dplyr mutate with conditional values
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
Note
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))
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_
.