Get vector of column names depending on logicals of same dataframe

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I have a named dataframe containig logicals with missings and I want to get a vector with the column names where values are TRUE (going down the rows and, if multiple TRUEs in one row, going from left to right). Here an example:

df <- data.frame(a= c(FALSE, NA, TRUE, TRUE),
                 b= c(TRUE, FALSE, FALSE, NA),
                 c= c(TRUE, TRUE, NA, NA))
df
#       a     b    c
# 1 FALSE  TRUE TRUE
# 2    NA FALSE TRUE
# 3  TRUE FALSE   NA
# 4  TRUE    NA   NA

expected <- c("b", "c", "c", "a", "a")

Going from first to last row we see TRUE in the first row. Here are multiple TRUEs, thus we go from left to right and get "b" and "c". In second tow we get "c", and so on.

How to do this (in an elegant way)?

names dataframe vector r
5 Answers

You can do in base R:

pos <- which(t(df) == TRUE, arr.ind = TRUE)
names(df)[pos[, "row"]]
[1] "b" "c" "c" "a" "a"
user_63564300

You can also try using apply

unlist(apply(df, 1, function(x){na.omit(names(df)[x])}))

[1] "b" "c" "c" "a" "a"
user_167291750

Here is a tidyverse way:

library(dplyr)
library(tidyr)

vector <- df %>% 
  mutate(across(, ~case_when(.==TRUE ~ cur_column()), .names = 'new_{col}')) %>%
  unite(New_Col, starts_with('new'), na.rm = TRUE, sep = ', ') %>% 
  separate_rows(New_Col) %>% 
  pull(New_Col)

Or:

library(dplyr)
library(tidyr)

df %>% 
  mutate(across(, ~case_when(.==TRUE ~ cur_column()))) %>% 
  pivot_longer(everything()) %>% 
  na.omit() %>% 
  pull(value)

[1] "b" "c" "c" "a" "a"
user_133216470

Another possible solution, based on purrr::pmap:

library(tidyverse)

pmap(df, ~ names(df)[c(...)] %>% na.omit) %>% unlist

#> [1] "b" "c" "c" "a" "a"
user_115644870

You can use %%(modulo) to identify the column indices.

names(df)[(which(t(df)) - 1) %% ncol(df) + 1]

# [1] "b" "c" "c" "a" "a"
Benchmark
df <- as.data.frame(matrix(sample(c(TRUE, FALSE, NA), 1e7, TRUE), 1e5, 1e2))

# A data.frame: 100,000 × 100
#     V1    V2    V3    V4    V5 ...
# 1 TRUE  TRUE    NA FALSE FALSE ...
# 2   NA  TRUE  TRUE  TRUE    NA ...
# 3   NA FALSE FALSE FALSE  TRUE ...
# 4   NA FALSE FALSE  TRUE FALSE ...
# 5   NA FALSE FALSE FALSE  TRUE ...

library(microbenchmark)

bm <- microbenchmark(
  Darren = {
    x1 <- names(df)[(which(t(df)) - 1) %% ncol(df) + 1]
  }, Clemsang = {
    x2 <- names(df)[which(t(df) == TRUE, arr.ind = TRUE)[, "row"]]
  })

all(x1 == x2)
# [1] TRUE

bm
# Unit: milliseconds
#      expr      min       lq     mean   median       uq      max neval
#    Darren 140.5595 153.3333 163.7934 159.4783 167.5418 284.4146   100
#  Clemsang 219.7802 242.6169 254.9226 250.8673 264.0462 356.9299   100
user_100689850
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