Statistics

R data visualization of wage distributions by education level using ggdist dotted distributions and the ISLR2 Wage dataset.

Published

April 9, 2022

Overview

This is one of my favourite visualizations. It looks like very simple, and straight forward with the use of the ggdist::stat_dots function to make dotted distributions of the wages by highest educational status reached.

The Tidyverse libraries needed for the data manipulation:

library(tidyverse)

The data set is the Wage dataset from the {ISLR2} package. This package contains a variety of datasets used for statistical analysis in An Introduction to Statistical Learning book.

library(ISLR2)
data(Wage)
wage_h <- Wage%>%group_by(education)%>%summarize(avg_wage=mean(wage))
kableExtra::kable(wage_h,row.names = F)

Data Wrangling

A bit of data wrangling to group by education and calculate the mean value and the standard deviation of the wage.

Wage1 <- Wage %>%
  mutate(education=gsub("\\d. ","",education)) %>% #count(year)
  group_by(education)%>%
  mutate(mean=mean(wage),
         sd=sd(wage)) %>%
  ungroup() %>% # pull(mean)%>%summary
  select(education,mean,sd) %>%
  distinct()

Set some extrafonts:

library(extrafont)
# loadfonts()

For this visualization I used: family = “Chelsea Market”

And finally, to make the plot, use:

ggdist::stat_dots to make the dots ditribution
distributional::dist_normal to normalize the data

library(ggdist)
library(distributional)

Wage1 %>%
ggplot(aes(y=fct_reorder(education,mean),
             xdist = dist_normal(mean, sd),
             layout = "weave",
             fill = stat(x < 111.70))) + 
  stat_dots(position = "dodge", color = "grey70")+
  geom_vline(xintercept = 111.70, alpha = 0.25) +
  scale_x_continuous(breaks = c(20,60,90,112,140,180,220)) +
  tvthemes::scale_fill_hilda()+
  # add a title / subtitle and a caption ------
  labs(x="Wage values from 2003 to 2009",
       y="",color="Race",fill="wage < avg",
       title="Wage distribution vs education 2003-2009",
       subtitle="Normalized values",
       caption="#30DayChartChallenge 2022 #day9 - Distribution/Statistics - v2\nDataSource: {ISLR2} Wage dataset | DataViz: Federica Gazzelloni") +
  # set a customized theme -------
  tvthemes::theme_avatar() +
  theme(text = element_text(family="Chelsea Market"),
        legend.background = element_blank(),
        legend.box.background = element_blank(),
        legend.key = element_blank(),
        legend.key.width = unit(0.5,units="cm"),
        legend.direction = "horizontal",
        legend.position = c(0.8,0.1))

If you’d like to save it as .png you can do it with ggsave()

ggsave("day9_statistics_v2.png",
       dpi=320,
       width = 9,
       height = 6)

Resources:

An Introduction to Statistical Learning