There is my dataset. I want to make group numbers depending on idx
, diff
. Exactly, I want to make the same number until diff
over 14 days. It means that if the same idx
, under diff
14 days, it should be the same group. But if they have the same idx, over 14 days, it should be different group.
idx = c("a","a","a","a","b","b","b","c","c","c","c")
date = c(20201115, 20201116, 20201117, 20201105, 20201107, 20201110, 20210113, 20160930, 20160504, 20160913, 20160927)
group = c("1","1","1","1","2","2","3","4","5","6","6")
df = data.frame(idx,date,group)
df <- df %>% arrange(idx,date)
df$date <- as.Date(as.character(df$date), format='%Y%m%d')
df <- df %>% group_by(idx) %>%
mutate(diff = date - lag(date))
This is the result of what I want.
CodePudding user response:
Use cumsum
to create another group criteria, and then cur_group_id()
.
library(dplyr)
df %>%
group_by(idx) %>%
mutate(diff = difftime(date, lag(date, default = first(date)), unit = "days"),
cu = cumsum(diff >= 14)) %>%
group_by(idx, cu) %>%
mutate(group = cur_group_id()) %>%
ungroup() %>%
select(-cu)
# A tibble: 11 × 4
idx date group diff
<chr> <date> <int> <drtn>
1 a 2020-11-05 1 0 days
2 a 2020-11-15 1 10 days
3 a 2020-11-16 1 1 days
4 a 2020-11-17 1 1 days
5 b 2020-11-07 2 0 days
6 b 2020-11-10 2 3 days
7 b 2021-01-13 3 64 days
8 c 2016-05-04 4 0 days
9 c 2016-09-13 5 132 days
10 c 2016-09-27 6 14 days
11 c 2016-09-30 6 3 days
CodePudding user response:
Given that the first value of diff
must be NA
because of the use of lag()
, you could use cumsum(diff >= 14 | is.na(diff)
without grouping to create the new group:
library(dplyr)
df %>%
group_by(idx) %>%
mutate(diff = date - lag(date)) %>%
ungroup() %>%
mutate(group = cumsum(diff >= 14 | is.na(diff)))
# # A tibble: 11 × 4
# idx date diff group
# <chr> <date> <drtn> <int>
# 1 a 2020-11-05 NA days 1
# 2 a 2020-11-15 10 days 1
# 3 a 2020-11-16 1 days 1
# 4 a 2020-11-17 1 days 1
# 5 b 2020-11-07 NA days 2
# 6 b 2020-11-10 3 days 2
# 7 b 2021-01-13 64 days 3
# 8 c 2016-05-04 NA days 4
# 9 c 2016-09-13 132 days 5
# 10 c 2016-09-27 14 days 6
# 11 c 2016-09-30 3 days 6