How to join and bind similar dataframes in R, iterative solutions welcome - r

Question

In R markdown through R Studio (R v. 4.0.3), I'm looking for a better solution to combining similarly structured dataframes while keeping all rows and matching entries on a key. Piping full_join() into a filter into a bind_rows() directly wasn't working, possibly because of the error message:
Error: Can't combine ..1$term_code 'character> and ..2$term_code '<integer.
I have 23 dataframes (let's call these "semester data") of data I'm looking to combine into a single dataframe (intended to be a single dataset of individuals outcomes from semester-to-semester).
Each semester dataframe is roughly 3000-4000 observations (individuals) with 45-47 variables of relevant data. A simplified example of a semester (or term) dataframe is shown below.
Simplified example of a "semester" dataframe:
id
ACT_math
course_code
section_code
term_code
grade
term_GPA
0001
23
101
001
FA12
3.45
3.8
0002
28
201
003
FA12
3.2
3.4
Individuals will show up in multiple semester dataframes as they progress through the program (taking course 101 in the fall and course 102 in the spring).
I want to use the dplyr full_join() to match these individuals on an ID key.
Using the suffix argument, I hope to keep track of which semester and course a set of data (grade, term_GPA, etc) for an individual comes from.
There's some data (ACT score, gender, state residency, etc) that is the stable for an individual across semester dataframes. Ideally I could take the first input and drop the rest, but if I had to clean this afterwards, that's fine.
I started by defining an object programatic_database using the first semester of data SP11. To cut down on the duplication of stable data for an individual, I selected the relevant columns that I wanted to join.
programmatic_database <- programmatic_database %>%
full_join(select(fa12, id, course_code, section_code, grade, term_gpa), by = "id", copy = TRUE, suffix = c(".sp11", ".fa12"), keep = FALSE, name = "id")
However, every semester new students join the program. I would like to add these entries to the bottom of the growing programmatic_database.
I'm also looking to use rbind() or bind_rows() to add these individuals to the bottom of the programmatic_database, along with their relevant data.
After full_join(), I'm filtering out the entries that have already been added horizontally to the dataframe, then piping the remaining entries into bind_rows()
programmatic_database <- fa12[!which(fa12$id %in% programmatic_database),] %>% dplyr::bind_rows(programmatic_database, fa12)
Concatenated example of what my code is producing after several iterations:
id
ACT_math
course_code
section_code
section_code.db
section_code.db.db
term_code
grade.sp11
grade.fa12
grade.sp13
grade.sp15
term_GPA.sp11
term_GPA.fa12
term_GPA.sp15
0001
23
102
001
001
001
FA12
3.45
3.8
3.0
-
3.8
3.7
-
0002
28
201
003
003
003
FA12
3.2
3.4
3.0
-
3.8
3.7
-
1020
28
201
003
003
003
FA12
3.2
3.4
-
-
3.8
3.7
-
6783
30
101
-
-
-
SP15
-
-
-
3.8
-
-
4.0
where I have successfully added horizontally for students 0001 and 0002 for outcomes in subsequent courses in subsequent semesters. I have also managed to add vertically, like with student 6783, leaving blanks for previous semesters before they enrolled but still adding the relevant columns.
Questions:
Is there a way to pipe full_join() into a filter() into a bind_rows() without running into these errors?
rbind number of columns do not match
OR
Error: Can't combine ..1$term_code 'character> and ..2$term_code '<integer.
Is there a easy way to keep certain columns and only add the suffix ".fa12" to certain columns? As you can see, the .db is piling up.
Is there any way to automate this? Loops aren't my strong suit, but I'm sure there's a better-looking code than doing each of the 23 joins/binds by hand.
Thank you for assistance!
Current code for simplicity:
#reproducible example
fa11 <- structure(list(id = c("1001", "1002", "1003",
"1013"), act6_05_composite = c(33L, 26L, 27L, 25L), course_code = c("101",
"101", "101", "101"), term_code = c("FA11", "FA11", "FA11", "FA11"
), section_code = c(1L, 1L, 1L, 1L), grade = c(4, 0, 0, 2.5
), repeat_status_flag = c(NA, "PR", NA, NA), class_code = c(1L,
1L, 1L, 1L), cum_atmpt_credits_prior = c(16, 0, 0, 0), cum_completed_credits_prior = c(0L,
0L, 0L, 0L), cum_passed_credits_prior = c(16, 0, 0, 0), cum_gpa_prior = c(0,
0, 0, 0), cum_atmpt_credits_during = c(29, 15, 18, 15), cum_completed_credits_during = c(13L,
1L, 10L, 15L), cum_passed_credits_during = c(29, 1, 14, 15),
term_gpa = c(3.9615, 0.2333, 2.3214, 2.9666), row.names = c(NA, 4L
), class = "data.frame")
sp12 <- structure(list(id = c("1007", "1013", "1355",
"2779", "2302"), act6_05_composite = c(24L, 26L, 25L, 24L,
24L), course_code = c(101L, 102L, 101L, 101L, 101L
), term_code = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_), section_code = c(1L, 1L, 1L, 1L, 1L), grade = c(2,
2.5, 2, 1.5, 3.5), repeat_status_flag = c(NA_character_,
NA_character_, NA_character_, NA_character_, NA_character_
), class_code = c(2L, 2L, 1L, 2L, 2L), cum_atmpt_credits_prior = c(44,
43, 12, 43, 30), cum_completed_credits_prior = c(41L, 43L,
12L, 43L, 12L), cum_passed_credits_prior = c(41, 43, 12,
43, 30), cum_gpa_prior = c(3.3125, 3.186, 3.5416, 3.1785,
3.8636), cum_atmpt_credits_during = c(56, 59, 25, 64, 43),
cum_completed_credits_during = c(53L, 56L, 25L, 56L, 25L),
cum_passed_credits_during = c(53, 59, 25, 64, 43), term_gpa = c(2.8333,
3.423, 3.1153, 2.1923, 3.6153), row.names = c(NA,
5L), class = "data.frame")
# make object from fall 2011 semester dataframe
programmatic_database <- fa11
# join the spring 2012 semester dataframe by id using select variables and attaching relevant suffix
programmatic_database <- programmatic_database %>%
full_join(select(sp12, id, course_code, section_code, grade, term_gpa), by = "id", copy = TRUE, suffix = c(".fa11", ".sp12"), keep = FALSE, name = "id")
#view results of join, force integer type on certain variables as needed (see error above)
#filter the joined entries from fall 2012 database, then bind the remaining entries to the bottom of the growing dataset
programmatic_database <- sp12[!which(sp12$id %in% programmatic_database),] %>% dplyr::bind_rows(programmatic_database, sp12)

Answer 1

It would be possible to use bind_rows here if you make the column types consistent between tables. For instance, you could make a function to re-type any particular columns that aren't consistent in your original data. (That might also be something you could fix upstream as you read it in.)
library(dplyr)
set_column_types <- function(df) {
df %>%
mutate(term_code = as.character(term_code),
course_code = as.character(course_code))
}
bind_rows(
fa11 %>% set_column_types(),
sp12 %>% set_column_types() %>% mutate(term_code = "SP12")
)
This will stack your data into a relatively "long" format, like below. You may want to then reshape it depending on what kind of subsequent calculations you want to do.
id act6_05_composite course_code term_code section_code grade repeat_status_flag class_code cum_atmpt_credits_prior cum_completed_credits_prior cum_passed_credits_prior cum_gpa_prior cum_atmpt_credits_during cum_completed_credits_during cum_passed_credits_during term_gpa
1 1001 33 101 FA11 1 4.0 <NA> 1 16 0 16 0.0000 29 13 29 3.9615
2 1002 26 101 FA11 1 0.0 PR 1 0 0 0 0.0000 15 1 1 0.2333
3 1003 27 101 FA11 1 0.0 <NA> 1 0 0 0 0.0000 18 10 14 2.3214
4 1013 25 101 FA11 1 2.5 <NA> 1 0 0 0 0.0000 15 15 15 2.9666
5 1007 24 101 SP12 1 2.0 <NA> 2 44 41 41 3.3125 56 53 53 2.8333
6 1013 26 102 SP12 1 2.5 <NA> 2 43 43 43 3.1860 59 56 59 3.4230
7 1355 25 101 SP12 1 2.0 <NA> 1 12 12 12 3.5416 25 25 25 3.1153
8 2779 24 101 SP12 1 1.5 <NA> 2 43 43 43 3.1785 64 56 64 2.1923
9 2302 24 101 SP12 1 3.5 <NA> 2 30 12 30 3.8636 43 25 43 3.6153