mutate two or more columns if case_when is used
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1
I am trying to use the case_when function for a bunch of columns y a data.frame.
This case does not return the specified columns in mutate
cars %>% mutate (
km = speed * dist,
mt = km / 1000
) %>%
mutate (
.funs = case_when(
(speed < 20 ) ~ {
km = km * 2
mt = mt * 3
}
)
)
Thanks
r dplyr
asked Nov 16 '18 at 19:46
Captain TylerCaptain Tyler
12213
add a comment |
1
I am trying to use the case_when function for a bunch of columns y a data.frame.
This case does not return the specified columns in mutate
cars %>% mutate (
km = speed * dist,
mt = km / 1000
) %>%
mutate (
.funs = case_when(
(speed < 20 ) ~ {
km = km * 2
mt = mt * 3
}
)
)
Thanks
r dplyr
asked Nov 16 '18 at 19:46
Captain TylerCaptain Tyler
12213
add a comment |
1
1
1
I am trying to use the case_when function for a bunch of columns y a data.frame.
This case does not return the specified columns in mutate
cars %>% mutate (
km = speed * dist,
mt = km / 1000
) %>%
mutate (
.funs = case_when(
(speed < 20 ) ~ {
km = km * 2
mt = mt * 3
}
)
)
Thanks
r dplyr
asked Nov 16 '18 at 19:46
Captain TylerCaptain Tyler
12213
I am trying to use the case_when function for a bunch of columns y a data.frame.
This case does not return the specified columns in mutate
cars %>% mutate (
km = speed * dist,
mt = km / 1000
) %>%
mutate (
.funs = case_when(
(speed < 20 ) ~ {
km = km * 2
mt = mt * 3
}
)
)
Thanks
r dplyr
r dplyr
asked Nov 16 '18 at 19:46
Captain TylerCaptain Tyler
12213
asked Nov 16 '18 at 19:46
Captain TylerCaptain Tyler
12213
edited Nov 16 '18 at 19:57
Captain Tyler
asked Nov 16 '18 at 19:46
Captain TylerCaptain Tyler
12213
asked Nov 16 '18 at 19:46
Captain TylerCaptain Tyler
12213
asked Nov 16 '18 at 19:46
Captain TylerCaptain Tyler
12213
12213
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
2
We could use mutate_at
library(tidyverse)
cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
mutate_at(vars(km, mt), funs(case_when(speed < 20 ~ .*2,
TRUE ~ .)))
If we need to do computation with separate values for each of the column, then use map2 or pmap
out <- cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
select(km, mt) %>%
map2_df(., list(2, 3), ~
case_when(cars$speed < 20 ~ .x * .y, TRUE ~ .x)) %>%
bind_cols(cars, .)
head(out)
# speed dist km mt
#1 4 2 16 0.024
#2 4 10 80 0.120
#3 7 4 56 0.084
#4 7 22 308 0.462
#5 8 16 256 0.384
#6 9 10 180 0.270
answered Nov 16 '18 at 19:49
akrunakrun
422k13209285
I ve edit the example. The mutated vars are nowkm = km * 2andmt = mt * 3
– Captain Tyler
Nov 16 '18 at 19:59
@CaptainTyler Is there any other conditions?
– akrun
Nov 16 '18 at 20:13
The objetive is mutate more than a column (existing or new) based in some row conditions
– Captain Tyler
Nov 16 '18 at 20:15
@CaptainTyler Thanks, Updated the solution
– akrun
Nov 16 '18 at 20:27
add a comment |
1
Discover this solution, but is a bit weird and tricky
mutate_when <- function (data, ...) {
dots <- eval (substitute (alist(...)))
for (i in seq (1, length (dots), by = 3)) {
condition <- eval (dots [[i]], envir = data)
mutations <- eval (dots [[i + 1]], envir = data [condition, ])
data[condition, names(mutations)] <- mutations
mutations_else <- eval (dots [[i + 2]], envir = data [!condition, ])
data[!condition, names(mutations)] <- mutations_else
}
data
}
cars %>%
mutate(
km = speed * dist,
mt = km/1000
) %>%
mutate_when(
speed < 20,
list (
km = km * 2,
mt = mt * 3
),
list (
0
)
)
Gives
speed dist km mt
1 4 2 16 0.024
2 4 10 80 0.120
3 7 4 56 0.084
4 7 22 308 0.462
5 8 16 256 0.384
6 9 10 180 0.270
answered Nov 16 '18 at 20:14
Captain TylerCaptain Tyler
12213
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
We could use mutate_at
library(tidyverse)
cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
mutate_at(vars(km, mt), funs(case_when(speed < 20 ~ .*2,
TRUE ~ .)))
If we need to do computation with separate values for each of the column, then use map2 or pmap
out <- cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
select(km, mt) %>%
map2_df(., list(2, 3), ~
case_when(cars$speed < 20 ~ .x * .y, TRUE ~ .x)) %>%
bind_cols(cars, .)
head(out)
# speed dist km mt
#1 4 2 16 0.024
#2 4 10 80 0.120
#3 7 4 56 0.084
#4 7 22 308 0.462
#5 8 16 256 0.384
#6 9 10 180 0.270
answered Nov 16 '18 at 19:49
akrunakrun
422k13209285
I ve edit the example. The mutated vars are nowkm = km * 2andmt = mt * 3
– Captain Tyler
Nov 16 '18 at 19:59
@CaptainTyler Is there any other conditions?
– akrun
Nov 16 '18 at 20:13
The objetive is mutate more than a column (existing or new) based in some row conditions
– Captain Tyler
Nov 16 '18 at 20:15
@CaptainTyler Thanks, Updated the solution
– akrun
Nov 16 '18 at 20:27
add a comment |
2
We could use mutate_at
library(tidyverse)
cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
mutate_at(vars(km, mt), funs(case_when(speed < 20 ~ .*2,
TRUE ~ .)))
If we need to do computation with separate values for each of the column, then use map2 or pmap
out <- cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
select(km, mt) %>%
map2_df(., list(2, 3), ~
case_when(cars$speed < 20 ~ .x * .y, TRUE ~ .x)) %>%
bind_cols(cars, .)
head(out)
# speed dist km mt
#1 4 2 16 0.024
#2 4 10 80 0.120
#3 7 4 56 0.084
#4 7 22 308 0.462
#5 8 16 256 0.384
#6 9 10 180 0.270
answered Nov 16 '18 at 19:49
akrunakrun
422k13209285
I ve edit the example. The mutated vars are nowkm = km * 2andmt = mt * 3
– Captain Tyler
Nov 16 '18 at 19:59
@CaptainTyler Is there any other conditions?
– akrun
Nov 16 '18 at 20:13
The objetive is mutate more than a column (existing or new) based in some row conditions
– Captain Tyler
Nov 16 '18 at 20:15
@CaptainTyler Thanks, Updated the solution
– akrun
Nov 16 '18 at 20:27
add a comment |
2
2
2
We could use mutate_at
library(tidyverse)
cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
mutate_at(vars(km, mt), funs(case_when(speed < 20 ~ .*2,
TRUE ~ .)))
If we need to do computation with separate values for each of the column, then use map2 or pmap
out <- cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
select(km, mt) %>%
map2_df(., list(2, 3), ~
case_when(cars$speed < 20 ~ .x * .y, TRUE ~ .x)) %>%
bind_cols(cars, .)
head(out)
# speed dist km mt
#1 4 2 16 0.024
#2 4 10 80 0.120
#3 7 4 56 0.084
#4 7 22 308 0.462
#5 8 16 256 0.384
#6 9 10 180 0.270
answered Nov 16 '18 at 19:49
akrunakrun
422k13209285
We could use mutate_at
library(tidyverse)
cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
mutate_at(vars(km, mt), funs(case_when(speed < 20 ~ .*2,
TRUE ~ .)))
If we need to do computation with separate values for each of the column, then use map2 or pmap
out <- cars %>%
mutate(km = speed * dist, mt = km/1000) %>%
select(km, mt) %>%
map2_df(., list(2, 3), ~
case_when(cars$speed < 20 ~ .x * .y, TRUE ~ .x)) %>%
bind_cols(cars, .)
head(out)
# speed dist km mt
#1 4 2 16 0.024
#2 4 10 80 0.120
#3 7 4 56 0.084
#4 7 22 308 0.462
#5 8 16 256 0.384
#6 9 10 180 0.270
answered Nov 16 '18 at 19:49
akrunakrun
422k13209285
edited Nov 16 '18 at 20:20
answered Nov 16 '18 at 19:49
akrunakrun
422k13209285
answered Nov 16 '18 at 19:49
akrunakrun
422k13209285
answered Nov 16 '18 at 19:49
akrunakrun
422k13209285
422k13209285
I ve edit the example. The mutated vars are nowkm = km * 2andmt = mt * 3
– Captain Tyler
Nov 16 '18 at 19:59
@CaptainTyler Is there any other conditions?
– akrun
Nov 16 '18 at 20:13
The objetive is mutate more than a column (existing or new) based in some row conditions
– Captain Tyler
Nov 16 '18 at 20:15
@CaptainTyler Thanks, Updated the solution
– akrun
Nov 16 '18 at 20:27
add a comment |
I ve edit the example. The mutated vars are nowkm = km * 2andmt = mt * 3
– Captain Tyler
Nov 16 '18 at 19:59
@CaptainTyler Is there any other conditions?
– akrun
Nov 16 '18 at 20:13
The objetive is mutate more than a column (existing or new) based in some row conditions
– Captain Tyler
Nov 16 '18 at 20:15
@CaptainTyler Thanks, Updated the solution
– akrun
Nov 16 '18 at 20:27
I ve edit the example. The mutated vars are now
km = km * 2 and mt = mt * 3– Captain Tyler
Nov 16 '18 at 19:59
I ve edit the example. The mutated vars are now
km = km * 2 and mt = mt * 3– Captain Tyler
Nov 16 '18 at 19:59
@CaptainTyler Is there any other conditions?
– akrun
Nov 16 '18 at 20:13
@CaptainTyler Is there any other conditions?
– akrun
Nov 16 '18 at 20:13
The objetive is mutate more than a column (existing or new) based in some row conditions
– Captain Tyler
Nov 16 '18 at 20:15
The objetive is mutate more than a column (existing or new) based in some row conditions
– Captain Tyler
Nov 16 '18 at 20:15
@CaptainTyler Thanks, Updated the solution
– akrun
Nov 16 '18 at 20:27
@CaptainTyler Thanks, Updated the solution
– akrun
Nov 16 '18 at 20:27
add a comment |
1
Discover this solution, but is a bit weird and tricky
mutate_when <- function (data, ...) {
dots <- eval (substitute (alist(...)))
for (i in seq (1, length (dots), by = 3)) {
condition <- eval (dots [[i]], envir = data)
mutations <- eval (dots [[i + 1]], envir = data [condition, ])
data[condition, names(mutations)] <- mutations
mutations_else <- eval (dots [[i + 2]], envir = data [!condition, ])
data[!condition, names(mutations)] <- mutations_else
}
data
}
cars %>%
mutate(
km = speed * dist,
mt = km/1000
) %>%
mutate_when(
speed < 20,
list (
km = km * 2,
mt = mt * 3
),
list (
0
)
)
Gives
speed dist km mt
1 4 2 16 0.024
2 4 10 80 0.120
3 7 4 56 0.084
4 7 22 308 0.462
5 8 16 256 0.384
6 9 10 180 0.270
answered Nov 16 '18 at 20:14
Captain TylerCaptain Tyler
12213
add a comment |
1
Discover this solution, but is a bit weird and tricky
mutate_when <- function (data, ...) {
dots <- eval (substitute (alist(...)))
for (i in seq (1, length (dots), by = 3)) {
condition <- eval (dots [[i]], envir = data)
mutations <- eval (dots [[i + 1]], envir = data [condition, ])
data[condition, names(mutations)] <- mutations
mutations_else <- eval (dots [[i + 2]], envir = data [!condition, ])
data[!condition, names(mutations)] <- mutations_else
}
data
}
cars %>%
mutate(
km = speed * dist,
mt = km/1000
) %>%
mutate_when(
speed < 20,
list (
km = km * 2,
mt = mt * 3
),
list (
0
)
)
Gives
speed dist km mt
1 4 2 16 0.024
2 4 10 80 0.120
3 7 4 56 0.084
4 7 22 308 0.462
5 8 16 256 0.384
6 9 10 180 0.270
answered Nov 16 '18 at 20:14
Captain TylerCaptain Tyler
12213
add a comment |
1
1
1
Discover this solution, but is a bit weird and tricky
mutate_when <- function (data, ...) {
dots <- eval (substitute (alist(...)))
for (i in seq (1, length (dots), by = 3)) {
condition <- eval (dots [[i]], envir = data)
mutations <- eval (dots [[i + 1]], envir = data [condition, ])
data[condition, names(mutations)] <- mutations
mutations_else <- eval (dots [[i + 2]], envir = data [!condition, ])
data[!condition, names(mutations)] <- mutations_else
}
data
}
cars %>%
mutate(
km = speed * dist,
mt = km/1000
) %>%
mutate_when(
speed < 20,
list (
km = km * 2,
mt = mt * 3
),
list (
0
)
)
Gives
speed dist km mt
1 4 2 16 0.024
2 4 10 80 0.120
3 7 4 56 0.084
4 7 22 308 0.462
5 8 16 256 0.384
6 9 10 180 0.270
answered Nov 16 '18 at 20:14
Captain TylerCaptain Tyler
12213
Discover this solution, but is a bit weird and tricky
mutate_when <- function (data, ...) {
dots <- eval (substitute (alist(...)))
for (i in seq (1, length (dots), by = 3)) {
condition <- eval (dots [[i]], envir = data)
mutations <- eval (dots [[i + 1]], envir = data [condition, ])
data[condition, names(mutations)] <- mutations
mutations_else <- eval (dots [[i + 2]], envir = data [!condition, ])
data[!condition, names(mutations)] <- mutations_else
}
data
}
cars %>%
mutate(
km = speed * dist,
mt = km/1000
) %>%
mutate_when(
speed < 20,
list (
km = km * 2,
mt = mt * 3
),
list (
0
)
)
Gives
speed dist km mt
1 4 2 16 0.024
2 4 10 80 0.120
3 7 4 56 0.084
4 7 22 308 0.462
5 8 16 256 0.384
6 9 10 180 0.270
answered Nov 16 '18 at 20:14
Captain TylerCaptain Tyler
12213
answered Nov 16 '18 at 20:14
Captain TylerCaptain Tyler
12213
answered Nov 16 '18 at 20:14
Captain TylerCaptain Tyler
12213
answered Nov 16 '18 at 20:14
Captain TylerCaptain Tyler
12213
12213
add a comment |
add a comment |
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