Remove duplicates, keeping most frequent row - r

Question

I would like to deduplicate my data, keeping the row that has the most frequent appearances. If there is a tie in rows, I don't care which gets returned—the first in alphabetical or numeric order is fine. I would like to do this by group of id and var.
MRE:
df <- data.frame(
id = rep("a", 8),
var = c(rep("b", 4), rep("c", 4)),
val = c("d", "d", "d", "e", "f", "f", "g", "g")
)
> df
id var val
1 a b d
2 a b d
3 a b d
4 a b e
5 a c f
6 a c f
7 a c g
8 a c g
Should be:
id var val
1 a b d
2 a c f
I'm working with large datasets and tidyverse pipe chains, so a dplyr solution would be preferable.

Answer 1

Use table and which.max to extract the mode:
df %>%
group_by(id, var) %>%
summarise(val = {t <- table(val); names(t)[which.max(t)] })
# A tibble: 2 x 3
# Groups: id [?]
# id var val
# <fct> <fct> <chr>
#1 a b d
#2 a c f
Another way to do this in base R: Create a three way contingency table directly, and then find the max column along the third axis:
apply(table(df), c(1, 2), function(v) names(v)[which.max(v)])
# var
#id b c
# a "d" "f"
Convert this to a data frame:
as.data.frame.table(
apply(table(df), c(1, 2), function(v) names(v)[which.max(v)])
)
# id var Freq
#1 a b d
#2 a c f

Answer 2

Using dplyr:
library(dplyr)
df %>%
group_by(id, var, val) %>%
summarise(n = n()) %>%
group_by(id, var) %>%
arrange(-n) %>%
slice(1) %>%
ungroup() %>%
select(-n)
# # A tibble: 2 x 3
# id var val
# <fct> <fct> <fct>
# 1 a b d
# 2 a c f

Answer 3

One option could be using table and max as:
library(dplyr)
df %>% group_by(id, var) %>%
filter(table(val) == max(table(val))) %>%
slice(1)
# # A tibble: 2 x 3
# # Groups: id, var [2]
# id var val
# <fctr> <fctr> <fctr>
# 1 a b d
# 2 a c g
NOTE: a c g is case of tie. Per OP any record can be returned in case of tie.

Answer 4

I doubt this is any faster, but another option is
df %>%
group_by(id, var) %>%
filter(row_number() == rle(as.character(val))$lengths %>%
{sum(.[1:which.max(.)])})

Answer 5

A dplyr solution using count:
library(dplyr)
df %>%
count(id,var,val,sort = T) %>%
group_by(id,var) %>%
summarize_at("val",head,1)
# # A tibble: 2 x 3
# id var val
# <fctr> <fctr> <fctr>
# 1 a b d
# 2 a c f
or maybe more idiomatic but longer:
df %>%
count(id,var,val,sort = T) %>%
group_by(id,var) %>%
slice(1) %>%
select(-n) %>%
ungroup
Or with tally for same output with slightly different syntax:
df %>%
group_by(id,var,val) %>%
tally(sort = T) %>%
slice(1) %>%
select(-n) %>%
ungroup
and a base solution :
df2 <- aggregate(x ~ .,cbind(df,x=1),sum)
aggregate(val ~ id+var, df2[order(-df2$x),],head,1)
# id var val
# 1 a b d
# 2 a c f

Answer 6

Here is my try:
library(dplyr)
df %>%
group_by(id,var,val) %>%
mutate(n=n()) %>%
arrange(desc(n)) %>%
group_by(id,var) %>%
filter(row_number()==1) %>%
select(-n)
`