Remove duplicate values across a few columns but keep rows - r

Question

I have a dataframe that looks like this:
dat <- data.frame(id=1:6,
z_1=c(100,290,38,129,0,290),
z_2=c(20,0,0,0,0,290),
z_3=c(0,0,38,0,0,98),
z_4=c(0,0,38,127,38,78),
z_5=c(23,0,25,0,0,98),
z_6=c(100,0,25,127,0,9))
dat
id z_1 z_2 z_3 z_4 z_5 z_6
1 1 100 20 0 0 23 100
2 2 290 0 0 0 0 0
3 3 38 0 38 38 25 25
4 4 129 0 0 127 0 127
5 5 0 0 0 38 0 0
6 6 290 290 98 78 98 9
I want to remove duplicate values of z_x across each row, replacing any duplicates with either a 0 or NA, but leaving the rows & columns intact (ie not dropping any). The 0s here do not count as duplicates, they are missing values. Duplicate values within a column are ok. My ideal output would look like this:
id z_1 z_2 z_3 z_4 z_5 z_6
1 1 100 20 0 0 23 0
2 2 290 0 0 0 0 0
3 3 38 0 0 0 25 0
4 4 129 0 0 127 0 0
5 5 0 0 0 38 0 0
6 6 290 0 98 78 0 9
I don't really care what order the values within the z_xs appear in, so it's fine if they get moved around. Is there an efficient way to do this, preferably in some tidyverse way? I know I can pivot longer and drop duplicate rows, but my dataset is very large and I'm looking for a way to do this without pivoting.

Answer 1

Base R way using apply :
cols <- grep('z_\\d+', names(dat))
dat[cols] <- t(apply(dat[cols], 1, function(x) replace(x, duplicated(x), 0)))
# id z_1 z_2 z_3 z_4 z_5 z_6
#1 1 100 20 0 0 23 0
#2 2 290 0 0 0 0 0
#3 3 38 0 0 0 25 0
#4 4 129 0 0 127 0 0
#5 5 0 0 0 38 0 0
#6 6 290 0 98 78 0 9
tidyverse way without reshaping can be done using pmap :
library(tidyverse)
dat %>%
mutate(result = pmap(select(., matches('z_\\d+')), ~{
x <- c(...)
replace(x, duplicated(x), 0)
})) %>%
select(id, result) %>%
unnest_wider(result)
Since tests performed by #thelatemail suggests reshaping is a better option than handling the data rowwise you might want to consider it.
dat %>%
pivot_longer(cols = matches('z_\\d+')) %>%
group_by(id) %>%
mutate(value = replace(value, duplicated(value), 0)) %>%
pivot_wider()

Answer 2

This solution isn't tidyverse, but hopefully is sufficiently simple.
The duplicated() function does what you want. You can use apply() function to feed duplicated() your data by row.
dat[t(apply(dat, MARGIN = 1, duplicated))] <- 0