How to use purrr map with columns from a dataframe?

Question

I want to divide multiple columns of a dataframe by a column of that same dataframe (to get percentages). I would like to do it with purrr, to be able to iterate the operation for each column, and I tried to do it in the following way, but failed.

#create the tibble
competitor_a <- c(125,300,250)
competitor_b <- c(321,452,366)
competitor_c <- c(231,271,139)
data <- tibble(competitor_a,competitor_b,competitor_c)
data <- data %>% 
  mutate(total_sales=sum(competitor_a,competitor_b,competitor_c))

#iteration 

data %>% 
  map(select(competitor_a,competitor_b,competitor_c)/total_sales))

where data is my dataframe, competitor_a, competitor_b, competitor_c are columns with the sales of the competitors (dividend), and total_sales are my divider.

Answer 1

In this case I think it is better to use across :

library(dplyr)
data <- data %>% mutate(across(competitor_a:competitor_c, `/`, total_sales))

This can also be done using base R:

data[1:3] <- data[1:3]/data$total_sales

Answer 2

Your total_sales column is incorrect. Because you're summing across rows, you need to do the following (using dplyr ):

data <- data %>% 
  rowwise() %>%
  mutate(total_sales = sum(c_across(everything())))

As for the map, one way is to use an anonymous function:

data %>%
  map_dfr(~.x/data$total_sales*100) %>%
  select(-total_sales)
# A tibble: 3 x 3
  competitor_a competitor_b competitor_c
         <dbl>        <dbl>        <dbl>
1         9.23         23.7        17.1 
2        14.7          22.1        13.2 
3        16.6          24.2         9.21