CODEWITHSUNDEEP
rdplyrgrouping
Keeley Seymour
Keeley Seymour

Reputation: 291

How to find the last occurrence of a certain observation in grouped data in R?

I have data that is grouped using dplyr in R. I would like to find the last occurrence of observations ('B') equal to or greater than 1 (1, 2, 3 or 4) in each group ('A'), in terms of the 'day' they occurred. I would like the value of 'day' for each group to be given in a new column.

For example, given the following sample of data, grouped by A (this has been simplified, my data is actually grouped by 3 variables):

A  B  day
a  2  1
a  2  2
a  1  5
a  0  8
b  3  1
b  3  4
b  3  6 
b  0  7 
b  0  9
c  1  2 
c  1  3
c  1  4

I would like to achieve the following:

A  B  day last
a  2  1   5
a  2  2   5
a  1  5   5
a  0  8   5
b  3  1   6
b  3  4   6
b  3  6   6
b  0  7   6
b  0  9   6
c  1  2   4
c  1  3   4
c  1  4   4

I hope this makes sense, thank you all very much for your help! I have thoroughly searched for my answer online but couldn't find anything. However, if I have accidentally duplicated a question then I apologise.

Upvotes: 3

Views: 3002

Answers (2)

akrun
akrun

Reputation: 887901

We can try

library(data.table)
setDT(df1)[, last := day[tail(which(B>=1),1)] , A]
df1
#    A B day last
# 1: a 2   1    5
# 2: a 2   2    5
# 3: a 1   5    5
# 4: a 0   8    5
# 5: b 3   1    6
# 6: b 3   4    6
# 7: b 3   6    6
# 8: b 0   7    6
# 9: b 0   9    6
#10: c 1   2    4
#11: c 1   3    4
#12: c 1   4    4

Or using dplyr

library(dplyr)
df1 %>%
   group_by(A) %>%
   mutate(last = day[max(which(B>=1))])

Or use the last function from dplyr (as @docendo discimus suggested)

df1 %>%
   group_by(A) %>%
   mutate(last= last(day[B>=1]))

For the second question,

setDT(df1)[, dayafter:= if(all(!!B)) NA_integer_  else 
             day[max(which(B!=0))+1L] , A]
#    A B day dayafter
# 1: a 2   1        8
# 2: a 2   2        8
# 3: a 1   5        8
# 4: a 0   8        8
# 5: b 3   1        7
# 6: b 3   4        7
# 7: b 3   6        7
# 8: b 0   7        7
# 9: b 0   9        7
#10: c 1   2       NA
#11: c 1   3       NA
#12: c 1   4       NA

Upvotes: 3

Mikko
Mikko

Reputation: 7755

Here is a solution that does not require loading external packages:

df <- structure(list(A = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 3L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"), 
B = c(2L, 2L, 1L, 0L, 3L, 3L, 3L, 0L, 0L, 1L, 1L, 1L), day = c(1L, 
2L, 5L, 8L, 1L, 4L, 6L, 7L, 9L, 2L, 3L, 4L)), .Names = c("A", 
"B", "day"), class = "data.frame", row.names = c(NA, -12L))

x <- split(df, df$A, drop = TRUE)

tp <- lapply(x, function(k) {
  tmp <- k[k$B >0,]
  k$last <- tmp$day[length(tmp$day)]
  k
})

do.call(rbind, tp)

         A B day last
#a.1  a 2   1    5
#a.2  a 2   2    5
#a.3  a 1   5    5
#a.4  a 0   8    5
#b.5  b 3   1    6
#b.6  b 3   4    6
#b.7  b 3   6    6
#b.8  b 0   7    6
#b.9  b 0   9    6
#c.10 c 1   2    4
#c.11 c 1   3    4
#c.12 c 1   4    4

Upvotes: 2

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