Rolling mean in fixed intervals in R - r

Question

I would like to calculate a rolling average for a fixed interval in my dataset.
start end value value_per_unit
4 20 20 1.25
21 33 40 3.33
34 45 30 2.73
46 60 10 0.71
I would like to obtain the value for a fixed interval of 10 as follows:
start end value_per_unit
4 13 1.25
14 23 1.874
24 33 3.33
.
.
Where:
for the interval c(4, 14): (1.25*10)/10 = 1.25
for the interval c(15, 25): (1.25*7 + 3.33*3)/10 = 1.874
for the interval c(26,36): (10*3.33)/10 = 3.33
Is it possible to achieve this in R?

Answer 1

There seems to be some confusion in your question, however this approach gives the desired output:
library(dplyr, warn.conflicts = FALSE)
# Create the test data
df <- tribble(
~start, ~end, ~value, ~value_per_unit,
4 , 20, 20, 1.25,
21 , 33, 40, 3.33,
34 , 45, 30, 2.73,
46 , 60, 10, 0.71
)
# Some data prep to apply the transformation
df1 <- df %>%
rowwise() %>%
mutate(row = list(seq(from = start, to = end))) %>%
ungroup() %>%
tidyr::unnest(row) %>%
mutate(group = (row - 4) %/% 10)
# This hopefully illustrates what's happening here:
print(df1, n = 20)
#> # A tibble: 57 x 6
#> start end value value_per_unit row group
#> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 4 20 20 1.25 4 0
#> 2 4 20 20 1.25 5 0
#> 3 4 20 20 1.25 6 0
#> 4 4 20 20 1.25 7 0
#> 5 4 20 20 1.25 8 0
#> 6 4 20 20 1.25 9 0
#> 7 4 20 20 1.25 10 0
#> 8 4 20 20 1.25 11 0
#> 9 4 20 20 1.25 12 0
#> 10 4 20 20 1.25 13 0
#> 11 4 20 20 1.25 14 1
#> 12 4 20 20 1.25 15 1
#> 13 4 20 20 1.25 16 1
#> 14 4 20 20 1.25 17 1
#> 15 4 20 20 1.25 18 1
#> 16 4 20 20 1.25 19 1
#> 17 4 20 20 1.25 20 1
#> 18 21 33 40 3.33 21 1
#> 19 21 33 40 3.33 22 1
#> 20 21 33 40 3.33 23 1
#> # ... with 37 more rows
# Summarise to create new values of start, end and value_per_unit
df1 %>%
group_by(group) %>%
summarise(
start = min(row),
end = max(row),
value_per_unit = mean(value_per_unit),
.groups = "drop"
) %>%
select(-group)
#> # A tibble: 6 x 3
#> start end value_per_unit
#> <int> <int> <dbl>
#> 1 4 13 1.25
#> 2 14 23 1.87
#> 3 24 33 3.33
#> 4 34 43 2.73
#> 5 44 53 1.11
#> 6 54 60 0.71
Created on 2021-10-12 by the reprex package (v2.0.0)

Answer 2

Here is another solution:
library(tidyverse)
seq(4, 60, 10) %>%
enframe(value = "start") %>%
mutate(end = pmin(start + 9, max(df$end))) %>%
{map2(.$start, .$end, ~ c(.x:.y))} %>%
map_dfc(~ df %>%
rowwise() %>%
mutate(cnt = length(intersect(.x, seq(start, end, 1)))) %>%
pull(cnt)) %>%
bind_cols(as_tibble(df$value_per_unit)) %>%
summarise(across(matches("\\d+"), ~ sum(.x * value) / 10)) %>%
pivot_longer(everything(), names_to = "name",
values_to = "weighted_avg",
names_pattern = ".*(\\d+)")
# A tibble: 6 x 2
name weighted_avg
<chr> <dbl>
1 1 1.25
2 2 1.87
3 3 3.33
4 4 2.73
5 5 1.11
6 6 0.497