👀 Views: 0 💬 Answers: 1 📅 Created: 2025-06-12

r dplyr purrr R

I just started working with I'm testing a new approach and I'm trying to apply a function across multiple columns of a data frame using `purrr::map`. However, I'm working with unexpected results where the output data types don't match what I anticipated. Specifically, I'm attempting to convert character columns to factors for a data frame containing mixed data types. Here’s the code I’m currently using: ```r library(dplyr) library(purrr) # Sample data frame df <- data.frame( id = 1:3, category = c('A', 'B', 'A'), value = c('100', '200', '300'), stringsAsFactors = FALSE ) # Function to convert character columns to factors convert_to_factor <- function(x) { if (is.character(x)) { return(factor(x)) } return(x) } # Applying the function using purrr::map result <- df %>% mutate(across(everything(), ~map(., convert_to_factor))) ``` However, this results in an behavior: `behavior in UseMethod("map") : no applicable method for 'map' applied to an object of class 'character'`. I’ve tried to troubleshoot by checking the class of each column before applying the function and using `map_df`, but the data types still end up being incorrect after the operation. I expected the values in the `category` and `value` columns to be converted to factors, but instead, I get a nested list structure. I've also attempted using `mutate_if` as an alternative: ```r result <- df %>% mutate_if(is.character, factor) ``` This approach does work but I'm specifically looking to understand why my use of `map` did not yield the desired outcome. Any insights on how to properly use `map` for this case or how to handle the unexpected data type issues? Any help would be greatly appreciated! Any help would be greatly appreciated! I recently upgraded to R 3.10. Has anyone dealt with something similar?