Calculate rolling sum by group [duplicate] - r

Question

This question already has answers here:
Why are my dplyr group_by & summarize not working properly? (name-collision with plyr)
(5 answers)
Closed 2 years ago.
I would like to calculate a rolling sum (or a custom function) of 3 previous values, treating each group separately. I have tried this:
require(dplyr)
# Build dataframe
df <- data.frame(person = c(rep("Peter", 5), rep("James", 5)),
score1 = c(1,3,2,5,4,6,8,4,5,3),
score2 = c(1,1,1,5,1,3,4,8,9,0))
# Attempt rolling sum by group
df %>%
group_by(person) %>%
mutate(s1_rolling = rollsumr(score1, k = 3, fill = NA),
s2_rolling = rollsumr(score2, k = 3, fill = NA))
But the new columns do not treat each group separately, instead continuing down the whole dataset:
person score1 score2 s1_rolling s2_rolling
<chr> <dbl> <dbl> <dbl> <dbl>
1 Peter 1 1 NA NA
2 Peter 3 1 NA NA
3 Peter 2 1 6 3
4 Peter 5 5 10 7
5 Peter 4 1 11 7
6 James 6 3 15 9
7 James 8 4 18 8
8 James 4 8 18 15
9 James 5 9 17 21
10 James 3 0 12 17
I would like row 6 and 7 to show NA in the two new columns, because until row 8 there is insufficient James data to sum 3 rows.
How can I do this?

Answer 1

It could be that plyr was also loaded and the mutate from plyr masked the mutate from dplyr. We could use dplyr::mutate
library(dplyr)
library(zoo)
df %>%
group_by(person) %>%
dplyr::mutate(s1_rolling = rollsumr(score1, k = 3, fill = NA),
s2_rolling = rollsumr(score2, k = 3, fill = NA))
# A tibble: 10 x 5
# Groups: person [2]
# person score1 score2 s1_rolling s2_rolling
# <chr> <dbl> <dbl> <dbl> <dbl>
# 1 Peter 1 1 NA NA
# 2 Peter 3 1 NA NA
# 3 Peter 2 1 6 3
# 4 Peter 5 5 10 7
# 5 Peter 4 1 11 7
# 6 James 6 3 NA NA
# 7 James 8 4 NA NA
# 8 James 4 8 18 15
# 9 James 5 9 17 21
#10 James 3 0 12 17
If there are more than one column, we can also use across
df %>%
group_by(person) %>%
dplyr::mutate(across(starts_with('score'),
~ rollsumr(., k = 3, fill = NA), .names = '{col}_rolling'))
For a faster version, use RcppRoll::roll_sumr
df %>%
group_by(person) %>%
dplyr::mutate(across(starts_with('score'),
~ RcppRoll::roll_sumr(., 3, fill = NA), .names = '{col}_rolling'))
The behavior can be reproduced with plyr::mutate
df %>%
group_by(person) %>%
plyr::mutate(s1_rolling = rollsumr(score1, k = 3, fill = NA),
s2_rolling = rollsumr(score2, k = 3, fill = NA))
# A tibble: 10 x 5
# Groups: person [2]
# person score1 score2 s1_rolling s2_rolling
# <chr> <dbl> <dbl> <dbl> <dbl>
# 1 Peter 1 1 NA NA
# 2 Peter 3 1 NA NA
# 3 Peter 2 1 6 3
# 4 Peter 5 5 10 7
# 5 Peter 4 1 11 7
# 6 James 6 3 15 9
# 7 James 8 4 18 8
# 8 James 4 8 18 15
# 9 James 5 9 17 21
#10 James 3 0 12 17

Answer 2

I would suggest a slider approach with slide_dbl() function with works similar to zoo and it is compatible with dplyr:
library(slider)
library(dplyr)
#Code
# Build dataframe
df <- data.frame(person = c(rep("Peter", 5), rep("James", 5)),
score1 = c(1,3,2,5,4,6,8,4,5,3),
score2 = c(1,1,1,5,1,3,4,8,9,0))
# Attempt rolling sum by group
df %>%
group_by(person) %>%
mutate(s1_rolling = slide_dbl(score1, sum, .before = 2, .complete = TRUE),
s2_rolling = slide_dbl(score2, sum, .before = 2, .complete = TRUE))
Output:
# A tibble: 10 x 5
# Groups: person [2]
person score1 score2 s1_rolling s2_rolling
<fct> <dbl> <dbl> <dbl> <dbl>
1 Peter 1 1 NA NA
2 Peter 3 1 NA NA
3 Peter 2 1 6 3
4 Peter 5 5 10 7
5 Peter 4 1 11 7
6 James 6 3 NA NA
7 James 8 4 NA NA
8 James 4 8 18 15
9 James 5 9 17 21
10 James 3 0 12 17