Lecture 4.1 KEY

Visualizing rankings

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Published

January 27, 2025

Note

This template follows lecture 4.1 slides. Please be sure to cross-reference the slides, which contain important information and additional context!

Setup

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                    setup                                 ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#..........................load packages.........................
library(tidyverse)

#..........................import data...........................
# find import code at: https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-03-05#grab-the-clean-data-here
jobs <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-05/jobs_gender.csv")

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                wrangle data                              ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

jobs_clean <- jobs |> 
  
  # add col with % men in a given occupation (% females in a given occupation was already included) ----
  mutate(percent_male = 100 - percent_female) |>
         
  # rearrange columns ----
  relocate(year, major_category, minor_category, occupation,
          total_workers, workers_male, workers_female,
          percent_male, percent_female,
          total_earnings, total_earnings_male, total_earnings_female, 
          wage_percent_of_male) |> 
  
  # drop rows with missing earnings data ----
  drop_na(total_earnings_male, total_earnings_female) |> 
  
  # make occupation a factor (necessary for reordering groups in our plot) ----
  mutate(occupation = as.factor(occupation)) |> 
 
  # classify jobs by percentage male or female (these will become facet labels in our plot) ----
  mutate(group_label = case_when(
    percent_female >= 75 ~ "Occupations that are 75%+ female",
    percent_female >= 45 & percent_female <= 55 ~ "Occupations that are 45-55% female",
    percent_male >= 75 ~ "Occupations that are 75%+ male"
  ))

Bar chart vs. Lolliplot chart

  • for both examples, we’ll:
    • flip axes to make space for labels
    • reorder groups
    • add scales labels
    • add direct labels

Bar chart

# bar chart ----
jobs_clean |> 
  filter(year == 2016) |> 
  slice_max(order_by = total_earnings, n = 10) |> # keep top 10 jobs with most `total_earnings`
  ggplot(aes(x = fct_reorder(occupation, total_earnings), y = total_earnings)) +
  geom_col() +
  geom_text(aes(label = scales::dollar(total_earnings)), hjust = 1.2, color = "white") +
  scale_y_continuous(labels = scales::label_currency(accuracy = 1, scale = 0.001, suffix = "k")) +
  coord_flip()

Lollipop chart

# lollipop chart ----
jobs_clean |> 
  filter(year == 2016) |> 
  slice_max(order_by = total_earnings, n = 10) |> 
  ggplot(aes(x = fct_reorder(occupation, total_earnings), y = total_earnings)) +
  ggalt::geom_lollipop() +
  geom_text(aes(label = scales::dollar(total_earnings)), hjust = -0.2) +
  scale_y_continuous(labels = scales::label_currency(accuracy = 1, scale = 0.001, suffix = "k"),
                     limits = c(0, 250000)) +
  coord_flip()

An aside: when to use geom_col() vs. geom_bar()

  • use geom_col() when you have data that’s already summarized
# geom_col() ----
jobs_clean |> 
  filter(year == 2016) |> 
  slice_max(order_by = total_earnings, n = 10) |> 
  ggplot(aes(x = occupation, y = total_earnings)) +
  geom_col() +
  coord_flip()

  • use geom_bar() when you need ggplot to count up the number of rows for you
# geom_bar() ----
ggplot(jobs_clean, aes(x = major_category)) +
  geom_bar() + 
  coord_flip()

Visualizing 2+ groups

  • for both examples, we’ll:
    • transform data from long to wide format
    • color by sex
    • dodge by sex

Bar chart (2 groups)

# bar chart ----
jobs_clean |> 
  filter(year == 2016) |> 
  slice_max(order_by = total_earnings, n = 10) |> 
  pivot_longer(cols = c(total_earnings_female, total_earnings_male), names_to = "group", values_to = "earnings_by_group") |> 
  mutate(sex = str_remove(group, pattern = "total_earnings_")) |> 
  ggplot(aes(x = fct_reorder(occupation, earnings_by_group), y = earnings_by_group, fill = sex)) +
  geom_col(position = position_dodge()) +
  coord_flip()

Lollipop chart (2 groups)

# lollipop chart ----
jobs_clean |>
  filter(year == 2016) |>
  slice_max(order_by = total_earnings, n = 10) |>
  pivot_longer(cols = c(total_earnings_female, total_earnings_male), names_to = "group", values_to = "earnings_by_group") |>
  mutate(sex = str_remove(group, pattern = "total_earnings_")) |>
  ggplot(aes(x = fct_reorder(occupation, earnings_by_group), y = earnings_by_group, color = sex)) +
  geom_point(position = position_dodge(width = 0.5)) +
  geom_linerange(aes(xmin = occupation, xmax = occupation, 
                     ymin = 0, ymax = earnings_by_group),
                 position = position_dodge(width = 0.5)) +
  coord_flip()

Dumbbell plot

(More) data wrangling

  • there are too many occupations to reasonably plot at once, so let’s take just 10 random occupations from each group (female-dominated, male-dominated, and evenly(ish) split); we’ll also only use 2016 data
#....guarantee the same random samples each time we run code.....
set.seed(0)

#.........get 10 random jobs that are 75%+ female (2016).........
f75 <- jobs_clean |> 
  filter(year == 2016, group_label == "Occupations that are 75%+ female") |> 
  slice_sample(n = 10)

#..........get 10 random jobs that are 75%+ male (2016)..........
m75 <- jobs_clean |> 
  filter(year == 2016, group_label == "Occupations that are 75%+ male") |> 
  slice_sample(n = 10)

#........get 10 random jobs that are 45-55%+ female (2016).......
f50 <- jobs_clean |> 
  filter(year == 2016, group_label == "Occupations that are 45-55% female") |> 
  slice_sample(n = 10)

#.......combine dfs & relevel factors (for plotting order).......
subset_jobs <- rbind(f75, m75, f50) |> 
  mutate(group_label = fct_relevel(group_label, 
                                   "Occupations that are 75%+ female", 
                                   "Occupations that are 45-55% female", 
                                   "Occupations that are 75%+ male"))

Build dumbbell plot

# initialize plot (we'll map our aesthetics locally for each geom, below) ----
ggplot(subset_jobs) +
  
  # create dumbbells ----
  geom_segment(aes(x = total_earnings_female, xend = total_earnings_male, 
                   y = fct_reorder(occupation, total_earnings), yend = occupation)) + # reorder occupation by avg_salary here
  geom_point(aes(x = total_earnings_male, y = occupation), 
             color = "#CD93D8", size = 2.5) +
  geom_point(aes(x = total_earnings_female, y = occupation), 
             color = "#6A1E99", size = 2.5) +
  
  # facet wrap by group ----
  facet_wrap(~group_label, nrow = 3, scales = "free_y") + # "free_y" plots only the axis labels that exist in each group
  
  # axis breaks & $ labels ----
  scale_x_continuous(labels = scales::label_dollar(scale = 0.001, suffix = "k"),
                     breaks = c(25000, 50000, 75000, 100000, 125000))