I have a dataset with a column which indicate the number of occurence of a group constituted by multiples variables. Here SEX
and COLOR
.
CASES <- base::data.frame(SEX = c("M", "M", "F", "F", "F"),
COLOR = c("brown", "blue", "brown", "brown", "brown"))
COUNT <- base::as.data.frame(base::table(CASES))
COUNT
I need to change the structure of the dataset, so I have one row for each occurence of the group. Someone helped me to create a function which works perfectly.
countsToCases <- function(x, countcol = "Freq") {
# Get the row indices to pull from x
idx <- rep.int(seq_len(nrow(x)), x[[countcol]])
# Drop count column
x[[countcol]] <- NULL
# Get the rows from x
x[idx, ]
}
CASES <- countsToCases(base::as.data.frame(COUNT))
CASES
The problem is now that I have a HUGE dataset (the babyname
dataset from tidytuesday), and this is not working since it's too slow.
db_babynames <- data.table::as.data.table(tuesdata$babyname)
db_babynames <- db_babynames[
j = characters_n := stringr::str_count(string = name,
pattern = ".")
][
j = c("year", "characters_n", "n")
]
I'm looking for a faster solution, working with the data.table
package if possible.
If an uncounted version is needed I would use tidyr::uncount()
, but consider the recommendation in this post to work with your original data
library(dplyr)
library(tidyr)
CASES <- base::data.frame(
SEX = c("M", "M", "F", "F", "F"),
COLOR = c("brown", "blue", "brown", "brown", "brown")
)
COUNT <- count(CASES, SEX, COLOR, name = 'Freq')
tidyr::uncount(base::as.data.frame(COUNT), Freq)
#> SEX COLOR
#> 1 F brown
#> 2 F brown
#> 3 F brown
#> 4 M blue
#> 5 M brown
Created on 2022-03-25 by the reprex package (v2.0.1)