How to get subsets by unique values

[TimeTravellerK]

Is there a function to get all subsets by their each unique value available in 'Department' column ? Can this be filter by unique value and copy --> create a dataframe ?

ID	Department	Strength	DoD	Group_ID
82950	Training	96	953	A
32045	Training	86	5277	A
84817	Sales	77	1633	C
2857	Logistics	88	5629	Z
55587	Training	46	6710	L
73103	Sales	51	1892	M
5837	Sales	62	2394	A
7140	Sales	81	2854	C
70450	Logistics	56	5449	A
34207	Manufacturing	85	1332	S
20095	Manufacturing	58	4589	X
30200	Sales	91	1094	W
47211	Manufacturing	36	5862	C
8932	Manufacturing	75	4847	A
82663	Manufacturing	39	5798	A

What I expect to see;
Logistics.xlsx

ID	Department	Strength	DoD	Group_ID
2857	Logistics	88	5629	Z
70450	Logistics	56	5449	A

Manufacturing.xlsx

ID	Department	Strength	DoD	Group_ID
34207	Manufacturing	85	1332	S
20095	Manufacturing	58	4589	X
47211	Manufacturing	36	5862	C
8932	Manufacturing	75	4847	A
82663	Manufacturing	39	5798	A

and so on.....

[GreyMerchant]

Hello,

Is this what you had in mind? (I just haven't unpacked the list object yet but you can easily save each seperately with the right name)

library(tidyverse)


df <- data.frame(
  stringsAsFactors = FALSE,
                ID = c(82950L,32045L,84817L,2857L,
                       55587L,73103L,5837L,7140L,70450L,34207L,20095L,
                       30200L,47211L,8932L,82663L),
        Department = c("Training","Training","Sales",
                       "Logistics","Training","Sales","Sales","Sales",
                       "Logistics","Manufacturing","Manufacturing","Sales",
                       "Manufacturing","Manufacturing","Manufacturing"),
          Strength = c(96L,86L,77L,88L,46L,51L,
                       62L,81L,56L,85L,58L,91L,36L,75L,39L),
               DoD = c(953L,5277L,1633L,5629L,
                       6710L,1892L,2394L,2854L,5449L,1332L,4589L,1094L,5862L,
                       4847L,5798L),
          Group_ID = c("A","A","C","Z","L","M",
                       "A","C","A","S","X","W","C","A","A")
)


filter_values <- df %>% select(Department) %>% distinct() %>% unlist()



df_list <- list()

for (i in 1:length(filter_values)) {
  df_list[[i]] <-  df %>% filter(Department == filter_values[i])
  
}

df_list[[1]]
#>      ID Department Strength  DoD Group_ID
#> 1 82950   Training       96  953        A
#> 2 32045   Training       86 5277        A
#> 3 55587   Training       46 6710        L

df_list[[2]]
#>      ID Department Strength  DoD Group_ID
#> 1 84817      Sales       77 1633        C
#> 2 73103      Sales       51 1892        M
#> 3  5837      Sales       62 2394        A
#> 4  7140      Sales       81 2854        C
#> 5 30200      Sales       91 1094        W

df_list[[3]]
#>      ID Department Strength  DoD Group_ID
#> 1  2857  Logistics       88 5629        Z
#> 2 70450  Logistics       56 5449        A

df_list[[4]]
#>      ID    Department Strength  DoD Group_ID
#> 1 34207 Manufacturing       85 1332        S
#> 2 20095 Manufacturing       58 4589        X
#> 3 47211 Manufacturing       36 5862        C
#> 4  8932 Manufacturing       75 4847        A
#> 5 82663 Manufacturing       39 5798        A

^{Created on 2020-10-11 by the reprex package (v0.3.0)}

[FJCC]

Here is another approach giving a result similar to @GreyMerchant's code.

library(purrr)
DF <- data.frame(ID = 1:10, 
                 Department = c("T", "L", "T", "S", "L", "S", "T", "M", "S", "M"))
Depts <- unique(DF$Department)
names(Depts) <- Depts
MyFunc <- function(Nm, D, Dpt) D[D[[Dpt]] == Nm,]
DataFrames <- map(Depts, .f = MyFunc, D = DF, Dpt = "Department")
DataFrames
#> $T
#>   ID Department
#> 1  1          T
#> 3  3          T
#> 7  7          T
#> 
#> $L
#>   ID Department
#> 2  2          L
#> 5  5          L
#> 
#> $S
#>   ID Department
#> 4  4          S
#> 6  6          S
#> 9  9          S
#> 
#> $M
#>    ID Department
#> 8   8          M
#> 10 10          M

^{Created on 2020-10-11 by the reprex package (v0.3.0)}

[TimeTravellerK]

This is fantastic. Thank you @GreyMerchant

Also, you mentioned something about unpacking.

df1 <-- df_list[[1]] gives me a subset for department 'Training'. Is there a way to automatically unlist one by one by their department and save as department.xlsx ?

see this post to get additional details:

Filter by unique category and save as uniqueCategory.xlsx in a folder

I have a dataframe as provided below; ID Department Strength DoD Group_ID 82950 Training 96 953 A 32045 Training 86 5277 A 84817 Sales 77 1633 C 2857 Logistics 88 5629 Z 55587 Training 46 6710 L 73103 Sales 51 1892 M 5837 Sales 62 2394 A 7140 Sales 81 2854 C 70450 Logistics 56 5449 A 34207 Manufacturing 85 1332 S 20095 Manufacturing 58 4589 X 30200 Sales 91 1094 W 47211 Manufacturing 36 5862 C 8932 Manufacturing 75 4847 A 82663 Manufacturing 39 5798 A I do this activity everyday manually filter column category one by one, copy filtered data, open a new .xlsx file, name it by the filtered category name, password protect and save it in a folder. edit: T…