CODEWITHSUNDEEP
r
Electrino
Electrino

Reputation: 2890

Covert dummy variables to single categorical in R?

Similar questions have been asked here, here, and here. However, they don't seem to cover exactly what I need. For example, if I have a dataset like so:

df <- data.frame(
  x = rnorm(10),
  y = rnorm(10),
  a = c(0,0,0,1,1,0,0,0,1,0),
  b = c(1,1,1,1,0,0,1,0,0,0),
  c = c(0,1,0,1,0,0,0,0,0,0),
  z = c(1,1,1,1,1,0,1,0,1,0)
)

What I'm trying to do is convert the variables a, b, and c to a single categorical where the levels are a, b, and c. But as you can see, sometimes 2 variables occur in the same row. So, what I'm trying to achieve is a data frame that would look something like this:

df <- data.frame(
  x = rnorm(10),
  y = rnorm(10),
  a = c(0,0,0,1,1,0,0,0,1,0),
  b = c(1,1,1,1,0,0,1,0,0,0),
  c = c(0,1,0,1,0,0,0,0,0,0),
  z = c(“b”,“b,c”,“b”,“a,b,c”,“a”,0,“b”,0,“a”,0)
)

I tried using :

apply(df[,c("a","b", "c")], 1, sum, na.rm=TRUE)

which sums the amount of each variable... but I'm not sure how to combine 2 (or more) variables into a single factor level!?

Any suggestions as to how I could do this?

Upvotes: 5

Views: 212

Answers (2)

SAL
SAL

Reputation: 2140

Here is another solution using pmap_chr similar to what @akrun showed above but using across() and then replacing NULL cells with 0 values:

library(dplyr);library(purrr)

df |>
dplyr::mutate(z=pmap_chr(across(a:c), ~ paste(names(c(...)[c(...) > 0]), collapse = ","))) |>
mutate(across(z, ~ replace(.x, .x == '', "0")))

output:

            x           y a b c     z
1  -0.3720247  1.09624218 0 1 0     b
2  -1.3545475  0.06103844 0 1 1   b,c
3   0.6472896 -1.15717339 0 1 0     b
4   0.2699036  0.82303370 1 1 1 a,b,c
5  -0.8318826  0.27290774 1 0 0     a
6  -0.7483059  0.79102464 0 0 0     0
7   1.1854403 -0.31954540 0 1 0     b
8   0.1317170 -0.52332482 0 0 0     0
9  -1.4327706 -0.45194686 1 0 0     a
10  0.3727059  1.85332187 0 0 0     0

Upvotes: 1

akrun
akrun

Reputation: 886938

Loop over the selected columns by row (MARGIN = 1), subset the column names where the value is 1 and paste them together

df$z <-  apply(df[c('a', 'b', 'c')], 1, function(x) toString(names(x)[x ==1]))
df$z
#[1] "b"       "b, c"    "b"       "a, b, c" "a"       ""        "b"       ""        "a"       ""

If we want to change the "" to '0'

df$z[df$z == ''] <- '0'

For a solution with purrr and dplyr:

df %>% mutate(z = pmap_chr(select(., a, b, c), ~  {v1 <- c(...); toString(names(v1)[v1 == 1])}))

Upvotes: 6

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