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

I found a way to do this while working on this question

df <- data.frame(
  id = c(1, 1, 1, 1, 1, 2, 2, 3, 4),
  date = c("2015-01-01", 
           "2015-01-01", 
           "2015-01-05", 
           "2015-01-25",
           "2015-02-15",
           "2015-05-05", 
           "2015-01-01", 
           "2015-08-01", 
           "2015-01-01"),
  n_widgets = c(1,2,3,4,4,5,2,4,5)
)

count_window <- function(df, date2, w, id2){
  min_date <- date2 - w
  df2 <- df %>% filter(id == id2, date >= min_date, date <= date2)
  out <- length(df2$date)
  return(out)
}
v_count_window <- Vectorize(count_window, vectorize.args = c("date2","id2"))

sum_window <- function(df, date2, w, id2){
  min_date <- date2 - w
  df2 <- df %>% filter(id == id2, date >= min_date, date <= date2)
  out <- sum(df2$n_widgets)
  return(out)
}
v_sum_window <- Vectorize(sum_window, vectorize.args = c("date2","id2"))

res <- df %>% mutate(date = ymd(date)) %>% 
  mutate(min_date = date - 30,
         n_trans = v_count_window(., date, 30, id),
         total_widgets = v_sum_window(., date, 30, id)) %>% 
  select(id, date, n_widgets, n_trans, total_widgets)
res


id       date n_widgets n_trans total_widgets

1  1 2015-01-01         1       2             3
2  1 2015-01-01         2       2             3
3  1 2015-01-05         3       3             6
4  1 2015-01-25         4       4            10
5  1 2015-02-15         4       2             8
6  2 2015-05-05         5       1             5
7  2 2015-01-01         2       1             2
8  3 2015-08-01         4       1             4
9  4 2015-01-01         5       1             5

This version is fairly case specific but you could probably make a version of the functions that is more general.