计算行之间的行数 [英] Counting number of rows between rows

问题描述

我有一个包含四列的数据框：user_id，事件和时间

I have a data frame with four columns: user_id, event, and time

User_id一个user_id，事件是 A或 B，并且时间是时间。我需要计算在每个 A值之前出现的 B值的数量。因此，如果在第一个 A之前出现3个 B值，则该 A实例将获得一个值为3的新列。如果在下一个 B值之前存在25个 B实例一个，那么它将得到25的值。我认为自己是一位可靠的R / dplyr熟练手，但这让我感到难过！

User_id a user_id, event is either "A" or "B", and time is time. I need to count the number of "B" values that occur before each "A" value. So if there are 3 "B" values that occur before the first "A" then that instance of "A" will get a new column with a value of 3. If there are 25 instances of "B" before the next values of "A" then that will get a value of 25. I consider myself a solid R/dplyr journeyman but this has me stumped! Thanks.

user_id   event   date_time    desired_column
1         B       2018-01-01   NA
1         B       2018-01-02   NA
1         B       2018-01-03   NA
1         B       2018-01-04   NA
1         B       2018-01-05   NA
1         A       2018-01-06   5
1         B       2018-01-07   NA
1         A       2018-01-08   1
2         B       2018-01-05   NA
2         B       2018-01-06   NA
2         A       2018-01-07   2
2         B       ...          NA
2         A       ...          1

推荐答案

x <- read.table(header=TRUE, stringsAsFactors=FALSE, text='
user_id   event   date_time    desired_column
1         B       2018-01-01   NA
1         B       2018-01-02   NA
1         B       2018-01-03   NA
1         B       2018-01-04   NA
1         B       2018-01-05   NA
1         A       2018-01-06   5
1         B       2018-01-07   NA
1         A       2018-01-08   1
2         B       2018-01-05   NA
2         B       2018-01-06   NA
2         A       2018-01-07   2')

也许有点笨重，但是...

Perhaps a little clunky, but ...

（编辑：指定 dplyr :: lag ，因为 stats :: lag 不能满足我们的需要。）

(edit: specified dplyr::lag, since stats::lag doesn't do what we need.)

x$a <- NA
x$a[cumsum(rle(x$event)$lengths)] <- rle(x$event)$lengths
x$a <- dplyr::lag(x$a)
x$a[x$event == "B"] <- NA

x
#    user_id event  date_time desired_column  a
# 1        1     B 2018-01-01             NA NA
# 2        1     B 2018-01-02             NA NA
# 3        1     B 2018-01-03             NA NA
# 4        1     B 2018-01-04             NA NA
# 5        1     B 2018-01-05             NA NA
# 6        1     A 2018-01-06              5  5
# 7        1     B 2018-01-07             NA NA
# 8        1     A 2018-01-08              1  1
# 9        2     B 2018-01-05             NA NA
# 10       2     B 2018-01-06             NA NA
# 11       2     A 2018-01-07              2  2

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