pmap_df inside mutate applying to entire dataframe not just row-CodePudding

I have a dataframe where each row includes arguments that I want to pass into a function iteratively. The function itself returns a dataframe with a few rows. I would like to keep the arguments and results together in one dataframe by applying pmap_df like you can with pmap_dbl inside of a mutate to add a new column with the results from the function. With the code below, I am able to get a column with nested data in it, but every row contains the data for all of the results, not just the ones corresponding to that row.

library(tidyr)

example_function <- function(data, string, ...){
  
  word_one <- paste(data$word_one, string)
  word_two <- paste(data$word_two, string)
  
  output <- data_frame(result_words = c(word_one, word_two))
  
}

fake_data <- tibble(group_id = rep(c(1, 2), each = 3),
                    word_one = c("hello", "goodbye", "today",
                                 "apple", "banana", "coconut"),
                    word_two = c("my", "name", "is",
                                 "ellie", "good", "morning"))

test <- fake_data %>% 
        group_by(group_id) %>% 
        nest() %>% 
        mutate(string = "not working") %>% 
        mutate(final_output = list(purrr::pmap_df(.l = ., .f = example_function)))

The output looks like:

Rows: 2
Columns: 4
Groups: group_id [2]
$ group_id     <dbl> 1, 2
$ data         <list> [<tbl_df[3 x 2]>], [<tbl_df[3 …
$ string       <chr> "not working", "not working"
$ final_output <list> [<tbl_df[12 x 1]>], [<tbl_df[…

What I would like to have would be for each of the final outputs to have only 6 rows in each dataframe, corresponding to the inputs from the nested data column. Is this possible?

CodePudding user response：

With the OP's function, it may be easily done without any pmap (return the output from the function)

example_function <- function(data, string, ...){
  
  word_one <- paste(data$word_one, string)
  word_two <- paste(data$word_two, string)
  
  output <- data_frame(result_words = c(word_one, word_two))
  output
  
}

As it is a nest_by, directly apply the function

library(dplyr)
fake_data %>% 
         nest_by(group_id) %>%
    mutate(string = "not working") %>%
    mutate(final_output = list(example_function(data, string)))
# A tibble: 2 × 4
# Rowwise:  group_id
  group_id               data string      final_output    
     <dbl> <list<tibble[,2]>> <chr>       <list>          
1        1            [3 × 2] not working <tibble [6 × 1]>
2        2            [3 × 2] not working <tibble [6 × 1]>

With pmap, extract the contents as a list to an object 'x1' then apply the OP's function on the list elements i.e. x1$data and x1$string

library(purrr)
library(stringr)
fake_data %>% 
         nest_by(group_id) %>%
    mutate(string = "not working") %>%
    ungroup %>%
    mutate(final_output = pmap(across(-group_id), 
       ~ {
      x1 <- list(...)
       example_function(x1$data, x1$string)
 }))
# A tibble: 2 × 4
  group_id               data string      final_output    
     <dbl> <list<tibble[,2]>> <chr>       <list>          
1        1            [3 × 2] not working <tibble [6 × 1]>
2        2            [3 × 2] not working <tibble [6 × 1]>