dplyr: grouping and summarizing/mutating data with rolling time windows

Question

I have irregular timeseries data representing a certain type of transaction for users. Each line of data is timestamped and represents a transaction at that time. By the i

Answer 1

For simplicity reasons I recommend runner package which handles sliding window operations. In OP request window size k = 30 and windows depend on date idx = date. You can use runner function which applies any R function on given window, and sum_run

library(runner)
library(dplyr)

df %>%
  group_by(id) %>%
  arrange(date, .by_group = TRUE) %>%
  mutate(
    n_trans30 = runner(n_widgets, k = 30, idx = date, function(x) length(x)),
    n_widgets30 = sum_run(n_widgets, k = 30, idx = date),
  )

# id      date       n_widgets n_trans30 n_widgets30
#                        
# 1    2015-01-01         1         1           1
# 1    2015-01-01         2         2           3
# 1    2015-01-05         3         3           6
# 1    2015-01-25         4         4          10
# 1    2015-02-15         4         2           8
# 2    2015-01-01         2         1           2
# 2    2015-05-05         5         1           5
# 3    2015-08-01         4         1           4
# 4    2015-01-01         5         1           5

Important: idx = date should be in ascending order.

For more go to documentation and vignettes