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?
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])}))