unique words by group - r

Question

this is my example dataframe
example = data.frame(group = c("A", "B", "A", "A"), word = c("car", "sun ,sun, house", "car, house", "tree"))
I would like to get only unique words within group and through groups
So I would like to get this
group word
A car, tree
B sun
I used aggregate and get this
aggregate(word ~ group , data = example, FUN = paste0)
group word
1 A car, car, house, tree
2 B sun ,sun, house
but now i need to select only unique values, but even this does not work out
for (i in 1:nrow(cluster)) {cluster[i, ][["word"]] = lapply(unlist(cluster[i, ][["word"]]), unique)}
with
Error in `[[<-.data.frame`(`*tmp*`, "word", value = list("car", "car, house", :
replacement has 3 rows, data has 1

Answer 1

A base R option using aggregate + subset + ave like below
with(
aggregate(
word ~ .,
example,
function(x) {
unlist(strsplit(x, "[, ]+"))
}
),
aggregate(
. ~ ind,
subset(
unique(stack(setNames(word, group))),
ave(seq_along(ind), values, FUN = length) == 1
),
c
)
)
gives
ind values
1 A car, tree
2 B sun

Answer 2

Here's a dplyr solution:
library(dplyr)
library(tidyr)
example %>%
separate_rows(word) %>%
distinct(group, word) %>%
group_by(word) %>%
filter(n() == 1) %>%
group_by(group) %>%
summarise(word = toString(word))
output
group word
1 A car, tree
2 B sun

Answer 3

In base you can use strsplit to get the words, split them by group and use unique the get unique words per group. Use table to get the number of same words and take those which appear only once.
t1 <- lapply(split(strsplit(example$word, "[, ]+"), example$group),
\(x) unique(unlist(x)))
t2 <- table(unlist(t1))
t2 <- names(t2)[t2 == 1]
t1 <- lapply(t1, \(x) paste(x[x %in% t2], collapse = ", "))
data.frame(group = names(t1), word=unlist(t1))
# group word
#A A car, tree
#B B sun
Or another way starting with the already used aggregate in the question.
t1 <- aggregate(word ~ group , data = example, FUN = toString)
t2 <- lapply(strsplit(t1$word, "[, ]+"), unique)
t3 <- table(unlist(t2))
t3 <- names(t3)[t3 == 1]
t1$word <- lapply(t2, \(x) x[x %in% t3])
t1
# group word
#1 A car, tree
#2 B sun
And just for fun a Benchmark
library(bench)
library(dplyr)
library(tidyr)
library(tidyverse)
example = data.frame(group = c("A", "B", "A", "A"), word = c("car", "sun ,sun, house", "car, house", "tree"))
bench::mark(check = FALSE,
GKi = {t1 <- lapply(split(strsplit(example$word, "[, ]+"), example$group),
\(x) unique(unlist(x)))
t2 <- table(unlist(t1))
t2 <- names(t2)[t2 == 1]
t1 <- lapply(t1, \(x) paste(x[x %in% t2], collapse = ", "))
data.frame(group = names(t1), word=unlist(t1))},
GKi2 = {t1 <- aggregate(word ~ group , data = example, FUN = toString)
t2 <- lapply(strsplit(t1$word, "[, ]+"), unique)
t3 <- table(unlist(t2))
t3 <- names(t3)[t3 == 1]
t1$word <- lapply(t2, \(x) x[x %in% t3])
t1},
ThomasIsCoding = with(
aggregate(
word ~ .,
example,
function(x) {
unlist(strsplit(x, ", "))
}
),
aggregate(
. ~ ind,
subset(
unique(stack(setNames(word, group))),
ave(seq_along(ind), values, FUN = length) == 1
),
c
)
),
Mael = {example %>%
separate_rows(word) %>%
distinct(group, word) %>%
group_by(word) %>%
filter(n() == 1) %>%
group_by(group) %>%
summarise(word = toString(word))},
"Nir Graham" = {example <- data.frame(group = c("A", "B", "A", "A"),
word = c("car", "sun ,sun, house", "car, house", "tree"))
(sep_df <- separate_rows(example,word,sep = ",") |> mutate_all(trimws) |> distinct())
(uniq_df <- sep_df|> group_by(word) |> count() |> filter(n==1))
(result_df <- inner_join(sep_df,uniq_df) |> group_by(group) |> summarise(word=paste0(word,collapse=", ")))
}
)
Result
expression min median `itr/sec` mem_alloc `gc/sec` n_itr n_gc
<bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> <int> <dbl>
1 GKi 445.13µs 486.26µs 1997. 16.03KB 6.15 974 3
2 GKi2 916.97µs 968.68µs 1023. 7.3KB 6.15 499 3
3 ThomasIsCoding 3.54ms 3.73ms 266. 8.19KB 8.45 126 4
4 Mael 16.07ms 16.48ms 60.1 60.04KB 6.68 27 3
5 Nir Graham 37.29ms 39.49ms 24.0 90.59KB 8.00 9 3
GKi is about 2 times faster than GKi2, 7 times faster than ThomasIsCoding, 30 than Mael and 80 than Nir Graham.

Answer 4

library(tidyverse)
example <- data.frame(group = c("A", "B", "A", "A"),
word = c("car", "sun ,sun, house", "car, house", "tree"))
(sep_df <- separate_rows(example,word,sep = ",") |> mutate_all(trimws) |> distinct())
(uniq_df <- sep_df|> group_by(word) |> count() |> filter(n==1))
(result_df <- inner_join(sep_df,uniq_df) |> group_by(group) |> summarise(word=paste0(word,collapse=", ")))