Module 10: Apply it to your data 9

Import your data

# csv file
jobs_gender <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-05/jobs_gender.csv")

Chapter 14

Tools

Detect matches

jobs_gender$occupation

str_detect(jobs_gender$occupation, "Computer programmers")
sum(str_detect(jobs_gender$occupation, "Computer programmers"))

jobs_gender %>%
    summarise(num_Computerprogrammers = sum(str_detect(occupation, "Computer programmers")))

Extract matches

jobs_gender %>%
    mutate(col_computerprogrammers = str_extract(occupation, "Computer Programmers")) %>% 
    select(occupation, col_computerprogrammers) %>%
    filter(!is.na(col_computerprogrammers))

Replacing matches

jobs_gender %>%
   mutate(col_ComputerProgramming = str_replace(occupation, "Computer Programmers", "Computer Programming")) %>%
    select(occupation, col_ComputerProgramming)