Module 6: Apply 5

Import data

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

Apply the following dplyr verbs to your data

Filter rows

filter(jobs_gender, year == 1, total_workers == 1)

Arrange rows

arrange(jobs_gender, desc(year), desc(total_workers))

Select columns

select(jobs_gender, total_earnings)
select(jobs_gender, total_earnings, total_earnings_male)
select(jobs_gender, total_earnings, total_earnings_male, total_earnings_female)

Add columns

mutate(jobs_gender,
       gain = total_earnings) %>%
    
    # Select total earnings, total male earnings, and total female earnings
    select(total_earnings:total_earnings_male, total_earnings_female)

Summarize by groups

jobs_gender %>%
    
    # Group by total earnings
    group_by(total_earnings) %>%
    
    # Calculate average male earnings
    summarise(average = mean(total_earnings_male, na.rm = TRUE)) %>%
    
    # Sort it
    arrange(total_earnings)

jobs_gender %>%
    
    # Group by total earnings
    group_by(total_earnings) %>%
    
    # Calculate average male earnings
    summarise(average = mean(total_earnings_female, na.rm = TRUE)) %>%

  # Sort it
    arrange(total_earnings)