Reputation: 845
I have a data set that I would like to separate by 10-day intervals. For example, I would like to get all of the dates 26-12-2010
to 04-01-2011
for ID
1 together than the next 10-days for ID
1 together. I would like to do this for each ID
, and compile the 10-day intervals into a list.
library(lubridate)
date <- rep_len(seq(dmy("26-12-2010"), dmy("20-12-2013"), by = "days"), 500)
ID <- rep(seq(1, 5), 100)
df <- data.frame(date = date,
x = runif(length(date), min = 60000, max = 80000),
y = runif(length(date), min = 800000, max = 900000),
ID)
df %>%
mutate(interval = map(1:50, ~rep(.x, 10)) %>% reduce(c)) %>%
group_split(interval) %>%
map(~arrange(.x, ID)) %>%
map(~ group_split(.x, ID)) %>%
head(2)
)
When using the last lines of code, it breaks the days
and ID
s but the observations that are suppose to be within 10-days are not being grouped together.
Upvotes: 2
Views: 249
Reputation: 21908
I've had difficulty understanding your desired output yesterday, but I have no idea why you don't start by arranging all ID
s first. I hope this is what you are looking for:
library(dplyr)
library(magrittr)
# slicing first 2 elements only
df %>%
arrange(ID) %>%
mutate(cut = data.table::rleid(cut(date, breaks = "10 day"))) %>%
group_split(ID, cut) %>%
extract(1:2)
[[1]]
# A tibble: 2 x 5
date x y ID cut
<date> <dbl> <dbl> <int> <int>
1 2010-12-26 73719. 803002. 1 1
2 2010-12-31 66825. 870527. 1 1
[[2]]
# A tibble: 2 x 5
date x y ID cut
<date> <dbl> <dbl> <int> <int>
1 2011-01-05 63023. 807545. 1 2
2 2011-01-10 76356. 875837. 1 2
Upvotes: 1