Adjust condition for filter in R

I would like to make an adjustment in md1. Note that both group_by of med and inner_join of SPV will depend on group_cols, however, see the filter of md1, I have described them all, but I would like to leave it as I did for the others, because it will depend on what I get in group_cols . In other words:

If I have "Category","Week" and "DTT" in group_cols, do:

  filter(date2 == ymd(dmda), Category == CategoryChosse,DTT == DTest)

If I have "Category" and "Week" in group_cols, do:

  filter(date2 == ymd(dmda), Category == CategoryChosse)

If I have just "Week" in group_cols, do:

   filter(date2 == ymd(dmda))

Executable code below


df1 <- structure(
  list(date1= c("2021-06-28","2021-06-28","2021-06-28","2021-06-28"),
       date2 = c("2021-06-23","2021-06-24","2021-06-30","2021-07-01"),
       DTT= c("Hol","Hol","Hol",0),
       Week= c("Wednesday","Thursday","Wednesday","Thursday"),
       Category = c("ABC","FDE","ABC","FDE"),
       DR1 = c(4,1,1,2),
       DR01 = c(4,1,2,3), DR02= c(4,2,0,2),DR03= c(9,5,0,1),
       DR04 = c(5,4,3,2),DR05 = c(5,4,0,2)),
  class = "data.frame", row.names = c(NA, -4L))



x<-Dx %>% select(starts_with("DR0"))

x<-cbind(Dx, setNames(Dx$DR1 - x, paste0(names(x), "_PV")))

PV<-select(x, date2,Week, Category, DTT, DR1, ends_with("PV"))

group_cols <-
  if (any(PV$DTT == DTest & PV$Week == Wk & PV$Category == CategoryChosse, na.rm = TRUE)) {
    c("Category", "Week", "DTT")
  } else if (any(PV$Week == Wk & PV$Category == CategoryChosse & PV$DTT != DTest, na.rm=TRUE)) {
    c("Category", "Week")
  } else {

med <- PV %>%
  group_by(across(all_of(group_cols))) %>%
  summarize(across(ends_with("PV"), median),.groups = 'drop')

SPV <- df1 %>%
  inner_join(med, by = group_cols) %>%
  mutate(across(matches("^DR0\\d+$"), ~.x + 
                  get(paste0(cur_column(), '_PV')),
                .names = '{col}_{col}_PV')) %>%
  select(date1:Category, DR01_DR01_PV:last_col())%>%

md1 <- df1 %>%
  filter(date2 == ymd(dmda), Category == CategoryChosse,DTT == DTest) %>%
  select(starts_with("DR0")) %>%
  pivot_longer(cols = everything()) %>%
  arrange(desc(row_number())) %>%
  mutate(cs = cumsum(value)) %>%
  filter(cs == 0) %>%
(dropnames <- paste0(md1,"_",md1, "_PV"))

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