R roll mean on a non continuous time serie - r

Question

I want to make a rolling mean on the last X number of days. rollmean() does that using rows. Since I am using loggers that sometimes fail, and also the data were cleaned, the time series is not continuous (rows do not necessarily represent a constant time difference).
A colleague suggested the solution below, which works great. Except my data need to be grouped (in the example by treatment). For each day, I want the rolling mean of the last X days for each treatment.
Thanks
# making some example data
# vector with days since the beginning of experiment
days <- 0:30
# random values df1 <- tibble::tibble(
days_since_beginning = days,
value_to_used = rnorm(length(days)),
treatment = sample(letters[1],31,replace = TRUE) )
df2 <- tibble::tibble(
days_since_beginning = days,
value_to_used = rnorm(length(days)),
treatment = sample(letters[2],31,replace = TRUE) )
df <- full_join(df1, df2)
# how long should be the period for mean
time_period <- 10 # calculate for last 10 days
df_mean <- df %>% dplyr::mutate(
# calculate rolling mean
roll_mean = purrr::map_dbl(
.x = days_since_beginning,
.f = ~ df %>%
# select only data for the last `time_period`
dplyr::filter(days_since_beginning >= .x - time_period &
days_since_beginning <= .x) %>%
purrr::pluck("value_to_used") %>%
mean() %>%
return()
) )

Answer 1

This takes the mean over the last 10 days by treatment. The width argument includes a computation of how many rows back to use so that it corresponds to 10 days rather than 10 rows. This uses the fact that width can be a vector.
library(dplyr)
library(zoo)
df %>%
group_by(treatment) %>%
mutate(roll = rollapplyr(value_to_used,
seq_along(days_since_beginning) - findInterval(days_since_beginning - 10, days_since_beginning),
mean)) %>%
ungroup

Answer 2

Same colleague came up with his own solution:
df_mean <-
df %>%
dplyr::group_by(treatment) %>%
tidyr::nest() %>%
dplyr::mutate(
data_with_mean = purrr::map(
.x = data,
.f = ~ {
dataset <- .x
dataset %>%
dplyr::mutate(
# calculate rolling mean
roll_mean = purrr::map_dbl(
.x = days_since_beginning,
.f = ~ dataset %>%
# select only data for the last `time_period`
dplyr::filter(days_since_beginning >= .x - time_period &
days_since_beginning <= .x) %>%
purrr::pluck("value_to_used") %>%
mean() %>%
return()
)) %>%
return()
}
)) %>%
dplyr::select(-data) %>%
tidyr::unnest(data_with_mean) %>%
dplyr::ungroup()
I compared the results with G. Grothendieck's idea, and it only matches if I use time_period in my colleague's code and time_period + 1 in G. Grothendieck's code. So there is a difference in how the time_period is used, and I am confused about why it happens.