create counter variable with Boolean condition usi

问题:

I want to create a counter variable c based on the group variable user and True or False variable B.

DT <- data.table(time=c(1,2,3,1,1,2,3,1,1,1),user=c(1,1,1,2,3,3,3,4,4,5), B=c('t','f','t','f','f','t','t','t','t','t'))
DT

The desired output of variable c

    time user B C
 1:    1    1 t 1
 2:    2    1 f 1
 3:    3    1 t 2
 4:    1    2 f 0
 5:    1    3 f 0
 6:    2    3 t 1
 7:    3    3 t 2
 8:    1    4 t 1 
 9:    2    4 t 2
10:    1    5 t 1

variable c is a counter within the group when B is true. The logic (NOT code) of variable c is as follow. The sequence do matter as you can see from the time variable.

 if time=1 and b=='f' {c=0}
    else 
    {
      if b=='t'{c=previous[c]+1} 
      else {c=previous[c]}
    }


  #if there is no variable b, the counter can be created using dplyr:     
          group_by(user)%>%mutate(c=seq_along(user))
  #or data.table
         DT[, c := seq_len(.N), by = user]
  # we can use data.table function shift() combined with for loop but i want to avoid for loop, it is slow and I have 300,000 rows.

回答1:

We group by 'user', cumsum the logical vector (B=="t") and assign (:= ) the output to 'C'.

DT[, C:= cumsum(B=="t"), by = user]
DT
#    time user B C
# 1:    1    1 t 1
# 2:    2    1 f 1
# 3:    3    1 t 2
# 4:    1    2 f 0
# 5:    1    3 f 0
# 6:    2    3 t 1
# 7:    3    3 t 2
# 8:    1    4 t 1
# 9:    2    4 t 2
#10:    1    5 t 1

---

The same logic can be applied to dplyr methods

library(dplyr)
DT %>%
   group_by(user) %>%
   mutate(C = cumsum(B == "t"))

回答2:

If you use for loop for the same the, syntax for the following logic would be

for(i in unique(DT$user)){
  DT$c[DT$user==i] <- cumsum(DT$B[DT$user==i]=="t")
}

print(DT)

  time user B c
 1:    1    1 t 1
 2:    2    1 f 1
 3:    3    1 t 2
 4:    1    2 f 0
 5:    1    3 f 0
 6:    2    3 t 1
 7:    3    3 t 2
 8:    1    4 t 1
 9:    2    4 t 2
10:    1    5 t 1