library(tidymodels)
data(Chicago)
chicago_small <- Chicago %>% slice(1:365)
splits <-
sliding_period(
chicago_small,
date,
"day",
lookback = 300, # Each resample has 300 days for modeling
assess_stop = 7, # One week for performance assessment
step = 7 # Ensure non-overlapping weeks for assessment
)
chicago_rec <-
recipe(ridership ~ ., data = Chicago) %>%
step_date(date) %>%
step_holiday(date, keep_original_cols = FALSE) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors()) %>%
step_normalize(all_predictors()) %>%
step_pca(all_of(stations), num_comp = 4)
tree_spec <-
decision_tree() %>%
set_engine("rpart") %>%
set_mode("regression")
chicago_fit <-
workflow(chicago_rec, tree_spec) %>%
fit(chicago_small)
library(vetiver)
v <- vetiver_model(chicago_fit, "julia.silge/chicago_ridership")
v
vetiver_pin_write(model_board, v, check_renv = TRUE)
#* -------------------------------------------------------------------------- *#
library(vetiver)
library(pins)
model_board <- board_connect()
vetiver_deploy_rsconnect(
model_board,
"julia.silge/chicago_ridership",
predict_args = list(debug = TRUE),
account = "julia.silge"
)
#* -------------------------------------------------------------------------- *#
library(plumber)
pr() %>%
vetiver_api(v, debug = TRUE)
## next pipe to pr_run(port = 8088) to see visual documentation
vetiver_write_plumber(
model_board,
"julia.silge/chicago_ridership",
debug = TRUE,
file = "inst/plumber/chicago-rpart/plumber.R"
)
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