R - dplyr - code to run a number of very similar queries...? - r

Question

Consider the following:
df <- data.frame(
Name = c("Alan", "Bob", "Christine", "David", "Erica"),
Gender = c("M", "M", "F", "M", "F"),
Star_Sign = c("Aquarius", "Capricorn", "Aquarius", "Libra", "Leo"),
City = c("London", "Paris", "Berlin", "London", "Paris"),
Blood_Group = c("A", "AB", "B", "O", "A"),
Hours_Worked = c(2000, 1600, 0, 100, 200),
Salary = c(100000, 20000, 0, 500, 4000)
)
Name_Summary <- df %>% group_by(Name) %>% summarise(Hours_Worked = sum(Hours_Worked), Average_Salary = mean(Salary))
Gender_Summary <- df %>% group_by(Gender) %>% summarise(Hours_Worked = sum(Hours_Worked), Average_Salary = mean(Salary))
Star_Sign_Summary <- df %>% group_by(Star_Sign) %>% summarise(Hours_Worked = sum(Hours_Worked), Average_Salary = mean(Salary))
City_Summary <- df %>% group_by(City) %>% summarise(Hours_Worked = sum(Hours_Worked), Average_Salary = mean(Salary))
Blood_Group_Summary <- df %>% group_by(Blood_Group) %>% summarise(Hours_Worked = sum(Hours_Worked), Average_Salary = mean(Salary))
Obviously this works fine for a small number of fields. If, however, I've got 100 different fields (say) to do this for, it becomes very unwieldy.
I'd like to think that there is a way to loop through the list of fields and produce these summaries for each field, using some code to generate (and name the summaries), but I don't think I know how to do this. Can anyone help please?
Thanks
Alan

Answer 1

If you have a list of the columns you want to group by as a character vector:
vars_to_group_by <- names(df)[1:5]
You could iterate over them (I'm using purrr::map() but you could use lapply() or a loop), and use this rlang pattern to convert strings >> symbols >> properly evaluated variables.
library(tidyverse)
map(vars_to_group_by, sym) %>%
map(~ df %>%
group_by(!!.x) %>%
summarise(avg_salary = mean(Salary),
avg_hours = mean(Hours_Worked),
avg_hourly_wage = avg_salary / avg_hours))
You get an unnamed list back, because the vector going in was unnamed.
[[1]]
# A tibble: 5 x 4
Name avg_salary avg_hours avg_hourly_wage
<fct> <dbl> <dbl> <dbl>
1 Alan 100000 2000 50
2 Bob 20000 1600 12.5
3 Christine 0 0 NaN
4 David 500 100 5
5 Erica 4000 200 20
[[2]]
# A tibble: 2 x 4
Gender avg_salary avg_hours avg_hourly_wage
<fct> <dbl> <dbl> <dbl>
1 F 2000 100 20
2 M 40167. 1233. 32.6
[[3]]
# A tibble: 4 x 4
Star_Sign avg_salary avg_hours avg_hourly_wage
<fct> <dbl> <dbl> <dbl>
1 Aquarius 50000 1000 50
2 Capricorn 20000 1600 12.5
3 Leo 4000 200 20
4 Libra 500 100 5
[[4]]
# A tibble: 3 x 4
City avg_salary avg_hours avg_hourly_wage
<fct> <dbl> <dbl> <dbl>
1 Berlin 0 0 NaN
2 London 50250 1050 47.9
3 Paris 12000 900 13.3
[[5]]
# A tibble: 4 x 4
Blood_Group avg_salary avg_hours avg_hourly_wage
<fct> <dbl> <dbl> <dbl>
1 A 52000 1100 47.3
2 AB 20000 1600 12.5
3 B 0 0 NaN
4 O 500 100 5
You could add names based on vars_to_group_by either before or after the map() calls.

Answer 2

We could use the group_by_at which can take a string as input
library(purrr)
library(dplyr)
map(names(df)[-6], ~ df %>%
group_by_at(.x) %>%
summarise(avg_salary = mean(Salary)))
#[[1]]
# A tibble: 5 x 2
# Name avg_salary
# <fct> <dbl>
#1 Alan 100000
#2 Bob 20000
#3 Christine 0
#4 David 500
#5 Erica 4000
#[[2]]
# A tibble: 2 x 2
# Gender avg_salary
# <fct> <dbl>
#1 F 2000
#2 M 40167.
#[[3]]
# A tibble: 4 x 2
# Star_Sign avg_salary
# <fct> <dbl>
#1 Aquarius 50000
#2 Capricorn 20000
#3 Leo 4000
#4 Libra 500
#[[4]]
# A tibble: 3 x 2
# City avg_salary
# <fct> <dbl>
#1 Berlin 0
#2 London 50250
#3 Paris 12000
#[[5]]
# A tibble: 4 x 2
# Blood_Group avg_salary
# <fct> <dbl>
#1 A 52000
#2 AB 20000
#3 B 0
#4 O 500