Count observations over rolling 30 day window

Question

I need to create a variable that counts the number of observations that have occurred in the last 30 days for each id.

For example, imagine an observation that occurs on 1/2/2021 (d / m / y) for the id "a". If this observation is the first between 1/1/2021 and 1/2/2021 for the id "a" the variable must give 1. If it is the second, 2, etc.

Here is a larger example:

dat <- tibble::tribble(
  ~id,  ~q,   ~date,
  "a",   1,   "01/01/2021",
  "a",   1,   "01/01/2021",
  "a",   1,   "21/01/2021",
  "a",   1,   "21/01/2021",
  "a",   1,   "12/02/2021",
  "a",   1,   "12/02/2021",
  "a",   1,   "12/02/2021",
  "a",   1,   "12/02/2021",
  "b",   1,   "02/02/2021",
  "b",   1,   "02/02/2021",
  "b",   1,   "22/02/2021",
  "b",   1,   "22/02/2021",
  "b",   1,   "13/03/2021",
  "b",   1,   "13/03/2021",
  "b",   1,   "13/03/2021",
  "b",   1,   "13/03/2021")
dat$date <- lubridate::dmy(dat$date)

The result should be:

id  q   date    newvar
a   1   01/01/2021  1
a   1   01/01/2021  2
a   1   21/01/2021  3
a   1   21/01/2021  4
a   1   12/02/2021  3
a   1   12/02/2021  4
a   1   12/02/2021  5
a   1   12/02/2021  6
b   1   02/02/2021  1
b   1   02/02/2021  2
b   1   22/02/2021  3
b   1   22/02/2021  4
b   1   13/03/2021  3
b   1   13/03/2021  4
b   1   13/03/2021  5
b   1   13/03/2021  6

Thank you very much.

Answer 1

With sapply and between, count the number of observations prior to the current observation that are within 30 days.

library(lubridate)
library(dplyr)
dat %>% 
  group_by(id) %>% 
  mutate(newvar = sapply(seq(length(date)), 
                         function(x) sum(between(date[1:x], date[x] - days(30), date[x]))))

# A tibble: 16 x 4
# Groups:   id [2]
   id        q date       newvar
   <chr> <dbl> <date>      <int>
 1 a         1 2021-01-01      1
 2 a         1 2021-01-01      2
 3 a         1 2021-01-21      3
 4 a         1 2021-01-21      4
 5 a         1 2021-02-12      3
 6 a         1 2021-02-12      4
 7 a         1 2021-02-12      5
 8 a         1 2021-02-12      6
 9 b         1 2021-02-02      1
10 b         1 2021-02-02      2
11 b         1 2021-02-22      3
12 b         1 2021-02-22      4
13 b         1 2021-03-13      3
14 b         1 2021-03-13      4
15 b         1 2021-03-13      5
16 b         1 2021-03-13      6