Hot encoding for a set of columns in R - r

Question

I am trying to do hot encoding for a subset of df columns in R,
One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction by converting string columns to binary columns for each string in that column.
Supose we are having a df that looks like this:
mes work_location birth_place
01/01/2000 China Chile
01/02/2000 Mexico Japan
01/03/2000 China Chile
01/04/2000 China Argentina
01/05/2000 USA Poland
01/06/2000 Mexico Poland
01/07/2000 USA Finland
01/08/2000 USA Finland
01/09/2000 Japan Norway
01/10/2000 Japan Kenia
01/11/2000 Japan Mali
01/12/2000 India Mali
Here's the code to hot encode :
## function to hot-encode ##
columna_dummy <- function(df, columna) {
df %>%
mutate_at(columna, ~paste(columna, eval(as.symbol(columna)), sep = "_")) %>%
mutate(valor = 1) %>%
spread(key = columna, value = valor, fill = 0)
}
## selecting columns ##
columnas <- c("work_location", "birth_place")
## applying loop to repeat columna_dummy function for each df column ##
for(i in 1:length(columnas)){
new_dataset <- columna_dummy(df, i)
}
Console output:
Error: Problem with `mutate()` input `mes`.
x objeto '1' no encontrado
i Input `mes` is `(structure(function (..., .x = ..1, .y = ..2, . = ..1) ...`.
Run `rlang::last_error()` to see where the error occurred.
Called from: signal_abort(cnd)
Column mes it's a date class column, however it is not included into columns atomic vector
and it still raises the above error,
Expected output should look somewhat like this for each string in selected string df column:
(I could not add every single column, but work_location_China it's an example of
how columns should look)
mes work_location birth_place work_location_China
01/01/2000 China Chile 1
01/02/2000 Mexico Japan 0
01/03/2000 China Chile 1
01/04/2000 China Argentina 1
01/05/2000 USA Poland 0
01/06/2000 Mexico Poland 0
01/07/2000 USA Finland 0
01/08/2000 USA Finland 0
01/09/2000 Japan Norway 0
01/10/2000 Japan Kenia 0
01/11/2000 Japan Mali 0
01/12/2000 India Mali 0
Is there any other way to apply this loop?

Answer 1

As we are passing strings, an option is to select the column (select can take both quoted/unquoted), create a column of 1s ('valor') and a row number column ('rn'), then do the reshaping from 'long' to 'wide' (pivot_wider)
library(dplyr)
library(tidyr)
library(purrr)
library(stringr)
columna_dummy <- function(df, columna) {
df %>%
select(columna) %>%
mutate(valor = 1, rn = row_number()) %>%
pivot_wider(names_from = all_of(columna),
values_from = valor, values_fill = 0) %>%
select(-rn)
}
-testing
For more than one column, an option is to loop over the column names of interest with map, apply the function and bind them with _dfc and bind with the original dataset (bind_cols)
out <- imap_dfc(setNames(c("work_location", "birth_place"),
c("work_location", "birth_place")) , ~ {
nm1 <- as.character(.y)
columna_dummy(df = df, columna = .x) %>%
rename_all(~ str_c(nm1, ., sep="_"))
}) %>%
bind_cols(df, .)
-output
head(out, 2)
# mes work_location birth_place work_location_China work_location_Mexico work_location_USA work_location_Japan
#1 01/01/2000 China Chile 1 0 0 0
#2 01/02/2000 Mexico Japan 0 1 0 0
# work_location_India birth_place_Chile birth_place_Japan birth_place_Argentina birth_place_Poland birth_place_Finland
#1 0 1 0 0 0 0
#2 0 0 1 0 0 0
# birth_place_Norway birth_place_Kenia birth_place_Mali
#1 0 0 0
#2 0 0 0
data
df <- structure(list(mes = c("01/01/2000", "01/02/2000", "01/03/2000",
"01/04/2000", "01/05/2000", "01/06/2000", "01/07/2000", "01/08/2000",
"01/09/2000", "01/10/2000", "01/11/2000", "01/12/2000"), work_location = c("China",
"Mexico", "China", "China", "USA", "Mexico", "USA", "USA", "Japan",
"Japan", "Japan", "India"), birth_place = c("Chile", "Japan",
"Chile", "Argentina", "Poland", "Poland", "Finland", "Finland",
"Norway", "Kenia", "Mali", "Mali")), class = "data.frame",
row.names = c(NA,
-12L))

Answer 2

By using purrr library I solved the issue:
## data ##
df <- structure(list(mes = c("01/01/2000", "01/02/2000", "01/03/2000",
"01/04/2000", "01/05/2000", "01/06/2000", "01/07/2000", "01/08/2000",
"01/09/2000", "01/10/2000", "01/11/2000", "01/12/2000"), work_location = c("China",
"Mexico", "China", "China", "USA", "Mexico", "USA", "USA", "Japan",
"Japan", "Japan", "India"), birth_place = c("Chile", "Japan",
"Chile", "Argentina", "Poland", "Poland", "Finland", "Finland",
"Norway", "Kenia", "Mali", "Mali")), class = "data.frame",
row.names = c(NA,
-12L))
## function to hot-encode ##
columna_dummy <- function(df, columna) {
df %>%
mutate_at(columna, ~paste(columna, eval(as.symbol(columna)), sep = "_")) %>%
mutate(valor = 1) %>%
spread(key = columna, value = valor, fill = 0)
}
## vector of columns ##
columnas <- c("work_location", "birth_place")
## hot_encoded_dataset ##
library(purrr)
hot_encoded_dataset <- purrr :: map(columnas , columna_dummy, df = df) %>%
reduce(inner_join)