Using dplyr mutate function to create new variable conditionally based on current row - r

Question

I am working on creating conditional averages for a large data set that involves # of flu cases seen during the week for several years. The data is organized as such:
What I want to do is create a new column that tabulates that average number of cases for that same week in previous years. For instance, for the row where Week.Number is 1 and Flu.Year is 2017, I would like the new row to give the average count for any year with Week.Number==1 & Flu.Year<2017. Normally, I would use the case_when() function to conditionally tabulate something like this. For instance, when calculating the average weekly volume I used this code:
mutate(average = case_when(
Flu.Year==2016 ~ mean(chcc$count[chcc$Flu.Year==2016]),
Flu.Year==2017 ~ mean(chcc$count[chcc$Flu.Year==2017]),
Flu.Year==2018 ~ mean(chcc$count[chcc$Flu.Year==2018]),
Flu.Year==2019 ~ mean(chcc$count[chcc$Flu.Year==2019]),
),
However, since there are four years of data * 52 weeks which is a lot of iterations to spell out the conditions for. Is there a way to elegantly code this in dplyr? The problem I keep running into is that I want to call values in counts column based on Week.Number and Flu.Year values in other rows conditioned on the current value of Week.Number and Flu.Year, and I am not sure how to accomplish that. Please let me know if there is further information / detail I can provide.
Thanks,
Steven
dat <- tibble( Flu.Year = rep(2016:2019,each = 52), Week.Number = rep(1:52,4), count = sample(1000, size=52*4, replace=TRUE) )

Answer 1

It's bad-form and, in some cases, an error when you use $-indexing within dplyr verbs.
I think a better way to get that average field is to group_by(Flu.Year) and calculate it straight-up.
library(dplyr)
set.seed(42)
dat <- tibble(
Flu.Year = sample(2016:2020, size=100, replace=TRUE),
count = sample(1000, size=100, replace=TRUE)
)
dat %>%
group_by(Flu.Year) %>%
mutate(average = mean(count)) %>%
# just to show a quick summary
slice(1:3) %>%
ungroup()
# # A tibble: 15 x 3
# Flu.Year count average
# <int> <int> <dbl>
# 1 2016 734 578.
# 2 2016 356 578.
# 3 2016 411 578.
# 4 2017 217 436.
# 5 2017 453 436.
# 6 2017 920 436.
# 7 2018 963 558
# 8 2018 609 558
# 9 2018 536 558
# 10 2019 943 543.
# 11 2019 740 543.
# 12 2019 536 543.
# 13 2020 627 494.
# 14 2020 218 494.
# 15 2020 389 494.
An alternative approach is to generate a summary table (just one row per year) and join it back in to the original data.
dat %>%
group_by(Flu.Year) %>%
summarize(average = mean(count))
# # A tibble: 5 x 2
# Flu.Year average
# <int> <dbl>
# 1 2016 578.
# 2 2017 436.
# 3 2018 558
# 4 2019 543.
# 5 2020 494.
dat %>%
group_by(Flu.Year) %>%
summarize(average = mean(count)) %>%
full_join(dat, by = "Flu.Year")
# # A tibble: 100 x 3
# Flu.Year average count
# <int> <dbl> <int>
# 1 2016 578. 734
# 2 2016 578. 356
# 3 2016 578. 411
# 4 2016 578. 720
# 5 2016 578. 851
# 6 2016 578. 822
# 7 2016 578. 465
# 8 2016 578. 679
# 9 2016 578. 30
# 10 2016 578. 180
# # ... with 90 more rows
The result, after chat:
tibble( Flu.Year = rep(2016:2018,each = 3), Week.Number = rep(1:3,3), count = 1:9 ) %>%
arrange(Flu.Year, Week.Number) %>%
group_by(Week.Number) %>%
mutate(year_week.average = lag(cumsum(count) / seq_along(count)))
# # A tibble: 9 x 4
# # Groups: Week.Number [3]
# Flu.Year Week.Number count year_week.average
# <int> <int> <int> <dbl>
# 1 2016 1 1 NA
# 2 2016 2 2 NA
# 3 2016 3 3 NA
# 4 2017 1 4 1
# 5 2017 2 5 2
# 6 2017 3 6 3
# 7 2018 1 7 2.5
# 8 2018 2 8 3.5
# 9 2018 3 9 4.5

Answer 2

We can use aggregate from base R
aggregate(count ~ Flu.Year, data, FUN = mean)