Ifelse statement for specific group

TvCasteren 0
Viewed 37

My DF has two dummy columns:

Invested ProgramParticipant
1 0
0 0
1 1
1 0
0 1
0 0

The goal is to create a third dummy column, which only considers the rows that did not invest, and assigns a 1 if they participated in the program. If the row did not invest, a NA should be assigned. Ideally, it would look like this:

Invested ProgramParticipant Invested&Participated
1 0 0
0 0 NA
1 1 1
1 0 0
0 1 NA
0 0 NA

I tried working with standard ifelse statements, or with DPLYR's casewhen, but I cannot seem to fix the NA assignments.

dplyr dataframe r
5 Answers

Does this work:

library(dplyr)

df %>% mutate(InvestednParticipated = if_else(Invested == 1 & ProgramParticipant == 1, 1,
                                              if_else(Invested == 0, NA_real_, 0)))

  Invested ProgramParticipant InvestednParticipated
1        1                  0                     0
2        0                  0                    NA
3        1                  1                     1
4        1                  0                     0
5        0                  1                    NA
6        0                  0                    NA
Karthik S0

If I understand you correctly, you want this:

ifelse(Invested == 1 & ProgramParticipant == 1, 1,
       ifelse(Invested == 1 & ProgramParticipant == 0, 0, NA))
Arduan0

You should take a look at case_when to avoid the confusion of nested ifelses:

library(dplyr)
df %>% 
  mutate(InvestedParticipated = 
           case_when(Invested == 1 & ProgramParticipant == 1 ~ 1,
                     Invested == 1 & ProgramParticipant == 0 ~ 0,
                     Invested == 0 ~ NA_real_))

output

  Invested ProgramParticipant InvestedParticipated
1        1                  0                    0
2        0                  0                   NA
3        1                  1                    1
4        1                  0                    0
5        0                  1                   NA
6        0                  0                   NA

case_when sets to NA cases that are not matched, so you could even do:

df %>% 
  mutate(InvestedParticipated = 
           case_when(Invested == 1 & ProgramParticipant == 1 ~ 1,
                     Invested == 1 & ProgramParticipant == 0 ~ 0))
Maël0

There is no need to use a nested ifelse(). A single ifelse() is enough.

library(dplyr)

df %>%
  mutate(res = ifelse(Invested == 0, NA, Invested + ProgramParticipant - 1))

#   Invested ProgramParticipant res
# 1        1                  0   0
# 2        0                  0  NA
# 3        1                  1   1
# 4        1                  0   0
# 5        0                  1  NA
# 6        0                  0  NA
Data
df <- structure(list(Invested = c(1L, 0L, 1L, 1L, 0L, 0L), ProgramParticipant = c(0L, 
0L, 1L, 0L, 1L, 0L)), class = "data.frame", row.names = c(NA, -6L))
Darren Tsai0

Using simple indexing:

DF$InvestedParticipated <- with(DF, c(NA, 0, 1)[Invested*(ProgramParticipant + 1) + 1])
jblood940
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