I have a very large dataset that looks simplified like this:
row. member_id entry_id comment_count timestamp
1 1 a 4 2008-06-09 12:41:00
2 1 b 1 2008-07-14 18:41:00
3 1 c 3 2008-07-17 15:40:00
4 2 d 12 2008-06-09 12:41:00
5 2 e 50 2008-09-18 10:22:00
6 3 f 0 2008-10-03 13:36:00
I can aggregate the counts with the following code:
transform(df, aggregated_count = ave(comment_count, member_id, FUN = cumsum))
But I want a lag of 1 in the cumulated data, or I want cumsum to ignore the current row. The result should be:
row. member_id entry_id comment_count timestamp previous_comments
1 1 a 4 2008-06-09 12:41:00 0
2 1 b 1 2008-07-14 18:41:00 4
3 1 c 3 2008-07-17 15:40:00 5
4 2 d 12 2008-06-09 12:41:00 0
5 2 e 50 2008-09-18 10:22:00 12
6 3 f 0 2008-10-03 13:36:00 0
Some idea how I can do this in R? Maybe even with a lag bigger than 1 ?
Data for reproducibility:
# dput(df)
structure(list(member_id = c(1L, 1L, 1L, 2L, 2L, 3L), entry_id = c("a",
"b", "c", "d", "e", "f"), comment_count = c(4L, 1L, 3L, 12L,
50L, 0L), timestamp = c("2008-06-09 12:41:00", "2008-07-14 18:41:00",
"2008-07-17 15:40:00", "2008-06-09 12:41:00", "2008-09-18 10:22:00",
"2008-10-03 13:36:00")), .Names = c("member_id", "entry_id",
"comment_count", "timestamp"), row.names = c("1", "2", "3", "4",
"5", "6"), class = "data.frame")
You can use 0 for the first element, and remove the last element using head(, -1)
transform(df, previous_comments=ave(comment_count, member_id,
FUN = function(x) cumsum(c(0, head(x, -1)))))
# member_id entry_id comment_count timestamp previous_comments
#1 1 a 4 2008-06-09 12:41:00 0
#2 1 b 1 2008-07-14 18:41:00 4
#3 1 c 3 2008-07-17 15:40:00 5
#4 2 d 12 2008-06-09 12:41:00 0
#5 2 e 50 2008-09-18 10:22:00 12
#6 3 f 0 2008-10-03 13:36:00 0
You could use lag from dplyr and change the k
library(dplyr)
df %>%
group_by(member_id) %>%
mutate(previous_comments=lag(cumsum(comment_count),k=1, default=0))
# member_id entry_id comment_count timestamp previous_comments
#1 1 a 4 2008-06-09 12:41:00 0
#2 1 b 1 2008-07-14 18:41:00 4
#3 1 c 3 2008-07-17 15:40:00 5
#4 2 d 12 2008-06-09 12:41:00 0
#5 2 e 50 2008-09-18 10:22:00 12
#6 3 f 0 2008-10-03 13:36:00 0
Or using data.table
library(data.table)
setDT(df)[,previous_comments:=c(0,cumsum(comment_count[-.N])) , member_id]
Just subtract comment_count from ave :
transform(df,
aggregated_count = ave(comment_count, member_id, FUN = cumsum) - comment_count)
来源：https://stackoverflow.com/questions/27649206/cumulative-sum-with-lag