Create a new variable based on three variables in R-CodePudding

I want to create a new variable called N1 based on three existing variables (resp, exp.1, exp.2) in R.

df <- data.frame(
                 resp = c(1, 2, 4, 3, 5, 7 ),
                 exp.1 = c(0, 0.24, 1, 1.5, 0, 0.4),
                 exp.2 = c(1, 1, 0, 0, 0.3, 0.2)
)

df   resp  exp.1   exp.2
1   1      0        1
2   2      0.24     1
3   2      1        0
4   4      1.5      0
5   5      0        0.3
6   7      0.4      0.2

I want to make a new variable N1 like this: when resp >4, extracting values from exp.1 when resp <4, extracting values from exp.2 when resp == 4, making it missing values.

The desired outcome is:

df   resp  exp.1   exp.2   N1
1   1      0        1      1
2   2      0.24     1      1
3   4      1        0      NA
4   3      1.5      0      0
5   5      0        0.3    0
6   7      0.4      0.2    0.4

I tried my best using mutate() or car::recode() but it does not work. Any clues?

CodePudding user response：

Using case_when,

library(dplyr)

df %>%
  mutate(N1 = case_when(
    resp>4 ~ exp.1,
    resp<4 ~ exp.2,
    resp == 5 ~ NA_real_
  ))

  resp exp.1 exp.2  N1
1    1  0.00   1.0 1.0
2    2  0.24   1.0 1.0
3    4  1.00   0.0  NA
4    3  1.50   0.0 0.0
5    5  0.00   0.3 0.0
6    7  0.40   0.2 0.4

CodePudding user response：

Edit: Using case_when(), as given in the solution above, might be better.

library(dplyr)
# #Data
df <- data.frame(
    resp = c(1, 2, 4, 3, 5, 7 ),
    exp.1 = c(0, 0.24, 1, 1.5, 0, 0.4),
    exp.2 = c(1, 1, 0, 0, 0.3, 0.2)
)

df %>% 
  rowwise() %>% 
  mutate(N1 = if (resp >4) {
                exp.1
              } else if (resp <4) {
                exp.2
              } else if (resp ==4) {
                NA
              } else {
                NA
              }
        )
## A tibble: 6 x 4
## Rowwise: 
#   resp exp.1 exp.2    N1
#  <dbl> <dbl> <dbl> <dbl>
#1     1  0      1     1  
#2     2  0.24   1     1  
#3     4  1      0    NA  
#4     3  1.5    0     0  
#5     5  0      0.3   0  
#6     7  0.4    0.2   0.4