R - Using grouping function inside a loop - r

Question

I have a set of data with group ids. Inside each group I have to do a calculation for the first observation, and then subsequently and sequentially calculate the remaining n observations in that group. I have the following data:
library(tidyverse)
df <- tibble(id = c(1:10),
group_id = c(rep(1,5), rep(6,2),rep(8,3)),
value1 = c(100,200,300,400,500,250,350,20,25,45),
value2 = c(50,75,150,175,200,15,25,78,99,101)
)
df <- df %>%
group_by(group_id) %>%
mutate(position = 1:n()) # Creating a position id
# A tibble: 6 x 5
# Groups: group_id [2]
id group_id value1 value2 position
<int> <dbl> <dbl> <dbl> <int>
1 1 1 100 50 1
2 2 1 200 75 2
3 3 1 300 150 3
4 4 1 400 175 4
5 5 1 500 200 5
6 6 6 250 15 1
I would like to create an "aggregation" column, that uses value1, value2, and most importantly, the previous values in itself.
I first calculate the first step:
df <- df %>%
mutate(aggregation = ifelse(position == 1, value1 * value2, 0))
# A tibble: 10 x 6
# Groups: group_id [3]
id group_id value1 value2 position aggregation
<int> <dbl> <dbl> <dbl> <int> <dbl>
1 1 1 100 50 1 5000
2 2 1 200 75 2 0
3 3 1 300 150 3 0
4 4 1 400 175 4 0
5 5 1 500 200 5 0
6 6 6 250 15 1 3750
7 7 6 350 25 2 0
8 8 8 20 78 1 1560
9 9 8 25 99 2 0
10 10 8 45 101 3 0
Then I use a loop and set the condition that if the aggregation column has 0 as a value (everything not calculated in the previous step), then I use the value1 * value2 / previous aggregation value:
for (i in 1:nrow(df)) {
df$aggregation[i] <- ifelse(df$aggregation[i] == 0, round((df$value1[i] * df$value2[i]) / lag(df$aggregation)[i],0), df$aggregation[i])
}
# A tibble: 10 x 6
# Groups: group_id [3]
id group_id value1 value2 position aggregation
<int> <dbl> <dbl> <dbl> <int> <dbl>
1 1 1 100 50 1 5000
2 2 1 200 75 2 3
3 3 1 300 150 3 15000
4 4 1 400 175 4 5
5 5 1 500 200 5 20000
6 6 6 250 15 1 3750
7 7 6 350 25 2 2
8 8 8 20 78 1 1560
9 9 8 25 99 2 2
10 10 8 45 101 3 2272
I was wondering if there was a better way to do this. I like to use dplyr, but so far, due to the necessity to calculate the values one after the other, I've been unable to find a good solution.
Most importantly, however, instead of the condition I use in the last portion, I would have liked to to the following:
df %>%
group_by(group_id) %>%
mutate(aggregation = case_when(
group_id != 1 ~ value1 * value2 / lag(aggregation),
TRUE ~ aggregation
))
However, this doesn't work in a loop. I've generally been unable to use dplyr inside a loop, especially since once group_by() is used, I feel like mutate() would be my only option, but it seems to create a conflict with the functionality of the loop itself.

Answer 1

Perhaps you could use accumulate2 from purrr, which I think may be what you're looking for.
There are 3 arguments needed for accumulate2:
For accumulate2(), a 3-argument function. The function will be passed
the accumulated result as the first argument. The next value in
sequence from .x is passed as the second argument. The next value in
sequence from .y is passed as the third argument.
So, ..1 would be the accumulated result (the previous row aggregation value), ..2 would be value1, and ..3 would be value2.
library(tidyverse)
df %>%
group_by(group_id) %>%
mutate(aggregation = accumulate2(value1[-1], value2[-1],
~ round(..2 * ..3 / ..1),
.init = value1[1] * value2[1]) %>% flatten_dbl)
Output
id group_id value1 value2 position aggregation
<int> <dbl> <dbl> <dbl> <int> <dbl>
1 1 1 100 50 1 5000
2 2 1 200 75 2 3
3 3 1 300 150 3 15000
4 4 1 400 175 4 5
5 5 1 500 200 5 20000
6 6 6 250 15 1 3750
7 7 6 350 25 2 2
8 8 8 20 78 1 1560
9 9 8 25 99 2 2
10 10 8 45 101 3 2272