Removing Duplicates while Maintaining values in (2) Other Columns

user_8272537 0
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I am trying to remove duplicates by req_id, but need to keep the specific req_id that have values of (1) in the Offer_accepted or Offer_rejected colulmns. I tried messing around with Tidy package, but couldn't quite figure out how to do this properly.

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Sample Data:

structure(list(req_id = c(335, 335, 335, 335, 336, 336, 336, 
337, 337, 337, 337), Offer_accepted = c(1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0), Offer_rejected = c(0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 
0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-11L))
tidyr r
2 Answers

Maybe we could it this way:

library(dplyr)

df %>% 
  filter(rowSums(df[,2:3])>=1)

  req_id Offer_accepted Offer_rejected
   <dbl>          <dbl>          <dbl>
1    335              1              0
2    336              0              1
3    337              0              1
user_133216470

We can use if_any in filter and add distinct

library(dplyr)
df1 %>% 
   filter(if_any(starts_with('Offer'),  ~.x == 1)) %>%
   distinct

-output

# A tibble: 3 × 3
  req_id Offer_accepted Offer_rejected
   <dbl>          <dbl>          <dbl>
1    335              1              0
2    336              0              1
3    337              0              1

if the intention is to keep the 'req_id' all rows having at least a 1 in either of the 'Offer' columns, do a group_by and filter (in this case, it returns the full data)

df1 %>%
  group_by(req_id) %>%
   filter(any(if_any(starts_with('Offer'),  ~.x == 1))) %>%
   ungroup
user_37322710
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