Conditional subset data frame based on value in multiple columns

Question

I have the following dataset (example)

idnumber=c(12,12,13,14,14,15,16,17,18,18)
reg = c('FR','FR','DE','US','US','TZ','MK','GR','ES','ES')
code1=c('F56','G76','G56','T78','G78','G76','G64','T65','G79','G56')
code2=c('G56','I89','J83','S46','D78','G56','H89','G56','W34','T89')
df = data.frame(idnumber,reg,code1,code2)

which gives:

  idnumber reg code1 code2
1   12     FR   F56   G56
2   12     FR   G76   I89
3   13     DE   G56   J83
4   14     US   T78   S46
5   14     US   G78   D78
6   15     TZ   G76   G56
7   16     MK   G64   H89
8   17     GR   T65   G56
9   18     ES   G79   W34
10  18     ES   G56   T89

I would like to subset df keeping only the raws where the value G56 appears in column code1 or code 2 , though keeping the raw idnumber if the id value is the same id value matching with the value G56 such as:

  idnumber reg code1 code2
1   12     FR   F56   G56
2   12     FR   G76   I89
3   13     DE   G56   J83
6   15     TZ   G76   G56
8   17     GR   T65   G56
9   18     ES   G79   W34
10  18     ES   G56   T89

I have millions of observations and around 30 code columns. Hope the question is clear enough, any suggestion will be welcomed!

Cheers

Answer 1

Here's a way to do:

library(data.table)
setDT(df)
df[,.SD[any(code1 == 'G56' | code2 == 'G56')] ,.(idnumber)]

   idnumber reg code1 code2
1:       12  FR   F56   G56
2:       12  FR   G76   I89
3:       13  DE   G56   J83
4:       15  TZ   G76   G56
5:       17  GR   T65   G56
6:       18  ES   G79   W34
7:       18  ES   G56   T89

Answer 2

1. base

subset(df, idnumber %in% idnumber[code1=="G56" | code2=="G56"])

2. dplyr

library(dplyr)

df %>% filter(idnumber %in% idnumber[code1=="G56" | code2=="G56"])

Output

#   idnumber reg code1 code2
# 1       12  FR   F56   G56
# 2       12  FR   G76   I89
# 3       13  DE   G56   J83
# 4       15  TZ   G76   G56
# 5       17  GR   T65   G56
# 6       18  ES   G79   W34
# 7       18  ES   G56   T89

Answer 3

Another base R solution

subset(df,`class<-`(ave(cbind(as.character(code1),as.character(code2)),
                      idnumber,
                      FUN = function(v) ifelse("G56"%in%v,TRUE,FALSE)),"logical")[,1])

such that

   idnumber reg code1 code2
1        12  FR   F56   G56
2        12  FR   G76   I89
3        13  DE   G56   J83
6        15  TZ   G76   G56
8        17  GR   T65   G56
9        18  ES   G79   W34
10       18  ES   G56   T89

Answer 4

library(dplyr)
df %>%
  semi_join(df %>%
  filter(code1=="G56" | code2=="G56"),by="idnumber")

---

  idnumber reg code1 code2
1       12  FR   F56   G56
2       12  FR   G76   I89
3       13  DE   G56   J83
4       15  TZ   G76   G56
5       17  GR   T65   G56
6       18  ES   G79   W34
7       18  ES   G56   T89

Edit : This may be simpler with 30 code columns:

df %>%
  semi_join(df %>%
              pivot_longer(cols=-c(idnumber, reg)) %>%
              filter(value=="G56") %>%
              pivot_wider(id_cols=c(idnumber, reg)),
            by="idnumber")

Third choice:

df %>%
  semi_join(df %>%
              filter_at(vars(starts_with("code")), any_vars(. == "G56")),
            by="idnumber")

---

Edit : OP now wants to filter records if "G56" appears at least twice in the "code" columns (see comment below)

df %>%
  semi_join(df %>%
  mutate(n=rowSums(.[grep("code", names(.))] =="G56")) %>%
  group_by(idnumber) %>%
  filter(sum(n)>1),
  by="idnumber")

  idnumber reg code1 code2 code3
1       12  FR   F56   G56   M56
2       12  FR   G76   I89   G56
3       18  ES   G79   W34   W33
4       18  ES   G56   G56   T89

---

Data:

idnumber=c(12,12,13,14,14,15,16,17,18,18)
reg = c('FR','FR','DE','US','US','TZ','MK','GR','ES','ES')
code1=c('F56','G76','G56','T78','G78','G76','G64','T65','G79','G56')
code2=c('G56','I89','J83','S46','D78','G56','H89','G56','W34','G56')
code3=c('M56','G56','J83','S46','D78','G46','H89','J56','W33','T89')
df = data.frame(idnumber,reg,code1,code2,code3)