R: Filtering rows based on a group criterion - r

Question

I have a data frame with over 100,000 rows and with about 40 columns. The schools column has about 100 distinct schools. I have data from 1980 to 2023.
I want to keep all data from schools that have at least 10 rows for each of the years 2018 through 2022. Schools that do not meet that criterion should have all rows deleted.
In my minimal example, Schools, I have three schools.
Computing a table makes it apparent that only Washington should be retained. Adams only has 5 rows for 2018 and Jefferson has 0 for 2018.
Schools2 is what the result should look like.
How do I use the table computation or a dplyr computation to perform the filter?
Schools =
data.frame(school = c(rep('Washington', 60),
rep('Adams',70),
rep('Jefferson', 100)),
year = c(rep(2016, 5), rep(2018:2022, each = 10), rep(2023, 5),
rep(2017, 25), rep(2018, 5), rep(2019:2022, each = 10),
rep(2019:2023, each = 20)),
stuff = rnorm(230)
)
Schools2 =
data.frame(school = c(rep('Washington', 60)),
year = c(rep(2016, 5), rep(2018:2022, each = 10), rep(2023, 5)),
stuff = rnorm(60)
)
table(Schools$school, Schools$year)
Schools |> group_by(school, year) |> summarize(counts = n())

Answer 1

Keep only the year from 2018 to 2022 in the data with filter, then add a frequency count column by school, year, and filter only those 'school', having all count greater than or equal to 10 and if all the year from the range are present
library(dplyr)# version >= 1.1.0
Schools %>%
filter(all(table(year[year %in% 2018:2022]) >= 10) &
all(2018:2022 %in% year), .by = c("school")) %>%
as_tibble()
-output
# A tibble: 60 × 3
school year stuff
<chr> <dbl> <dbl>
1 Washington 2016 0.680
2 Washington 2016 -1.14
3 Washington 2016 0.0420
4 Washington 2016 -0.603
5 Washington 2016 2.05
6 Washington 2018 -0.810
7 Washington 2018 0.692
8 Washington 2018 -0.502
9 Washington 2018 0.464
10 Washington 2018 0.397
# … with 50 more rows
Or using count
library(magrittr)
Schools %>%
filter(tibble(year) %>%
filter(year %in% 2018:2022) %>%
count(year) %>%
pull(n) %>%
is_weakly_greater_than(10) %>%
all, all(2018:2022 %in% year) , .by = "school")

Answer 2

As it turns out, a friend just helped me come up with a base R solution.
# form 2-way table, school against year
sdTable = table(Schools$school, Schools$year)
# say want years 2018-2022 having lots of rows in school data
sdTable = sdTable[,3:7]
# which have >= 10 rows in all years 2018-2022
allGtEq = function(oneRow) all(oneRow >= 10)
whichToKeep = which(apply(sdTable,1,allGtEq))
# now whichToKeep is row numbers from the table; get the school names
whichToKeep = names(whichToKeep)
# back to school data
whichOrigRowsToKeep = which(Schools$school %in% whichToKeep)
newHousing = Schools[whichOrigRowsToKeep,]
newHousing