error in mutate() when updating column using if else condition for distinct ID-CodePudding

I a using the following code but it is giving me error

UPDATED_PENALTY['HISTORIC_PENALTY'] <- ave(UPDATED_PENALTY$PENALTY, UPDATED_PENALTY$OFFICIAL_NUMBER, FUN=cumsum)
UPDATED_PENALTY['cum_prodsum']<-NA
UPDATED_PENALTY %>%
  mutate(cum_prodsum = ifelse(UPDATED_PENALTY$INSPECTION_CLEARED==TRUE, UPDATED_PENALTY$HISTORIC_PENALTY*0.9, UPDATED_PENALTY$HISTORIC_PENALTY*0.9   UPDATED_PENALTY$PENALTY))

Error

Error in `mutate()`:
! Problem while computing `cum_prodsum = ifelse(...)`.
x `cum_prodsum` must be size 12 or 1, not 32217.
i The error occurred in group 1: OFFICIAL_NUMBER = "XXXXX".
Run `rlang::last_error()` to see where the error occurred.

Also, I want to exclude first "HISTORIC PENALTY" of each "OFFICIAL NUMBER from manipulation and copy the value as it is, rest all apply the above.

CodePudding user response：

We may do

library(dplyr)
UPDATE_PENALTY <- UPDATE_PENALTY %>%
        ungroup %>% 
        mutate(cum_prodsum = case_when(INSPECTION_COUNT == 1 
   ~ HISTORIC_PENALTY, 
    INSPECTION_CLEARED ~ HISTORIC_PENALTY*0.9, 
     TRUE  ~HISTORIC_PENALTY*0.9    PENALTY))

-output

UPDATE_PENALTY
# A tibble: 46 × 7
   OFFICIAL_NUMBER NUM_DEF PENALTY INSPECTION_COUNT INSPECTION_CLEARED HISTORIC_PENALTY cum_prodsum
   <chr>             <dbl>   <dbl>            <int> <lgl>                         <dbl>       <dbl>
 1 170718               14      60                1 FALSE                            60        60  
 2 803729                0       0                1 TRUE                              0         0  
 3 803729               65     311                2 FALSE                           311       591. 
 4 170718                8      24                2 FALSE                            84        99.6
 5 170718                0       0                3 TRUE                             84        75.6
 6 170718               39     110                4 FALSE                           194       285. 
 7 170718                0       0                5 TRUE                            194       175. 
 8 170718                1       5                6 FALSE                           199       184. 
 9 170718                0       0                7 TRUE                            199       179. 
10 170718                0       0                8 TRUE                            199       179. 
# … with 36 more rows