Grouping and Running a For Loop in R - r

Question

I'm looking to include a group statement within my for loop and I'm having difficulty finding any details into how to properly do this.
The example below , calculates the Extra, Outstanding and Current Column within my loop statement. I'm trying to group by id so that the loop will restart with every id. My current code:
dat <- tibble(
id = c("A","A","A","A","A","A","B","B"),
rn= c(1,2,3,4,5,6,1,2),
current = c(100,0,0,0,0,0,500,0),
paid = c(10,12,12,13,13,13,20,20),
pct_extra = c(.02,.05,.05,.07, .03, .01, .09,.01),
Extra = NA,
Outstanding = NA)
for(i in 1:nrow(dat)){
dat$Extra[i] <- dat$current[i]*dat$pct_extra[i]
dat$Outstanding[i] <- dat$current[i] - dat$paid[i] - dat$Extra[i]
if(i < nrow(dat)){
dat$current[(i+1)] <- dat$Outstanding[i]}}
I saw other posts with this same question and they seem to revert to using dplyr. So my first attempt was:
for(i in 1:nrow(dat)){
dat%>%
group_by(id)%>%
mutate(Extra=pct_extra*(current-paid),
Outstanding=current-paid-Extra,
current=if_else(rn==1,current,lag(Outstanding)))}
But this attempt didnt actually calculate the Extra, Outstanding and current columns which my guess is because I'm not using the loop statement properly.
Does anyone have any suggestions/references on how I can include a group statement into my for loop?
Thanks!

Answer 1

A few things.
for loops (surrounding dplyr pipes) are generally not necessary with dplyr grouping, this is no exception (though we will use your for loop in a "single group at a time" way).
Even if it were, you loop with i and never use i, so you're doing the same calculation to all rows, nrow(dat) times.
Third, you aren't storing the results.
My first attempt (after realizing the rolling nature of this) was to try to adapt slider::slide to it, but unfortunately I couldn't get it to work.
In older-dplyr, I would dat %>% group_by(id) %>% do({...}), but they've superseded do in lieu of across and multi-row summarize (which I could not figure out how to make do this).
So then I realized that your for loop works fine, it just needs to be applied one group at a time.
func <- function(z) {
for (i in seq_len(nrow(z))) {
z$Extra[i] <- z$current[i]*z$pct_extra[i]
z$Outstanding[i] <- z$current[i] - z$paid[i] - z$Extra[i]
if (i < nrow(z)) {
z$current[(i+1)] <- z$Outstanding[i]
}
}
z
}
library(dplyr)
library(tidyr) # nest, unnest
library(purrr) # map, can be done with base::Map as well
dat %>%
group_by(id) %>%
nest(quux = -id) %>%
mutate(quux = map(quux, func)) %>%
unnest(quux) %>%
ungroup()
# # A tibble: 8 x 7
# id rn current paid pct_extra Extra Outstanding
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 A 1 100 10 0.02 2 88
# 2 A 2 88 12 0.05 4.4 71.6
# 3 A 3 71.6 12 0.05 3.58 56.0
# 4 A 4 56.0 13 0.07 3.92 39.1
# 5 A 5 39.1 13 0.03 1.17 24.9
# 6 A 6 24.9 13 0.01 0.249 11.7
# 7 B 1 500 20 0.09 45 435
# 8 B 2 435 20 0.01 4.35 411.