Employed Gender

Code for Full-Time Workers

# load in packages
library(tidyverse)
library(readr)
library(dplyr)
# read in csv file
employed_gender <- read.csv("employed_gender.csv")
# plot line plots depicting the difference in full-time workers (by gender, by year)
ggplot(employed_gender) +
  geom_line(aes(x = year, y = full_time_female), color = "hotpink") +
  geom_line(aes(x = year, y = full_time_male), color = "blue") +
  labs(
    x = "Year",
    y = "% of Full-Time Workers",
    title = "Layered Line Plot of Male vs Female Full-Time Workers"
  )

Code for Part-Time Workers

ggplot(employed_gender) +
  geom_line(aes(x = year, y = part_time_female), color = "hotpink") +
  geom_line(aes(x = year, y = part_time_male), color = "blue") +
  labs(
    x = "Year",
    y = "% of Part-Time Workers",
    title = "Layered Line Plot of Male vs Female Part-Time Workers"
  )

Analysis

The most interesting aspect of these two graphs is the inversion which occurs for the ratio of full-time to part-time work by sex. This is most likely due to the historical struggle for self-identified women in earning and maintaining full-time positions. If I were to continue with this project, I would want to explore the relationship between maternity leave/motherhood and this domination of part-time work for women.

Citation

I retrieved the original csv file from a Tidy Tuesday data set. The raw data originates from the Bureau of Labor Statistics and the Census Bureau about women in the workforce. Jon Harmon, an advanced R programming consultant, then manipulated and divided the data, motivated by March being Women’s History month.