Efficient dplyr-style operations - r

Question

I have these two toy example tables:
Table 1:
attendance_events <- data.frame(student_id = c("RA123","RB123","RC123","RA456","RB456","RC456","RA123","RB123","RC123","RA456","RB456","RC456"),
dates = c("2020-02-01","2020-02-01","2020-02-01","2020-02-01","2020-02-01","2020-02-01","2020-02-02","2020-02-02","2020-02-02","2020-02-02","2020-02-02","2020-02-02"),
attendance = c(1,1,1,0,1,1,0,0,1,0,0,1),
stringsAsFactors = F)
attendance_events
student_id dates attendance
1 RA123 2020-02-01 1
2 RB123 2020-02-01 1
3 RC123 2020-02-01 1
4 RA456 2020-02-01 0
5 RB456 2020-02-01 1
6 RC456 2020-02-01 1
7 RA123 2020-02-02 0
8 RB123 2020-02-02 0
9 RC123 2020-02-02 1
10 RA456 2020-02-02 0
11 RB456 2020-02-02 0
12 RC456 2020-02-02 1
Table2:
all_students <- data.frame(student_id = c("RA123","RB123","RC123","RA456","RB456",'RC456'),
school_id = c(1,1,1,1,1,2),
grade_level = c(10,10,9,9,11,11),
date_of_birth = c("1990-02-02","1990-02-02","1991-01-01","1991-02-01","1989-02-02","1989-02-02"),
hometown = c("farm","farm","farm","farm","farm","city"),
stringsAsFactors = F)
> all_students
student_id school_id grade_level date_of_birth hometown
1 RA123 1 10 1990-02-02 farm
2 RB123 1 10 1990-02-02 farm
3 RC123 1 9 1991-01-01 farm
4 RA456 1 9 1991-02-01 farm
5 RB456 1 11 1989-02-02 farm
6 RC456 2 11 1989-02-02 city
attendance in attendance_events is 0 if the student was absent that day.
My question is what is the most efficient way in R to find the grade_level that had the largest drop off in attendance between "2020-02-01" and "2020-02-02"
My code is:
#Only include absences because it will be a smaller dataset
att_ws_alt <- inner_join(attendance_events, all_students[,c("student_id","grade_level")], by = "student_id") %>%
filter(attendance == 0)
#Set days to check between
date_from <- "2020-02-01"
date_to <- "2020-02-02"
#Continously pipe to not have to store and reference(?)
att_drop_alt <- att_ws_alt %>%
filter(dates %in% c(date_from, date_to)) %>%
group_by(grade_level,dates) %>%
summarize(absence_bydate = n()) %>%
dcast(grade_level ~ dates) %>%
sapply(FUN = function(x) { x[is.na(x)] <- 0; x}) %>%
as.data.frame() %>%
mutate("absence_change" = .[,3] - .[,2]) %>%
select(grade_level, absence_change) %>%
arrange(desc(absence_change))
>att_drop_alt
grade_level absence_change
1 10 2
2 11 1
3 9 0
However, this feels a bit complex for what seems like a reasonably simple question. I want to see other ways R programmers could answer this question, ideally for better performance but even readability would be good to see.
Thanks community!

Answer 1

With data.table
library(data.table)
setDT(attendance_events)[all_students, .SD[, .(sum(attendance)),
.(grade_level, dates)], on = .(student_id)][,
.(attendanace_change = diff(rev(V1))), .(grade_level)]
# grade_level attendanace_change
#1: 10 2
#2: 9 0
#3: 11 1

Answer 2

I guess this is a little more concise:
left_join(attendance_events, all_students, by = "student_id") %>%
group_by(grade_level, dates) %>%
summarise(attendance = sum(attendance)) %>%
group_by(grade_level) %>%
summarize(attendance_change = diff(attendance))
#> # A tibble: 3 x 2
#> grade_level attendance_change
#> <dbl> <dbl>
#> 1 9 0
#> 2 10 -2
#> 3 11 -1
Of course, if you want to count absences instead of attendances, just put a minus sign in front of the diff on the last line.

Answer 3

Sorry if this doesn't exactly answer your question, but I wouldn't want to unfairly accuse the students of being more absent then they were ;)
library(dplyr)
all_students %>%
left_join(attendance_events) %>%
mutate(dates = as.Date(dates)) %>%
group_by(grade_level, dates) %>%
summarise(NAbs = sum(ifelse(attendance == 0, 1, 0)),
N = n(),
pctAbs = NAbs / n() * 100) %>%
arrange(dates) %>%
mutate(change = pctAbs - lag(pctAbs)) %>%
ungroup() %>%
arrange(change)
# A tibble: 6 x 6
dates grade_level NAbs N pctAbs change
<date> <dbl> <dbl> <int> <dbl> <dbl>
1 2020-02-02 9 1 2 50 0
2 2020-02-02 11 1 2 50 50
3 2020-02-02 10 2 2 100 100
4 2020-02-01 9 1 2 50 NA
5 2020-02-01 10 0 2 0 NA
6 2020-02-01 11 0 2 0 NA