Rearrange rows and calculate mode in R by creating a new variable - r

Question

I have a dataset with two columns, "ID" and "CODCOM" with about 1 milion of rows. The first column "ID" contain duplicate values.
ID
CODCOM
10000
12
101010
14
201020
11
201020
11
201020
12
324032
43
324032
43
324032
43
405044
51
323032
21
I want to group "ID" duplicated values in different groups, then calculate the mode for each groups, after that I want to create a new column with the related mode values. Something like this:
ID
CODCOM
NEW_COL
10000
12
12
101010
14
14
201020
11
11
201020
11
11
201020
12
11
324032
43
43
324032
43
43
324032
43
43
405044
51
51
323032
21
43
How can I do this in an easy way?
Thank you so much in advance for any help provided.

Answer 1

A dplyr approach where I join the data to a version of itself with just the most-common CODCOM value (or first appearing with ties).
library(dplyr)
df1 %>%
left_join(
df1 %>%
group_by(ID) %>%
count(mode = CODCOM, sort = TRUE) %>%
slice(1),
by = "ID"
)
ID CODCOM mode n
1 10000 12 12 1
2 101010 14 14 1
3 201020 11 11 2
4 201020 11 11 2
5 201020 12 11 2
6 324032 43 43 3
7 324032 43 43 3
8 324032 43 43 3
9 405044 51 51 1
10 323032 21 21 1

Answer 2

Please find below one solution using the package data.table:
REPREX
Code
library(data.table)
# Function to compute mode
mode_compute <- function(x) {
uniqx <- unique(x)
uniqx[which.max(tabulate(match(x, uniqx)))]
}
# Compute mode by ID
DT[ , MODE := mode_compute(CODCOM), by = ID]
Output
DT
#> ID CODCOM MODE
#> 1: 10000 12 12
#> 2: 101010 14 14
#> 3: 201020 11 11
#> 4: 201020 11 11
#> 5: 201020 12 11
#> 6: 324032 43 43
#> 7: 324032 43 43
#> 8: 324032 43 43
#> 9: 405044 51 51
#> 10: 323032 21 21
Data:
# Data
DT <- data.table(ID = c("10000", "101010", "201020", "201020", "201020",
"324032", "324032", "324032", "405044", "323032"),
CODCOM = c(12, 14, 11, 11, 12, 43, 43, 43, 51, 21))
DT
#> ID CODCOM
#> 1: 10000 12
#> 2: 101010 14
#> 3: 201020 11
#> 4: 201020 11
#> 5: 201020 12
#> 6: 324032 43
#> 7: 324032 43
#> 8: 324032 43
#> 9: 405044 51
#> 10: 323032 21
Created on 2021-10-11 by the reprex package (v0.3.0)

Answer 3

If I understand you correctly: we could group_by ID and then use summarise the mode of the mode function:
If you don't want to summarise you could use mutate instead (will keep all rows)!
library(dplyr)
mode <- function(codes){
which.max(tabulate(codes))
}
df %>%
as_tibble() %>%
group_by(ID) %>%
summarise(NEW_COL = mode(CODCOM))
ID NEW_COL
<int> <int>
1 10000 12
2 101010 14
3 201020 11
4 323032 21
5 324032 43
6 405044 51

Answer 4

Base R solution:
# Option 1 using TarJae's mode function:
# Apply the function groupwise, store result as vector:
# NEW_COL => integer vector
df$NEW_COL <- with(
df,
ave(
CODCOM,
ID,
FUN = function(x){
which.max(tabulate(x))
}
)
)
# Option two:
# Function to calculate the mode of a vector:
# mode_statistic => function()
mode_statistic <- function(x){
# Calculate the mode: res => vector
res <- names(
head(
sort(
table(
x
),
decreasing = TRUE
),
1
)
)
# Explicitly define returned object: character vector => env
return(res)
}
# Apply the function groupwise, store result as vector:
# NEW_COL => integer vector
df$NEW_COL <- with(
df,
ave(
CODCOM,
ID,
FUN = function(x){
as.integer(
mode_statistic(x)
)
}
)
)