CODEWITHSUNDEEP
rdplyrdata-analysistidyverseforcats
user5068121
user5068121

Reputation:

R: convert to factor with order of levels same with case_when

When doing data analysis, I sometimes need to recode values to factors in order to carry out groups analysis. I want to keep the order of factor same as the order of conversion specified in case_when. In this case, the order should be "Excellent" "Good" "Fail". How can I achieve this without tediously mention it again as in levels=c('Excellent', 'Good', 'Fail')?

Thank you very much.


library(dplyr, warn.conflicts = FALSE)             
                                                   
set.seed(1234)                                     
score <- runif(100, min = 0, max = 100)     
   
Performance <- function(x) {                       
  case_when(                                         
    is.na(x) ~ NA_character_,                          
    x > 80   ~ 'Excellent',                            
    x > 50   ~ 'Good',                                 
    TRUE     ~ 'Fail'                                  
  ) %>% factor(levels=c('Excellent', 'Good', 'Fail'))
}                                                  
                                                   
performance <- Performance(score)                  
levels(performance)                                
#> [1] "Excellent" "Good"      "Fail"
table(performance)                                 
#> performance
#> Excellent      Good      Fail 
#>        15        30        55

Upvotes: 24

Views: 10615

Answers (5)

its.me.adam
its.me.adam

Reputation: 706

Let case_when() output numbers and use the labels argument in factor():

library(dplyr, warn.conflicts = FALSE)
set.seed(1234)
score <- runif(100, min = 0, max = 100)

Performance <- function(x) {
  case_when(
    is.na(x) ~ NA_real_,
    x > 80   ~ 1,
    x > 50   ~ 2,
    TRUE     ~ 3
  ) %>% factor(labels=c('Excellent', 'Good', 'Fail'))
}

performance <- Performance(score)
levels(performance)
#> [1] "Excellent" "Good"      "Fail"
table(performance)
#> performance
#> Excellent      Good      Fail 
#>        15        30        55

Created on 2023-01-13 with reprex v2.0.2

Upvotes: 2

user5068121
user5068121

Reputation:

My Solution

Finally, I came up with a solution. For those who are interested, here is my solution. I wrote a function fct_case_when (pretend being a function in forcats). It is just a wrapper of case_when with factor output. The order of levels is the same as the argument order.


fct_case_when <- function(...) {
  args <- as.list(match.call())
  levels <- sapply(args[-1], function(f) f[[3]])  # extract RHS of formula
  levels <- levels[!is.na(levels)]
  factor(dplyr::case_when(...), levels=levels)
}

Now, I can use fct_case_when in place of case_when, and the result will be the same as the previous implementation (but less tedious).


Performance <- function(x) {                       
  fct_case_when(                                         
    is.na(x) ~ NA_character_,                          
    x > 80   ~ 'Excellent',                            
    x > 50   ~ 'Good',                                 
    TRUE     ~ 'Fail'                                  
  )
}      
performance <- Performance(score)                  
levels(performance)                       
#> [1] "Excellent" "Good"      "Fail"
table(performance)                
#> performance
#> Excellent      Good      Fail 
#>        15        30        55

Upvotes: 13

snakeoilsales
snakeoilsales

Reputation: 113

This is an implementation I have been using:

library(dplyr)
library(purrr)
library(rlang)
library(forcats)

factored_case_when <- function(...) {
  args <- list2(...)
  rhs <- map(args, f_rhs)
  
  cases <- case_when(
    !!!args
  )
  
  exec(fct_relevel, cases, !!!rhs)
}


numbers <- c(2, 7, 4, 3, 8, 9, 3, 5, 2, 7, 5, 4, 1, 9, 8)

factored_case_when(
  numbers <= 2 ~ "Very small",
  numbers <= 3 ~ "Small",
  numbers <= 6 ~ "Medium",
  numbers <= 8 ~ "Large",
  TRUE    ~ "Huge!"
)
#>  [1] Very small Large      Medium     Small      Large      Huge!     
#>  [7] Small      Medium     Very small Large      Medium     Medium    
#> [13] Very small Huge!      Large     
#> Levels: Very small Small Medium Large Huge!

This has the advantage of not having to manually spoecify the factor levels.

I have also submitted a feature request to dplyr for this functionality: https://github.com/tidyverse/dplyr/issues/6029

Upvotes: 1

Luke Hayden
Luke Hayden

Reputation: 712

While my solution replaces your piping with a messy intermediate variable, this works:

    library(dplyr, warn.conflicts = FALSE)             

set.seed(1234)                                     
score <- runif(100, min = 0, max = 100)     

Performance <- function(x) {                       
  t <- case_when(                                         
    is.na(x) ~ NA_character_,                          
    x > 80   ~ 'Excellent',                            
    x > 50   ~ 'Good',                                 
    TRUE     ~ 'Fail'                                  
  ) 
  to <- subset(t, !duplicated(t))
  factor(t, levels=(to[order(subset(x, !duplicated(t)), decreasing=T)] ))
}                                                  
performance <- Performance(score)                
levels(performance)

Edited to fix!

Upvotes: 1

De Novo
De Novo

Reputation: 7630

levels are set in lexicographic order by default. If you don't want to specify them, you can set them up so that lexicographic order is correct (Performance1), or create a levels vector once, and use it when generating the factor and when setting the levels (Performance2). I don't know how much effort or tediousness either of these would save you, but here they are. Take a look at my 3rd recommendation for what I think would be the least tedious way. 

Performance1 <- function(x) {                       
  case_when(
    is.na(x) ~ NA_character_,                          
    x > 80 ~ 'Excellent',  
    x <= 50 ~ 'Fail',
    TRUE ~ 'Good',
  ) %>% factor()
}

Performance2 <- function(x, levels = c("Excellent", "Good", "Fail")){
  case_when(
    is.na(x) ~ NA_character_,
    x > 80 ~ levels[1],
    x > 50 ~ levels[2],
    TRUE ~ levels[3]
  ) %>% factor(levels)
}
performance1 <- Performance1(score)
levels(performance1)
# [1] "Excellent" "Fail"     "Good"
table(performance1)
# performance1
# Excellent      Fail      Good 
#        15        55        30 

performance2 <- Performance2(score)
levels(performance2)
# [1] "Excellent" "Good"      "Fail"  
table(performance2)
# performance2
# Excellent      Good      Fail 
#        15        30        55

If I could suggest an even less tedious way:

performance <- cut(score, breaks = c(0, 50, 80, 100), 
                   labels = c("Fail", "Good", "Excellent"))
levels(performance)
# [1] "Fail"      "Good"      "Excellent"
table(performance)
# performance
#      Fail      Good Excellent 
#        55        30        15

Upvotes: 4

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