R: Finding Out Which Students are Improving Their Grade - r

Question

I am working with the R programming language.
Suppose I have the following dataset of student grades:
my_data = data.frame(id = c(1,1,1,1,1,2,2,2,3,3,3,3), year = c(2010,2011,2012,2013, 2014, 2008, 2009, 2010, 2018, 2019, 2020, 2021), grade = c(55, 56, 61, 61, 62, 90,89,89, 67, 87, 51, 65))
> my_data
id year grade
1 1 2010 55
2 1 2011 56
3 1 2012 61
4 1 2013 61
5 1 2014 62
6 2 2008 90
7 2 2009 89
8 2 2010 89
9 3 2018 67
10 3 2019 87
11 3 2020 51
12 3 2021 65
My Question: I want to find out which students improved their grades (or kept the same grade) from year to year, and which students got worse grades from year to year.
Using the idea of "grouped window functions", I tried to write the following functions :
check_grades_improvement <- function(grades){
for(i in 2:length(grades)){
if(grades[i] < grades[i-1]){
return(FALSE)
}
}
return(TRUE)
}
check_grades_decline <- function(grades){
for(i in 2:length(grades)){
if(grades[i] > grades[i-1]){
return(FALSE)
}
}
return(TRUE)
}
Then, I tried to apply these functions to my dataset:
improving_students <- my_data %>% group_by(id) %>%
filter(check_grades_improvement(grade)) %>%
select(id) %>%
unique()
worse_students <- my_data %>%
group_by(id) %>%
filter(check_grades_decline(grade)) %>%
select(id) %>%
unique()
But I am getting empty results
Can someone please show me what I am doing wrong and how I can fix this?
Thanks!

Answer 1

Something like this:
library(dplyr)
my_data %>%
group_by(id) %>%
mutate(x = grade-lag(grade, default = grade[1])) %>%
mutate(peformance = case_when(x == 0 ~ "kept_same",
x > 0 ~ "improved",
x < 0 ~ "got_worse",
TRUE ~ NA_character_), .keep="unused")
id year grade peformance
<dbl> <dbl> <dbl> <chr>
1 1 2010 55 kept_same
2 1 2011 56 improved
3 1 2012 61 improved
4 1 2013 61 kept_same
5 1 2014 62 improved
6 2 2008 90 kept_same
7 2 2009 89 got_worse
8 2 2010 89 kept_same
9 3 2018 67 kept_same
10 3 2019 87 improved
11 3 2020 51 got_worse
12 3 2021 65 improved

Answer 2

If we want to break the function at the first instance
check_grades_improvement <- function(grades){
for(i in 2:length(grades)){
if(grades[i] < grades[i-1]){
return(FALSE)
break
}
}
return(TRUE)
}
check_grades_decline <- function(grades){
for(i in 2:length(grades)){
if(grades[i] > grades[i-1]){
return(FALSE)
break
}
}
return(TRUE)
}
-testing
my_data %>%
group_by(id) %>%
filter(check_grades_improvement(grade)) %>%
ungroup %>%
select(id) %>%
unique()
# A tibble: 1 × 1
id
<dbl>
1 1
my_data %>%
group_by(id) %>%
filter(check_grades_decline(grade)) %>%
ungroup() %>%
select(id) %>%
unique()
# A tibble: 1 × 1
id
<dbl>
1 2
Or if it is for all instances
my_data %>%
arrange(id, year) %>%
group_by(id) %>%
filter(c(FALSE, diff(grade) > 0)) %>%
ungroup %>%
select(id) %>%
unique
# A tibble: 2 × 1
id
<dbl>
1 1
2 3
my_data %>%
arrange(id, year) %>%
group_by(id) %>%
filter(c(FALSE, diff(grade) < 0)) %>%
ungroup %>%
select(id) %>%
unique
# A tibble: 2 × 1
id
<dbl>
1 2
2 3