The goal of cookbook is to …
You can install the development version of cookbook from Github with:
devtools::install_github("konradsemsch/cookbook")This is a basic example which shows you how to solve a common problem:
library(cookbook)
library(modeldata)
library(tidymodels)
#> Registered S3 method overwritten by 'xts':
#> method from
#> as.zoo.xts zoo
#> ── Attaching packages ───────────────────────────────────────── tidymodels 0.0.3 ──
#> ✓ broom 0.5.2 ✓ purrr 0.3.3
#> ✓ dials 0.0.4 ✓ recipes 0.1.9
#> ✓ dplyr 0.8.3 ✓ rsample 0.0.5
#> ✓ ggplot2 3.2.1 ✓ tibble 2.1.3
#> ✓ infer 0.5.0 ✓ yardstick 0.0.4
#> ✓ parsnip 0.0.4.9000
#> ── Conflicts ──────────────────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x ggplot2::margin() masks dials::margin()
#> x recipes::step() masks stats::step()
library(workflows)
data("okc")
colnames(okc) <- tolower(names(okc))
okc <- sample_n(okc, 1000)
strata <- "class"
split <- initial_split(okc, prop = 0.8, strata = strata)
train <- training(split)
test <- testing(split)
cv <- vfold_cv(
train,
v = 5,
repeats = 1,
strata = strata
)
# 1) Create a base recipe where training data is provided
# and roles for all variables are defined
recipe_base <- train %>%
recipe() %>%
update_role(class, new_role = "outcome") %>%
update_role(-one_of("class"), new_role = "predictors")
# 2) Pass your base recipe into the apply_recipe function
# and provide the recipe blueprint to bind to it
recipe <- recipe_base %>%
add_blueprint("v1")
# 3) Run your model as you normally would (single blueprint)
model <- logistic_reg() %>%
set_mode("classification") %>%
set_engine("glm")
workflow <- workflow() %>%
add_recipe(recipe) %>%
add_model(model) %>%
fit(data = train)