Test2<- Test %>%
dplyr::select(starts_with("Test"))%>%
mutate_all(function(x){x %in% c("DF60","DF61","DF62","DF63")})%>%
mutate(out = ifelse(rowSums(.)<1,0,1))

评论后调整

如果要保留其他列，则由yutannihilation提出的mutate_at要好得多.然后，问题就变成了在选择列时对rowums进行突变.不知道下一件事是否是***实践，但它能奏效(对我之前的问题进行了重新解答:

If you want to keep other columns, mutate_at, as is proposed by yutannihilation, is far better. The problem then becomes doing the rowsums in mutate on a selection of the columns. No idea if the next thing is best practice, but it works (reworked an answer on a previous question of mine: dplyr mutate on column subset (one function on all these columns combined))

library(tidyverse)
library(anomalyDetection)

Test1<-c("DF64", "DF63", "DF89", "DF30", "DF70")
Test2<-c("DF61", "DF25", "DF00", "DF30", "DF99")
Test3<-c("DF80", "DF63", "DF60", "DF63", "DF70")
Test<-data.frame(Test1, Test2, Test3)

Test$ExtraCol<-LETTERS[1:5]


Test2<- Test %>%
  mutate_at(vars(starts_with("Test")),funs(bin=.%in% c("DF60","DF61","DF62","DF63")))%>%
  split(.,1<10)%>%
  map_df(~mutate(.,out=rowSums(.[paste0("Test",1:3,"_bin")])>0))


  Test1 Test2 Test3 ExtraCol Test1_bin Test2_bin Test3_bin   out
   DF64  DF61  DF80        A     FALSE      TRUE     FALSE  TRUE
   DF63  DF25  DF63        B      TRUE     FALSE      TRUE  TRUE
   DF89  DF00  DF60        C     FALSE     FALSE      TRUE  TRUE
   DF30  DF30  DF63        D     FALSE     FALSE      TRUE  TRUE
   DF70  DF99  DF70        E     FALSE     FALSE     FALSE FALSE