1 Load packages
library(tidyverse) # data wrangling
library(tidymodels) # modelling2 Understanding recipes and preprocessing
Having defined a recipe in this Kaggle competition, I was left wondering about some details of the recipe definition. Let’s explore that.
3 Load data
traindata_url <- "https://raw.githubusercontent.com/sebastiansauer/Statistiklehre/main/data/titanic/train.csv"train <- read_csv(traindata_url)4 Define recipe
titanic_recipe <-
# define model formula:
recipe(Survived ~ Pclass, data = train) %>%
# convert numeric outcome to nominal (factor):
step_num2factor(Survived, levels = c("dead", "alive")) # not working, #todo
5 Prepare (prep()) the recipe
{ error = TRUE} titanic_recipe_prepped <- titanic_recipe %>% prep(verbose = TRUE)
6 Reproducibility
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