Subsetting data by date range across years in R - r

Question

I have a long term sightings data set of identified individuals (~16,000 records from 1979- 2019) and I would like to subset the same date range (YYYY-09-01 to YYYY(+1)-08-31) across years in R. I have successfully done so for each "year" (and obtained the unique IDs) using:
library(dplyr)
library(lubridate)
year79 <-data%>%
select(ID, Sex, AgeClass, Age, Date, Month, Year)%>%
filter(Date>= as.Date("1978-09-01") & Date<= as.Date("1979-08-31")) %>%
filter(!duplicated(ID))
year80 <-data%>%
select(ID, Sex, AgeClass, Age, Date, Month, Year)%>%
filter(Date>= as.Date("1979-09-01") & Date<= as.Date("1980-08-31")) %>%
filter(!duplicated(ID))
I would like to clean up the code and ideally not need to specify the each range (just have it iterate through). I am new at R and stuck how to do this. Any suggestions?
FYI "Month" and "Year" are included for producing a table via melt and cast later on.
example data:
ID Year Month Day Date AgeClass Age Sex
1 1034 1979 4 17 1979-04-17 U 3 F
2 1127 1979 5 3 1979-05-03 A 13 F
3 1222 1979 5 3 1979-05-03 U 0 F
4 1303 1979 6 16 1979-06-16 U 0 F
5 1153 1980 4 16 1980-04-16 C 0 F
6 1014 1980 4 16 1980-04-16 U 6 F
ID Year Month Day Date AgeClass Age Sex
16428 2503 2019 5 8 2019-05-08 U NA F
16429 3760 2019 5 8 2019-05-08 A 12 F
16430 4080 2019 5 9 2019-05-09 A 9 F
16431 4095 2019 5 9 2019-05-09 A 9 U
16432 1204 2019 5 11 2019-05-11 A 37 F
16433 1204 2019 5 11 2019-05-11 A NA F
#> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Answer 1

Every year has 122 days from Sept 1 to Dec 31 inclusive, so you could add a variable marking the "fiscal year" for each row:
set.seed(42)
library(dplyr)
my_data <- tibble(ID = 1:6,
Date = as.Date("1978-09-01") + c(-1, 0, 1, 364, 365, 366))
my_data
# There are 122 days from each Aug 31 (last of the FY) to the end of the CY.
# lubridate::ymd(19781231) - lubridate::ymd(19780831)
my_data %>%
mutate(FY = year(Date + 122))
## A tibble: 6 x 3
# ID Date FY
# <int> <date> <dbl>
#1 1 1978-08-31 1978
#2 2 1978-09-01 1979
#3 3 1978-09-02 1979
#4 4 1979-08-31 1979
#5 5 1979-09-01 1980
#6 6 1979-09-02 1980
You could keep the data in one table and do subsequent analysis using group_by(FY), or use %>% split(.$FY) to put each FY into its own element of a list. From my limited experience, I think it's generally an anti-pattern to create separate data frames for annual subsets of your data, as that makes your code harder to maintain, troubleshoot, and modify.