I have some data with groups for which I want to compute a summary (sum or mean) over a fixed number of periods. I'm trying to do this with a group_by followed by mutate and then operating with the variable and its dplyr::lag. Here is an example:
library(tidyverse)
df <- data.frame(group = rep(c("A", "B"), 5),
x = c(1, 3, 4, 7, 9, 10, 17, 29, 30, 55))
df %>%
group_by(group) %>%
mutate(cs = x + lag(x, 1, 0) + lag(x, 2, 0) + lag(x, 3, 0)) %>%
ungroup()
Which yields the desired result:
# A tibble: 10 x 3
group x cs
<fctr> <dbl> <dbl>
1 A 1 1
2 B 3 3
3 A 4 5
4 B 7 10
5 A 9 14
6 B 10 20
7 A 17 31
8 B 29 49
9 A 30 60
10 B 55 101
Is there a shorter way to accomplish this? (Here I calculated four values but I actually need twelve or more).
Perhaps you could use the purrr functions reduce and map included with the tidyverse:
library(tidyverse)
df <- data.frame(group = rep(c("A", "B"), 5),
x = c(1, 3, 4, 7, 9, 10, 17, 29, 30, 55))
df %>%
group_by(group) %>%
mutate(cs = reduce(map(0:3, ~ lag(x, ., 0)), `+`)) %>%
ungroup()
#> # A tibble: 10 x 3
#> group x cs
#> <fctr> <dbl> <dbl>
#> 1 A 1 1
#> 2 B 3 3
#> 3 A 4 5
#> 4 B 7 10
#> 5 A 9 14
#> 6 B 10 20
#> 7 A 17 31
#> 8 B 29 49
#> 9 A 30 60
#> 10 B 55 101
To see what's happening here it's probably easier to see with a simpler example that doesn't require a group.
v <- 1:5
lagged_v <- map(0:3, ~ lag(v, ., 0))
lagged_v
#> [[1]]
#> [1] 1 2 3 4 5
#>
#> [[2]]
#> [1] 0 1 2 3 4
#>
#> [[3]]
#> [1] 0 0 1 2 3
#>
#> [[4]]
#> [1] 0 0 0 1 2
reduce(lagged_v, `+`)
#> [1] 1 3 6 10 14