根据其他 2 列的值减去一列中的值

Question

I have the dataframe below

vol<-structure(list(activity = c("RAAMELK", "RAAMELK", "Separering", 
"Separering", "Sweetmilk Pasteurizer 8331", "Sweetmilk Pasteurizer 8331", 
"9004 - T42 kartong 70x70", "9004 - T42 kartong 70x70", "9006 - T61 BIB", 
"9006 - T61 BIB", "9004 - T41 kartong 70x70", "9004 - T41 kartong 70x70"
), qty = c(0, 31, 31, 9, 31, 31, 6, 6, 3, 3, 28, 28), in_out = c("in", 
"out", "in", "out", "in", "out", "in", "out", "in", "out", "in", 
"out"), qty_scrap = c(0, 0, 0, 0, 0, -270.64, 0, 524, 0, 260, 
0, 0)), 
row.names = c(NA, -12L), case_id = "case_id", activity_id = "activity", 
activity_instance_id = "action", lifecycle_id = "registration_type", 
resource_id = "resource", timestamp = "timestamp", 
class = c("eventlog", "log", "tbl_df", "tbl", "data.frame"))

And I want to subtract the values on column qty based on if they have the same value in column activity and with the logic in-out from the in_out column. The result will be saved in the qty_scrap column.

The result dataframe will then have 4 columns. The activity column with the unique name of the activity , the in column with the in value of the qty , the out column with the out value of the qty and the qty_scrap column with their difference.

Answer 1

You can reshape your data to a wider format, and then mutate your scrape column by subtracting the in and out columns.

library(dplyr)
library(tidyr)

vol %>% 
  pivot_wider(id_cols = "activity", names_from = "in_out", values_from = "qty") %>% 
  mutate(qty_scrap = `in` - out)

#> # A tibble: 6 x 4
#>   activity                    `in`   out qty_scrap
#>   <chr>                      <dbl> <dbl>     <dbl>
#> 1 RAAMELK                        0    31       -31
#> 2 Separering                    31     9        22
#> 3 Sweetmilk Pasteurizer 8331    31    31         0
#> 4 9004 - T42 kartong 70x70       6     6         0
#> 5 9006 - T61 BIB                 3     3         0
#> 6 9004 - T41 kartong 70x70      28    28         0

^{Created on 2023-01-24 by the reprex package (v2.0.1)}

Answer 2

You could try this:

vol %>% 
  group_by(activity) %>%
  mutate(qty_scrap = head(qty, 1) - tail(qty, 1)) %>%
  tidyr::pivot_wider(names_from = in_out, values_from = qty) %>%
  select(activity, `in`, out, qty_scrap)

Output

#   activity                    `in`   out qty_scrap
#   <chr>                      <dbl> <dbl>     <dbl>
# 1 RAAMELK                        0    31       -31
# 2 Separering                    31     9        22
# 3 Sweetmilk Pasteurizer 8331    31    31         0
# 4 9004 - T42 kartong 70x70       6     6         0
# 5 9006 - T61 BIB                 3     3         0
# 6 9004 - T41 kartong 70x70      28    28         0