I have a column - "issue_slip" in R dataframe - "vouchers" with values/rows such as

Issue slip: IS/001, IS/001, IS/001, IS/002, IS/002, IS/002

and another column "rec_status" with values 0 or 1. Each issue_slip row can have rec_status 0 or 1. I would like to keep only those issue_slips that have all rec_status as 0 OR 0 or 1 --> remove issue_slip rows that have all rec_status as 1.

For example,

1. IS/001 - 1,
2. IS/001 - 0 ,
3. IS/001 - 1

should show up and not get filtered out because at least one row has rec_status = 1

I tried using the filter and subset functions but could not figure out how to go about filtering this in the same column

Sample data

quux <- data.frame(issue_slip = c("IS/001", "IS/001", "IS/001", "IS/002", "IS/002", "IS/002"), rec_status = c(0, 0, 1, 1, 1, 1))
quux
#   issue_slip rec_status
# 1     IS/001          0
# 2     IS/001          0
# 3     IS/001          1
# 4     IS/002          1
# 5     IS/002          1
# 6     IS/002          1

base R

ind <- ave(quux$rec_status, quux$issue_slip, FUN = function(z) any(z %in% 0)) > 0
ind
# [1]  TRUE  TRUE  TRUE FALSE FALSE FALSE
quux[ind,]
#   issue_slip rec_status
# 1     IS/001          0
# 2     IS/001          0
# 3     IS/001          1

dplyr

library(dplyr)
quux %>%
  group_by(issue_slip) %>%
  filter(any(rec_status %in% 0)) %>%
  ungroup()
# # A tibble: 3 × 2
#   issue_slip rec_status
#   <chr>           <dbl>
# 1 IS/001              0
# 2 IS/001              0
# 3 IS/001              1

data.table

library(data.table)
as.data.table(quux)[, .SD[any(rec_status %in% 0),], by = issue_slip]
#    issue_slip rec_status
#        <char>      <num>
# 1:     IS/001          0
# 2:     IS/001          0
# 3:     IS/001          1

Note, I'm using rec_status %in% 0 instead of rec_status == 0 for a reason: since we have no sample data (and often even when we do), I have no assurance that there are not any NAs in the data; note that NA == 0 will return NA itself and therefore often fail non-defensive code, but NA %in% 0 returns false, which is often what we need (and I'm inferring it's what we want here).