As a continuation of the #DuBoisChallenge, this week’s tidyTuesday presents employment by industry, sex, race, and occupation. There is also some scraped data from the self-service tool that generates weekly and hourly earnings data from the CPS. Let’s see what we have.
library(tidyverse) library(fpp3) library(magrittr) employed <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-23/employed.csv') earn <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-23/earn.csv') employed %<>% as_tsibble(index=year, key=c(industry,major_occupation,minor_occupation,race_gender))
Let me try to plot something.
employed %>% filter(race_gender=="TOTAL") %>% autoplot(employ_n) + guides(color=FALSE)
To be continued….