Rolling window with slide_dbl() on grouped data - r

Question

This is an extension to following question: Rolling window slider::slide() with grouped data
I want to mutate a column of my grouped tibble with slide_dbl(), i.e. applying slide_dbl() on all groups, but only within them, not across them.
When running the solution of linked question I receive following error message:
Error: Problem with `mutate()` input `rollreg`.
x Inapplicable method for 'mutate_' applied to object of class "c('double', 'numeric')".
My tibble has following structure:
tibble [450,343 x 3] (S3: grouped_df/tbl_df/tbl/data.frame)
$ company: num [1:450343] 1 1 1 1 1 ...
$ date: Date[1:450343], format: "2011-11-30" "2011-12-31" "2012-01-31" "2012-02-29" ...
$ result: num [1:450343] NA NA NA 12.5981 -2.9023 ...
- attr(*, "groups")= tibble [3,339 x 2] (S3: tbl_df/tbl/data.frame)
..$ company: num [1:3339] 1 2 3 4 5 ...
..$ .rows : list<int> [1:3339]
To complete, this is the code I ran according to the linked solution:
testtest <- data %>%
group_by(company) %>% nest() %>%
mutate(rollreg = map(data, ~ .x %>% mutate(result_2 = slide_dbl(.x = .$result, .f = ~prod(1+.)-1, .before = 11, .after = -1, complete=TRUE)))) %>%
select(-data) %>% unnest(rollreg)
Here, above mentioned error message occurs. I guess it's because of the data structure. Yet, I can't figure any solution (also not with similar functions like group_map() or group_modify()). Can anyone help? Thanks in advance!

Answer 1

An option is group_split by the grouping column (in the example, using 'case', loop over the list of datasets with map, create new column in mutate by applying the slide_dbl
library(dplyr)
library(tidyr)
library(purrr)
data %>%
group_split(case) %>%
map_dfr(~ .x %>%
mutate(out = slide_dbl(r1, .f = ~ prod(1 + .x) - 1,
.before = 5, .after = -1, complete = TRUE)))
-output
# A tibble: 30 x 6
# t case r1 r2 r3 out
# <int> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 1 a -0.294 -0.164 1.33 0
# 2 2 a 0.761 1.01 0.115 -0.294
# 3 3 a -0.781 -0.499 0.290 0.243
# 4 4 a -0.0732 -0.110 0.289 -0.728
# 5 5 a -0.528 0.707 0.181 -0.748
# 6 6 a -1.35 -0.411 -1.47 -0.881
# 7 7 a -0.397 -1.28 0.172 -1.06
# 8 8 a 1.68 0.956 -2.81 -1.02
# 9 9 a -0.0167 -0.0727 -1.08 -1.24
#10 10 a 1.25 -0.326 1.61 -1.26
## … with 20 more rows
Or if we need to use the nest_by, it creates an attribute rowwise, so, it is better to ungroup before applying
out1 <- data %>%
select(-t) %>%
nest_by(case) %>%
ungroup %>%
mutate(data = map(data, ~ .x %>%
mutate(out = slide_dbl(r1, .f = ~ prod(1 + .x) - 1,
.before = 5, .after = -1, complete = TRUE))))
-output
out1
# A tibble: 3 x 2
# case data
# <chr> <list>
#1 a <tibble [10 × 4]>
#2 b <tibble [10 × 4]>
#3 c <tibble [10 × 4]>
Now, we unnest the structure
out1 %>%
unnest(data)
# A tibble: 30 x 5
# case r1 r2 r3 out
# <chr> <dbl> <dbl> <dbl> <dbl>
# 1 a -0.294 -0.164 1.33 0
# 2 a 0.761 1.01 0.115 -0.294
# 3 a -0.781 -0.499 0.290 0.243
# 4 a -0.0732 -0.110 0.289 -0.728
# 5 a -0.528 0.707 0.181 -0.748
# 6 a -1.35 -0.411 -1.47 -0.881
# 7 a -0.397 -1.28 0.172 -1.06
# 8 a 1.68 0.956 -2.81 -1.02
# 9 a -0.0167 -0.0727 -1.08 -1.24
#10 a 1.25 -0.326 1.61 -1.26
# … with 20 more rows
data
data <- tibble(t = rep(1:10, 3),
case = c(rep("a", 10), rep("b", 10), rep("c", 10)),
r1 = rnorm(30),
r2 = rnorm(30),
r3 = rnorm(30))

Answer 2

i also got a question regarding the slide_Dbl function. I would like to check out other rollingregressions. My data is already fixed with an 8 weak week, but if i would like to look at for example 16 or 24 weeks, should i change the (before= ) from 8 to 16? The reason why i am asking is that i dont have the original dataset, but its already fixed with 8 weeks, so if i add the (before= ) with an additional 8 will it be 16?
new8 <- new%>%mutate( across(
where(is.double),
function(x) slide_dbl(x, mean, na.rm = TRUE, .before = 8L) %>% lag()))
Or should i put
new16 <- new%>%mutate(across(
where(is.double),
function(x) slide_dbl(x, mean, na.rm = TRUE, .before = 16L) %>% lag()))