R - Identify and remove duplicate rows based on two columns

Question

I have some data that looks like this:

Course_ID   Text_ID
33          17
33          17
58          17
5           22
8           22
42          25
42          25
17          26
17          26
35          39
51          39

Not having a background in programming, I'm finding it tricky to articulate my question, but here goes: I only want to keep rows where Course_ID varies but where Text_ID is the same. So for example, the final data would look something like this:

Course_ID   Text_ID
5           22
8           22
35          39
51          39

As you can see, Text_ID 22 and 39 are the only ones that have different Course_ID values. I suspect subsetting the data would be the way to go, but as I said, I'm quite a novice at this kind of thing and would really appreciate any advice on how to approach this.

Answer 1

Select those groups where there is no repeats of Course_ID.

In dplyr you can write this as -

library(dplyr)
df %>% group_by(Text_ID) %>% filter(n_distinct(Course_ID) == n()) %>% ungroup

#  Course_ID Text_ID
#      <int>   <int>
#1         5      22
#2         8      22
#3        35      39
#4        51      39

and in data.table -

library(data.table)
setDT(df)[, .SD[uniqueN(Course_ID) == .N], Text_ID]

Answer 2

You can use ave testing if not anyDuplicated.

x[ave(x$Course_ID, x$Text_ID, FUN=anyDuplicated)==0,]
#   Course_ID Text_ID
#4          5      22
#5          8      22
#10        35      39
#11        51      39

Data:

x <- read.table(header=TRUE, text="Course_ID   Text_ID
33          17
33          17
58          17
5           22
8           22
42          25
42          25
17          26
17          26
35          39
51          39")

Answer 3

Here is my approach with rlist and dplyr:

library(dplyr)

your_data %>%
  split(~ Text_ID) %>%
  rlist::list.filter(length(unique(Course_ID)) == length(Course_ID)) %>%
  bind_rows()

Returns:

# A tibble: 4 x 2
  Course_ID Text_ID
      <dbl>   <dbl>
1         5      22
2         8      22
3        35      39
4        51      39

# Data used:
your_data <- structure(list(Course_ID = c(33, 33, 58, 5, 8, 42, 42, 17, 17, 35, 51), Text_ID = c(17, 17, 17, 22, 22, 25, 25, 26, 26, 39, 39)), row.names = c(NA, -11L), class = c("tbl_df", "tbl", "data.frame"))