I want to run the same commands (basically some general filter(), mutate() summarise() etc.) over a bunch of variables with the group_by() command. Is there a way to bundle this process instead of having to use a block of code for each individual variable? I've tried using a for loop, which unfortunately didn't work for me. My goal is to only have to write one block of code for all group_by variables.
library(dplyr)
df <- mtcars
Test1 <- df %>%
group_by(mpg) %>%
filter(hp > 100) %>%
summarise(N = n())
Test2 <- df %>%
group_by(cyl) %>%
filter(hp > 100) %>%
summarise(N = n())
Test3 <- df %>%
group_by(disp) %>%
filter(hp > 100) %>%
summarise(N = n())
Thank you in advance!
CodePudding user response：
You can do it using lapply, which will result in a list of data frames. Then you can unlist it to individual data frames.
After my code, you should find three data frames in your global environment with the name of "Test1", "Test2" and "Test3".
library(dplyr)
setNames(
lapply(c("mpg", "cyl", "disp"), function(x)
mtcars %>% group_by(mtcars[x]) %>% filter(hp > 100) %>% summarize(N = n())),
c("Test1", "Test2", "Test3")
) %>% list2env(envir = .GlobalEnv)
Output
Test1
# A tibble: 18 x 2
mpg N
<dbl> <int>
1 10.4 2
2 13.3 1
3 14.3 1
4 14.7 1
5 15 1
6 15.2 2
7 15.5 1
8 15.8 1
9 16.4 1
10 17.3 1
11 17.8 1
12 18.1 1
13 18.7 1
14 19.2 2
15 19.7 1
16 21 2
17 21.4 2
18 30.4 1
Test2
# A tibble: 3 x 2
cyl N
<dbl> <int>
1 4 2
2 6 7
3 8 14
Test3
# A tibble: 18 x 2
disp N
<dbl> <int>
1 95.1 1
2 121 1
3 145 1
4 160 2
5 168. 2
6 225 1
7 258 1
8 276. 3
9 301 1
10 304 1
11 318 1
12 350 1
13 351 1
14 360 2
15 400 1
16 440 1
17 460 1
18 472 1
CodePudding user response：
One option would be to store the variable names in a vector or list, then loop over this list using e.g. lapply where I make use of the .data pro-noun:
library(dplyr)
df <- mtcars
var_list <- c("mpg", "cyl", "disp")
lapply(var_list, function(x) {
df %>%
group_by(.data[[x]]) %>%
filter(hp > 100) %>%
summarise(N = n())
})
#> [[1]]
#> # A tibble: 18 × 2
#> mpg N
#> <dbl> <int>
#> 1 10.4 2
#> 2 13.3 1
#> 3 14.3 1
#> 4 14.7 1
#> 5 15 1
#> 6 15.2 2
#> 7 15.5 1
#> 8 15.8 1
#> 9 16.4 1
#> 10 17.3 1
#> 11 17.8 1
#> 12 18.1 1
#> 13 18.7 1
#> 14 19.2 2
#> 15 19.7 1
#> 16 21 2
#> 17 21.4 2
#> 18 30.4 1
#>
#> [[2]]
#> # A tibble: 3 × 2
#> cyl N
#> <dbl> <int>
#> 1 4 2
#> 2 6 7
#> 3 8 14
#>
#> [[3]]
#> # A tibble: 18 × 2
#> disp N
#> <dbl> <int>
#> 1 95.1 1
#> 2 121 1
#> 3 145 1
#> 4 160 2
#> 5 168. 2
#> 6 225 1
#> 7 258 1
#> 8 276. 3
#> 9 301 1
#> 10 304 1
#> 11 318 1
#> 12 350 1
#> 13 351 1
#> 14 360 2
#> 15 400 1
#> 16 440 1
#> 17 460 1
#> 18 472 1
EDIT To store the results in a dataframe I would first name the vector of variables, inside the function use the same name for the grouping column. i.e. I simply used "value" then use bind_rows to bind the results together where as an identifier I added the variable name:
var_list <- c("mpg", "cyl", "disp")
names(var_list) <- var_list
lapply(var_list, function(x) {
df %>%
group_by(value = .data[[x]]) %>%
filter(hp > 100) %>%
summarise(N = n())
}) %>%
bind_rows(.id = "var")
#> # A tibble: 39 x 3
#> var value N
#> <chr> <dbl> <int>
#> 1 mpg 10.4 2
#> 2 mpg 13.3 1
#> 3 mpg 14.3 1
#> 4 mpg 14.7 1
#> 5 mpg 15 1
#> 6 mpg 15.2 2
#> 7 mpg 15.5 1
#> 8 mpg 15.8 1
#> 9 mpg 16.4 1
#> 10 mpg 17.3 1
#> # ... with 29 more rows
CodePudding user response：
Since you already use a tidyverse approach you could just pivot the table:
library(tidyverse)
df <- mtcars
df %>%
filter(hp > 100) %>%
select(mpg, cyl, hp) %>%
pivot_longer(cols = c(mpg, cyl, hp),
names_to = "var",
values_to = "value") %>%
group_by(var) %>%
summarise(N = n())
#> # A tibble: 3 × 2
#> var N
#> <chr> <int>
#> 1 cyl 23
#> 2 hp 23
#> 3 mpg 23
Would that solve your problem? This way you can put any functions after the pivoted dataset and apply them to all variables as you wish. You can pivot it back if you prefer to have them as variables again. The main advantage of this approach is that you never split your dataset. However, there are downsides to this as well.
Another option could be using purrr.