如何根据R中的分组将单独的列值添加到另一列？

Question

I have a data table called "DF1" like so (with structure beneath):

# A tibble: 10 x 4
# Groups:   Month [1]
   Response.Status UNSUBSCRIBE   Month Year 
   <fct>           <fct>         <fct> <chr>
 1 SURVEY_OPENED   NA            Nov   2020 
 2 NOT_RESPONDED   TRUE          Nov   2020 
 3 EMAIL_OPENED    NA            Nov   2020 
 4 NOT_RESPONDED   NA            Nov   2020 
 5 NOT_RESPONDED   TRUE          Nov   2020 
 6 EMAIL_OPENED    NA            Nov   2020 
 7 NOT_RESPONDED   TRUE          Nov   2020 
 8 EMAIL_OPENED    NA            Nov   2020 
 9 SURVEY_OPENED   TRUE          Nov   2020 
10 EMAIL_OPENED    NA            Nov   2020 

structure(list(Response.Status = structure(c(5L, 2L, 1L, 2L, 
2L, 1L), .Label = c("EMAIL_OPENED", "NOT_RESPONDED", "PARTIALLY_SAVED", 
"SUBMITTED", "SURVEY_OPENED", "UNDELIVERED_OR_BOUNCED"), class = "factor"), 
    UNSUBSCRIBE = structure(c(NA_integer_, TRUE, NA_integer_, 
    TRUE, NA_integer_, TRUE), .Label = "TRUE", class = "factor"), 
    Month = c("Nov", "Nov", "Nov", "Nov", "Nov", "Nov"), Year = c("2020", 
    "2020", "2020", "2020", "2020", "2020")), row.names = c(NA, 
-6L), groups = structure(list(Month = "Nov", .rows = structure(list(
    1:6), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", 
"list"))), row.names = 1L, class = c("tbl_df", "tbl", "data.frame"
), .drop = TRUE), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
))

I converted this table in order to calculate the total amount of "Response.Status" per month with the table below called "Updated_DF":

Updated_DF <- DF1 %>%
  mutate(Month = format(as.Date(date, format = "%Y/%m/%d"), "%m/%Y")) %>%
  group_by(Month, Response.Status) %>%
  summarise(sum = n()) %>%
  pivot_wider(names_from = Month, values_from = sum)

# A tibble: 6 x 16
  Response.Status        `01/2020` `02/2020` `03/2020` `04/2020` `05/2020` `06/2020` `07/2020` `08/2020` `09/2019` `09/2020` `10/2019` `10/2020` `11/2019` `11/2020` `12/2019`
  <fct>                      <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>
1 EMAIL_OPENED                1090      3123      4087      5005      2098      1876      4278      3665       901      4162       883      2597      1187       690       883
2 NOT_RESPONDED               3205      9733     13177     15255      5470      4788     12708     10731      2835     15088      2846      8094      3665      1938      2667
3 PARTIALLY_SAVED                5        34        56         8        28        22        73        86        11        14         7        23         8         8         2
4 SUBMITTED                    216       557       838       828       357       310       654       621       214      1001       233       497       264       122       194
5 SURVEY_OPENED                164       395       597      1016       245       212       513       625       110       588       123       349       202        94       120
6 UNDELIVERED_OR_BOUNCED        92       280       318       260       109       127       319       321        63       445        69       192        93        39        74

Data Structure for "Updated_DF":

structure(list(Response.Status = structure(1:6, .Label = c("EMAIL_OPENED", 
"NOT_RESPONDED", "PARTIALLY_SAVED", "SUBMITTED", "SURVEY_OPENED", 
"UNDELIVERED_OR_BOUNCED"), class = "factor"), `01/2020` = c(1090L, 
3205L, 5L, 216L, 164L, 92L), `02/2020` = c(3123L, 9733L, 34L, 
557L, 395L, 280L), `03/2020` = c(4087L, 13177L, 56L, 838L, 597L, 
318L), `04/2020` = c(5005L, 15255L, 8L, 828L, 1016L, 260L), `05/2020` = c(2098L, 
5470L, 28L, 357L, 245L, 109L), `06/2020` = c(1876L, 4788L, 22L, 
310L, 212L, 127L), `07/2020` = c(4278L, 12708L, 73L, 654L, 513L, 
319L), `08/2020` = c(3665L, 10731L, 86L, 621L, 625L, 321L), `09/2019` = c(901L, 
2835L, 11L, 214L, 110L, 63L), `09/2020` = c(4162L, 15088L, 14L, 
1001L, 588L, 445L), `10/2019` = c(883L, 2846L, 7L, 233L, 123L, 
69L), `10/2020` = c(2597L, 8094L, 23L, 497L, 349L, 192L), `11/2019` = c(1187L, 
3665L, 8L, 264L, 202L, 93L), `11/2020` = c(690L, 1938L, 8L, 122L, 
94L, 39L), `12/2019` = c(883L, 2667L, 2L, 194L, 120L, 74L)), row.names = c(NA, 
-6L), class = c("tbl_df", "tbl", "data.frame"))

What I would like to do is add the "UNSUBSCRIBE" field from DF1 to my transpose process in order to have Response.Status include the sum TRUE values of "UNSUBSCRIBE" for each month -- even though it is a separate column.

I am completely stumped on how to do this - I eventually plan on using formattable and add color themes to this.

EXPECTED OUTPUT:

  Response.Status        `01/2020` `02/2020` `03/2020` `04/2020` `05/2020` `06/2020` `07/2020` `08/2020` `09/2019` `09/2020` `10/2019` `10/2020` `11/2019` `11/2020` `12/2019`
  <fct>                      <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>     <int>
1 EMAIL_OPENED                1090      3123      4087      5005      2098      1876      4278      3665       901      4162       883      2597      1187       690       883
2 NOT_RESPONDED               3205      9733     13177     15255      5470      4788     12708     10731      2835     15088      2846      8094      3665      1938      2667
3 PARTIALLY_SAVED                5        34        56         8        28        22        73        86        11        14         7        23         8         8         2
4 SUBMITTED                    216       557       838       828       357       310       654       621       214      1001       233       497       264       122       194
5 SURVEY_OPENED                164       395       597      1016       245       212       513       625       110       588       123       349       202        94       120
6 UNDELIVERED_OR_BOUNCED        92       280       318       260       109       127       319       321        63       445        69       192        93        39        74
7 UNSUBSCRIBE                  92       280       318       260       109       127       319       321        63       445        69       192        93        39        74

Answer 1

Is this what you are looking for?

library(dplyr)
library(tidyr)

DF1 %>% 
  mutate(UNSUBSCRIBE = if_else(UNSUBSCRIBE == "TRUE", "UNSUBSCRIBE", NA_character_)) %>% 
  pivot_longer(c(Response.Status, UNSUBSCRIBE), values_to = "Response.Status") %>% 
  drop_na() %>% 
  count(Month, Year, Response.Status) %>% 
  pivot_wider(names_from = c("Month", "Year"), names_sep = "/", values_from = n)

Output

# A tibble: 4 x 2
  Response.Status `Nov/2020`
  <chr>                <int>
1 EMAIL_OPENED             2
2 NOT_RESPONDED            3
3 SURVEY_OPENED            1
4 UNSUBSCRIBE              3