use grouping variable as input in group_map() function - r

Question

I have a data frame of 30 years of a response variable. I want to write a code that will subset that df into "x" number of years "n" times, and run a regression of the response in all of those subsets.
So if we started with 30 years, x=5 & n=2, we would end with 2 regressions, each using 5 random years out of the available 30. I wrote a function for that here:
# build df
df = data.frame(year=c(1:30),
response = runif(30,1,100))
# create function
subsample <- function(df, x, n ){
df %>%
# collaplse the tibble
nest(data=everything()) %>%
# repeat the tibble for number of simulations
slice(rep(1:n(), each = n)) %>%
# add group number, which will be the "nth" trial
mutate(group = c(1:n)) %>%
# expand data
unnest(cols = c(data)) %>%
# group by group number, then subsample n times from each group
group_by(group) %>%
group_map(~ sample_n(.x, x, replace = F)) %>%
# stitch back together and add group number col back
bind_rows(.id="trial") %>%
# arrange by group and year
mutate(trial=as.numeric(trial)) %>%
arrange(trial,year) %>%
# group by subsample and run regression
group_by(trial) %>%
do({
mod = lm(response ~ year, data = .)
data.frame(Intercept = coef(mod)[1],
Slope = coef(mod)[2])
})
}
# test function
subsample(df, x=5, n=2)
# A tibble: 2 x 3
# Groups: simulation [2]
trial Intercept Slope
<dbl> <dbl> <dbl>
1 1 48.5 -0.895
2 2 35.4 -0.275
Great, so that works, and we get two regressions (all I want is slope and intercept) each using a subset of 5 out of 30 years.
However, now I want to do this with every possible combination of years (so x = c(2:30)), ending with a df that should look like this
# A tibble: 2 x 3
number_of_years trial Intercept Slope
<dbl> <dbl> <dbl> <dbl>
1 2 1 48.5 -0.895
2 2 2 35.4 -0.275
3 3 1 55.2 0.333
4 3 2 34.1 0.224
5 4 1 63.2 -0.359
6 4 2 45.5 -0.241
7 5 1 43.1 0.257
8 5 2 37.9 -0.657
9 6 1 51.0 -0.456
10 6 2 65.6 0.126
This would be showing regression values of 2 trials ("n") each using 2 random years (number_of_years, "x"), then 2 trials using 3 random years, 4 random years, etc... all the way until 30.
So I tried to follow the same logic as above, but now trying map_group() with the custom function that I built:
df %>%
# collaplse the tibble
nest(data=everything()) %>%
# repeat the tibble for the number of simulations we want to test (29, in this case)
slice(rep(1:n(), each = (nrow(df)-1))) %>%
# add column for number out of total and unnest
mutate(number_of_years = c(2:(nrow(.)+1))) %>%
select(number_of_years,data) %>% #reorder
unnest(cols =c(data)) %>%
# group by out of total
group_by(number_of_years) %>%
group_map( ~ subsample(.x, x=5, n=2,))
### this is the problematic line!
### this is giving us 2 trials (n=2) of a regression, each using
### x=5 years of sampling. but instead of x=5 years, I want x=number_of_years
### so x should be the same as the grouping variable.
So the problem here is, since my subsample() function needs 3 inputs (df, x, n), I need to figure out how to make "x" the same as the grouping variable for the dataset. x should be (number_of_years). I've tried to do group_map( ~ subsample(.x,.x$number_of_years,2) and variations like that, but I can't figure out how to make it return 30 tibbles of 2 trials each, meaning 2 regressions of subsamples of the original df, but each one using a different number of years to calculate the regression.
I would like to stay in the tidyverse/ dplyr/ purr workflow if possible.
Thanks!