Generalize a for-loop for use in a custom function

Question

Using the for-loop below I can create a list of all managers above a given employee (essentially a list of an employee's manager, her manager's manager, etc.)

library(dplyr)
library(tidyr)
library(purrr)

# Create test data 
ds <-
  tibble(
    emp_id = c("001", "002", "003", "004", "005"),
    mgr_id  = c("002", "004", "004", "005", NA)
  )

# Hardcoded for-loop example 
  mgr_ids_above <- vector("list", length = 5)
  id <- "001"

  for (i in seq_along(mgr_ids_above)) {
    mgr_ids_above[[i]] <- ds$mgr_id[ds$emp_id == id]

    id <- mgr_ids_above[[i]]
  }

  # drop NAs
  mgr_ids_above <- unlist(mgr_ids_above)
  mgr_ids_above <- mgr_ids_above[!is.na(mgr_ids_above)]

  # return to list format
  as.list(mgr_ids_above)

My hope is to apply this for-loop to the entire data frame and save the results in a list-column. I can successfully do this using pmap() to apply a hard-coded for-loop to my data frame, but when I try to write a generalized function, everything falls apart.

# Define custom function with hardcoded data and variable names
get_mgrs_above <- function(id, max_steps = 5){

  mgr_ids_above <- vector("list", length = max_steps)

  for (i in seq_along(mgr_ids_above)) {
    mgr_ids_above[[i]] <- ds$mgr_id[ds$emp_id == id]

    id <- mgr_ids_above[[i]]
  }

  # drop NAs
  mgr_ids_above <- unlist(mgr_ids_above)
  mgr_ids_above <- mgr_ids_above[!is.na(mgr_ids_above)]

  # return to list format
  as.list(mgr_ids_above)
}

# Apply custom function
ds_mgrs_above <-
  ds %>%
  mutate(
    ranks_above = pmap(
      list(id = emp_id),
      get_mgrs_above
    )
  )

The output of the above code is

A tibble: 5 x 3
emp_id mgr_id ranks_above
  <chr>  <chr>  <list>     
1 001    002    <list [3]> 
2 002    004    <list [2]> 
3 003    004    <list [2]> 
4 004    005    <list [1]> 
5 005    NA     <list [0]>

And the content of the ranks_above list column look like

ds_mgrs_above$ranks_above[[1]]

[[1]]
[1] "002"

[[2]]
[1] "004"

[[3]]
[1] "005"

My failing function with all data and variables supplied as arguments fails with the message, "Error in mutate_impl(.data, dots) : Evaluation error: Element 1 has length 2, not 1 or 5..":

get_mgrs_above <- function(
  data,
  id = emp_id,
  mgr_id = mgr_id,
  emp_id = emp_id,
  max_steps = 5){

  mgr_ids_above <- vector("list", length = max_steps)

  for (i in seq_along(mgr_ids_above)) {
    mgr_ids_above[[i]] <- data$mgr_id[data$emp_id == id]

    id <- mgr_ids_above[[i]]
  }

  # drop NAs
  mgr_ids_above <- unlist(mgr_ids_above)
  mgr_ids_above <- mgr_ids_above[!is.na(mgr_ids_above)]

  # return to list format
  as.list(mgr_ids_above)
}

ds %>%
  mutate(
    ranks_above = pmap(
      list(
        data = ds,
        id = emp_id,
        mgr_id = mgr_id,
        emp_id = emp_id,
        max_steps = 5
      ),
      get_mgrs_above
    )
  )

To avoid confusion, this is a post about how to write a generalizable function that will create a list column from two columns. This is one component of a larger data munging attempt on a data frame with ~15k employees.

@CalumYou, yes, I have updated with expected output examples. — Joe
– Joe, Commented Aug 22, 2018 at 23:23

johnson-shuffle · Accepted Answer · 2018-08-23 00:13:20Z

1

The way your loop was structured was confusing me so I changed it by using while. This allows just an application of map at the end.

get_mgrs_above <- function(id, data = NULL, max_steps = 5) {

  stopifnot(!is.null(data))

  mgr_ids_above <- list()

  mgr  <- id
  iter <- 0

  while (iter < max_steps & !is.na(mgr)) {

    mgr <- data$mgr_id[data$emp_id == mgr]

    if (!is.na(mgr)) {
      mgr_ids_above <- append(mgr_ids_above, mgr)
    }

    iter <- iter + 1

  }

  return(mgr_ids_above)

}

ds$ranks_above <- map(ds$emp_id, get_mgrs_above, data = ds)

answered Aug 23, 2018 at 0:13

johnson-shuffle

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Comments

Calum You · Accepted Answer · 2018-08-23 00:06:08Z

Here's my attempt at doing what I think you want. I couldn't figure out a way to adapt your code but I hope this approach makes sense. Basically you want to get the full command chain for each employee from a single table of employee ids and their direct manager's id. Here I make that table lookup and repeatedly join it onto an input dataframe that is basically just the employee ids, a command_chain list-col that I add each additional manager to, and a current_join column that stores the id to be looked up in each iteration.

Then we can simply wrap the join_once function in a join_all which will keep calling it until we've reached the end of all the command chains (there are only NAs). I cleaned up the output to discard the NAs and print the command chains as comma-separated strings so you can see what it's done.

To some extent I don't know if this is particularly efficient because you have to join a lot of variables that might not need it (e.g. here 004 is joined three more times than necessary) but it's at least conceptually simple I think.

library(tidyverse)
lookup <- tibble(
    emp_id = c("001", "002", "003", "004", "005"),
    mgr_id = c("002", "004", "004", "005", NA)
  )

input <- lookup %>%
  select(emp_id) %>%
  mutate(command_chain = emp_id, current_join = emp_id)

join_once <- function(df) {
  df %>%
    left_join(lookup, by = c("current_join" = "emp_id")) %>%
    mutate(
      command_chain = map2(command_chain, mgr_id, ~ c(.x, .y)),
      current_join = mgr_id
    ) %>%
    select(-mgr_id)
}

join_all <- function(df) {
  output <- df
  while (!all(is.na(output$current_join))) {
    output <- join_once(output)
  }
  return(output)
}

output <- join_all(input)
output %>%
  mutate(
    command_chain = map(command_chain, ~ discard(.x, is.na)),
    cc_as_string = map_chr(command_chain, ~ str_c(.x, collapse = ","))
    ) %>%
  select(-current_join)
#> # A tibble: 5 x 3
#>   emp_id command_chain cc_as_string   
#>   <chr>  <list>        <chr>          
#> 1 001    <chr [4]>     001,002,004,005
#> 2 002    <chr [3]>     002,004,005    
#> 3 003    <chr [3]>     003,004,005    
#> 4 004    <chr [2]>     004,005        
#> 5 005    <chr [1]>     005

Created on 2018-08-22 by the reprex package (v0.2.0).

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