I have a table like this:

I have the Grades as a factor where grades = DP, G, H

I need to create a table that gives me the mean salary by grade for full-time males (ftMale) and part-time males (ptMale) like so:

My dataset is a lot larger than this and there would be means for part time and full time in each grade. Any help would be much appreciated! Thanks

CodePudding user response：

Another dplyr approach:

df %>% 
  group_by(genderTime, Grade) %>% 
  summarise(Salary = mean(Salary, na.rm=TRUE)) %>% 
  ungroup() %>% 
  pivot_wider(names_from = genderTime, values_from = Salary, values_fill = 0) %>% 
  select("Grade", "ftMale", "ptMale")

# A tibble: 3 × 3
  Grade ftMale ptMale
  <chr>  <dbl>  <dbl>
1 H          0  37250
2 G      34000      0
3 DP         0  15500

Just another (slightly different) dplyr solution

df %>% 
  filter(genderTime %in% c("ftMale", "ptMale")) %>% 
  group_by(genderTime, Grade) %>% 
  summarise(Salary = mean(Salary, na.rm=TRUE)) %>% 
  pivot_wider(names_from = genderTime, values_from = Salary, values_fill = 0)

The same using full R base:

reshape(aggregate(Salary ~ genderTime   Grade, 
                  subset = genderTime %in% c("ftMale", "ptMale"), 
                  FUN=mean, 
                  data=df), 
        direction = "wide", 
        idvar = "Grade", 
        timevar = "genderTime")

CodePudding user response：

Group by Grade and then summarize your data by getting the mean of Salary while making sure that you are subsetting to the relevant ftMale/ptMale values.

library(tidyverse)

df |>
  group_by(Grade) |>
  summarize(full_time_male = mean(Salary[genderTime == "ftMale"]),
            part_time_male = mean(Salary[genderTime == "ptMale"]))