# Packages
library(dplyr)
library(recipes)
# toy dataset, with A being multicolored
df <- tibble(name = c("A", "A", "A", "B", "C"), color = c("green", "yellow", "purple", "green", "blue"))
#> # A tibble: 5 x 2
#> name color
#> <chr> <chr>
#> 1 A green
#> 2 A yellow
#> 3 A purple
#> 4 B green
#> 5 C blue
The recipes step works nicely
dummified_df <- recipe(. ~ ., data = df) %>%
step_dummy(color, one_hot = TRUE) %>%
prep(training = df) %>%
juice()
#> # A tibble: 5 x 5
#> name color_blue color_green color_purple color_yellow
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 A 0 1 0 0
#> 2 A 0 0 0 1
#> 3 A 0 0 1 0
#> 4 B 0 1 0 0
#> 5 C 1 0 0 0
But the result I truly want to obtain is the one below, with one observation per row now that the multicolored item does not need several rows anymore.
summarized_dummified_df <- dummified_df %>%
group_by(name) %>%
summarise_all(~ifelse(max(.) > 0, 1, 0)) %>%
ungroup()
#> # A tibble: 3 x 5
#> name color_blue color_green color_purple color_yellow
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 A 0 1 1 1
#> 2 B 0 1 0 0
#> 3 C 1 0 0 0
Obviously, I can do it this way.
But to fully integrate my recipe step in the tidymodels ecosystem, for instance with workflow, it would be much better if I could group the rows that don't have to be duplicated anymore thanks to the dummy variables directly inside the recipe.
Is there any tidymodels-sanctioned way to obtain this result ?
I also tried to do this with mlr3, to no avail because I can't find any suitable PipeOp to aggregate rows.
library("mlr3")
library("mlr3pipelines")
task = TaskClassif$new("task",
data.table::data.table(
name = c("A", "A", "A", "B", "C"),
color = as.factor(c("green", "yellow", "purple", "green", "blue")),
price = as.factor(c("low", "low", "low", "high", "low"))),
"price"
)
poe = po("encode")
poe$train(list(task))[[1]]$data()
#> price name color.blue color.green color.purple color.yellow
#> 1: low A 0 1 0 0
#> 2: low A 0 0 0 1
#> 3: low A 0 0 1 0
#> 4: high B 0 1 0 0
#> 5: low C 1 0 0 0
I'm looking into the creation of custom step_ functions or custom PipeOp but I still feel like I'm missing something because my type of data doesn't feel that uncommon to me.
Dummy or indicator variables are conceptually mapped as one-to-one everywhere I have seen, not one-to-many, and I think this is why you are running into this. Like you, though, I have wanted to map them one-to-many sometime in the real world. I typically do this in a data tidying step before starting my model preprocessing workflow, something like this:
library(tidyverse)
# toy dataset, with A being multicolored
df <- tibble(name = c("A", "A", "A", "B", "C"), color = c("green", "yellow", "purple", "green", "blue"))
df %>%
mutate(value = 1) %>%
pivot_wider(names_from = "color", names_prefix = "color_", values_from = "value", values_fill = 0)
#> # A tibble: 3 x 5
#> name color_green color_yellow color_purple color_blue
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 A 1 1 1 0
#> 2 B 1 0 0 0
#> 3 C 0 0 0 1
Created on 2020-08-18 by the reprex package (v0.3.0.9001)