday 2018 Week 4 | Data from data.gov.au"
  ) +
  theme_minimal(base_family = "Lato") +
  scale_fill_manual(values = get_pal("Takahe")) +
  scale_y_continuous(limits = c(0,1200), breaks = seq(0, 1200, 300)) +
  theme(
    plot.caption = element_text(hjust = 0.5) # Centers the caption
  )

tidyplots

salaries_gender |> 
  tidyplot(colour = direction) |> 
  add_barstack_absolute() |> 
  adjust_size(unit = "mm", width = 120, height = 80) |> 
  reorder_color_levels("Women > Men") |> 
  adjust_y_axis_title("Number of occupations") |> 
  adjust_colors(new_colors = c("#DD3C51", "#313657")) |> 
  theme_tidyplot()  |> 
  remove_x_axis_ticks() |> 
  remove_x_axis_title() |> 
  adjust_y_axis(limits = c(0,1200)) |> 
  adjust_font(fontsize = 12, family = "Lato") |> 
  adjust_title("Gender Pay Gaps in Australia: Of the 1105 occupations in this dataset, \nonly 7% pay women on average more than men", fontsize = 14)  |> 
  adjust_caption("TidyTuesday 2018 Week 4 | Data from data.gov.au") |> 
    remove_legend_title() |> 
  save_plot(here::here("charts26", "2026-04-01_partwhole","tidyfeatured.png"))