Add group in column result (in dplyr)-CodePudding

I find myself doing this quite often.

set.seed(123)
test_data=data.frame(sample=sample(LETTERS[1:10], 100,replace = TRUE), type=sample(letters[1:2], 100, replace=TRUE ), area=sample(1:100, replace=TRUE) )

The content of test_data is:

head(test_data)
  sample type area
1      C    b   24
2      C    b   63
3      J    a   54
4      B    b   23
5      F    a   26
6      E    a   33

I usually want to sum a specific column by some grouping properties, and to do that I use:

res_sum=test_data %>% group_by(sample, type) %>% summarise_at( .vars = "area", .funs = sum )

So far so good, the problem arrives when I want to put the results in a "nice" format. The lines below put the result in the way I want, but I find this way cumbersome.

res_sum_a=res_sum[res_sum$type=="a", ]
colnames(res_sum_a)[3]=paste0( colnames(res_sum)[3], ".a")

res_sum_b=res_sum[res_sum$type=="b", ]
colnames(res_sum_b)[3]=paste0( colnames(res_sum)[3], ".b")

res_df=merge(res_sum_a[,c(1,3)], res_sum_b[, c(1,3)], by="sample", all=TRUE)

head(res_df)

  sample area.a area.b
1      A    244    147
2      B     17    152
3      C    153    541
4      D    107     94
5      E    246    266
6      F    189    286

Note, there may be more than 2 "type" in the original data frame (so, like a,b,c....). Is there a way, that is more dplyr idiomatic to do this? Thanks.

CodePudding user response：

What you're referring to is pivoting, which is provided by a separate package ({tidyr}).

I also updated your dplyr code to most recent syntax (e.g., summarise_at() has been repalced by across()).

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidyr)

set.seed(123)
test_data=data.frame(sample=sample(LETTERS[1:10], 100,replace = TRUE), type=sample(letters[1:2], 100, replace=TRUE ), area=sample(1:100, replace=TRUE) )

test_data |> 
  group_by(sample, type) |> 
  summarise(across(area, sum)) |> 
  tidyr::pivot_wider(names_from = type, values_from = area)
#> `summarise()` has grouped output by 'sample'. You can override using the
#> `.groups` argument.
#> # A tibble: 10 x 3
#> # Groups:   sample [10]
#>    sample     a     b
#>    <chr>  <int> <int>
#>  1 A        244   147
#>  2 B         17   152
#>  3 C        153   541
#>  4 D        107    94
#>  5 E        246   266
#>  6 F        189   286
#>  7 G         48   483
#>  8 H        223    94
#>  9 I        285   345
#> 10 J        491   252

^{Created on 2022-11-16 with reprex v2.0.2}