Combine mutate with conditional values

Question

In a large dataframe ("myfile") with four columns I have to add a fifth column with values conditonally based on the first four columns. Recently I have become a huge fan of dplyr, mainly because of its speed in large datasets. So I was wondering if I could deal with my problem using the mutate function.

My dataframe (actually a shorter version of it) looks a bit like this:

  V1 V2 V3 V4
1  1  2  3  5
2  2  4  4  1
3  1  4  1  1
4  4  5  1  3
5  5  5  5  4

The values of the fifth column (V5) are based on some conditional rules:

if (V1==1 & V2!=4){
V5 <- 1
}
else if (V2==4 & V3!=1){
V5 <- 2
}
else {
V5 <- 0
}

Now I want to use the mutate function to use these rules on all rows (so I don't have to use a slow loop). Something like this (and yes, I know it doesn't work this way!):

myfile <- mutate(myfile, if (V1==1 & V2!=4){V5 = 1}
    else if (V2==4 & V3!=1){V5 = 2}
    else {V5 = 0})

This should be the result:

  V1 V2 V3 V4 V5
1  1  2  3  5  1
2  2  4  4  1  2
3  1  4  1  1  0
4  4  5  1  3  0
5  5  5  5  4  0

How to do this in dplyr?

Answer 1

Try this:

myfile %>% mutate(V5 = (V1 == 1 & V2 != 4) + 2 * (V2 == 4 & V3 != 1))

giving:

  V1 V2 V3 V4 V5
1  1  2  3  5  1
2  2  4  4  1  2
3  1  4  1  1  0
4  4  5  1  3  0
5  5  5  5  4  0

or this:

myfile %>% mutate(V5 = ifelse(V1 == 1 & V2 != 4, 1, ifelse(V2 == 4 & V3 != 1, 2, 0)))

giving:

  V1 V2 V3 V4 V5
1  1  2  3  5  1
2  2  4  4  1  2
3  1  4  1  1  0
4  4  5  1  3  0
5  5  5  5  4  0

Suggest you get a better name for your data frame. myfile makes it seem as if it holds a file name.

Above used this input:

myfile <- 
structure(list(V1 = c(1L, 2L, 1L, 4L, 5L), V2 = c(2L, 4L, 4L, 
5L, 5L), V3 = c(3L, 4L, 1L, 1L, 5L), V4 = c(5L, 1L, 1L, 3L, 4L
)), .Names = c("V1", "V2", "V3", "V4"), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5"))

Update 1 Since originally posted dplyr has changed %.% to %>% so have modified answer accordingly.

Update 2 dplyr now has case_when which provides another solution:

myfile %>% 
       mutate(V5 = case_when(V1 == 1 & V2 != 4 ~ 1, 
                             V2 == 4 & V3 != 1 ~ 2,
                             TRUE ~ 0))

Answer 2

With dplyr 0.7.2, you can use the very useful case_when function :

x=read.table(
 text="V1 V2 V3 V4
 1  1  2  3  5
 2  2  4  4  1
 3  1  4  1  1
 4  4  5  1  3
 5  5  5  5  4")
x$V5 = case_when(x$V1==1 & x$V2!=4 ~ 1,
                 x$V2==4 & x$V3!=1 ~ 2,
                 TRUE ~ 0)

Expressed with dplyr::mutate, it gives:

x = x %>% mutate(
     V5 = case_when(
         V1==1 & V2!=4 ~ 1,
         V2==4 & V3!=1 ~ 2,
         TRUE ~ 0
     )
)

Please note that NA are not treated specially, as it can be misleading. The function will return NA only when no condition is matched. If you put a line with TRUE ~ ..., like I did in my example, the return value will then never be NA.

Therefore, you have to expressively tell case_when to put NA where it belongs by adding a statement like is.na(x$V1) | is.na(x$V3) ~ NA_integer_. Hint: the dplyr::coalesce() function can be really useful here sometimes!

Moreover, please note that NA alone will usually not work, you have to put special NA values : NA_integer_, NA_character_ or NA_real_.

Answer 3

It looks like derivedFactor from the mosaic package was designed for this. In this example, it would look something like:

library(mosaic)
myfile <- mutate(myfile, V5 = derivedFactor(
    "1" = (V1==1 & V2!=4),
    "2" = (V2==4 & V3!=1),
    .method = "first",
    .default = 0
    ))

(If you want the outcome to be numeric instead of a factor, wrap the derivedFactor with an as.numeric.)

Note that the .default option combined with .method = "first" sets the "else" condition -- this approach is described in the help file for derivedFactor.