Model Step 1 - Train and Deploy Model

Published

November 14, 2025

This notebook trains a model to predict the number of bikes at a given bike docking station. The model is trained using the bike_model_data table from Content DB. The trained model is then:

pinned to Posit Connect
deployed as a plumber API to Posit Connect using vetiver.

Get data

Connect to the database:

con <- DBI::dbConnect(
  odbc::odbc(),
  Driver      = "postgresql",
  Server      = Sys.getenv("DB_SERVER"),
  Port        = "5432",
  Database    = "soleng",
  UID         = Sys.getenv("DB_USER"),
  PWD         = Sys.getenv("DB_PASSWORD"),
  BoolsAsChar = "",
  timeout     = 10
)

Split the data into a train/test split:

all_days <- tbl(con, DBI::Id(schema="content", name="bike_model_data"))

# Get a vector that contains all of the dates.
dates <- all_days %>%
  distinct(date) %>%
  collect() %>%
  arrange(desc(date)) %>%
  pull(date) %>%
  as.Date()

# Split the data into test and train.
n_days_test <- 2
n_days_to_train <- 10

# TODO: FIX THIS. UPSTREAM DATA STOPPED PROVIDING HOURLY DATA. HAD TO PIN TO FIXED DATE RANGE FOR MODEL.
train_end_date <- as.Date("2024-01-05")
train_start_date <- as.Date("2023-12-10")
# train_end_date <- dates[n_days_test + 1]
# train_start_date <- train_end_date - n_days_to_train

# Training data split.
train_data <- all_days %>%
  filter(
    date >= train_start_date,
    date <= train_end_date
  ) %>%
  distinct() %>%
  collect()

start = min(train_data$date)
end = max(train_data$date)
num_obs = scales::comma(nrow(train_data))

print(glue::glue(
  "The model will be trained on data from {start} to {end} ",
  "({num_obs} observations). "))
## The model will be trained on data from 2023-12-10 to 2024-01-05 (186,971 observations).

# Test data split.
test_data <- all_days %>%
  filter(date > train_end_date) %>%
  distinct() %>%
  collect()

start = min(test_data$date)
end = max(test_data$date)
num_obs = scales::comma(nrow(test_data))

print(glue::glue(
  "The model will be tested on data from {start} to {end} ",
  "({num_obs} observations). "))
## The model will be tested on data from 2024-01-06 to 2025-11-13 (2,225,229 observations).

Train the model

Data preprocessing

Define a recipe to clean the data.

# Define a recipe to clean the data.
recipe_spec <- 
  recipe(n_bikes ~ ., data = train_data) %>% 
  step_dummy(dow) %>%
  step_integer(id, date)

# Preview the cleaned training data.
recipe_spec %>% 
  prep(train_data) %>% 
  bake(head(train_data)) %>%
  glimpse()
## Rows: 6
## Columns: 13
## $ id            <int> 1, 1, 1, 1, 1, 1
## $ hour          <dbl> 0, 0, 0, 0, 0, 0
## $ date          <int> 1, 2, 3, 4, 6, 7
## $ month         <dbl> 12, 12, 12, 12, 12, 12
## $ lat           <dbl> 38.87035, 38.87035, 38.87035, 38.87035, 38.87035, 38.870…
## $ lon           <dbl> -76.94528, -76.94528, -76.94528, -76.94528, -76.94528, -…
## $ n_bikes       <dbl> 1, 1, 1, 0, 0, 0
## $ dow_Monday    <dbl> 0, 1, 0, 0, 0, 0
## $ dow_Saturday  <dbl> 0, 0, 0, 0, 0, 1
## $ dow_Sunday    <dbl> 1, 0, 0, 0, 0, 0
## $ dow_Thursday  <dbl> 0, 0, 0, 0, 0, 0
## $ dow_Tuesday   <dbl> 0, 0, 1, 0, 0, 0
## $ dow_Wednesday <dbl> 0, 0, 0, 1, 0, 0

Fit model

Fit a random forest model:

model_spec <- 
  rand_forest() %>%
  set_mode("regression") %>%
  set_engine("ranger")

model_workflow <- 
  workflow() %>%
  add_recipe(recipe_spec) %>%
  add_model(model_spec)

model_fit <- fit(model_workflow, data = train_data)
model_fit
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: rand_forest()
## 
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 2 Recipe Steps
## 
## • step_dummy()
## • step_integer()
## 
## ── Model ───────────────────────────────────────────────────────────────────────
## Ranger result
## 
## Call:
##  ranger::ranger(x = maybe_data_frame(x), y = y, num.threads = 1,      verbose = FALSE, seed = sample.int(10^5, 1)) 
## 
## Type:                             Regression 
## Number of trees:                  500 
## Sample size:                      186971 
## Number of independent variables:  12 
## Mtry:                             3 
## Target node size:                 5 
## Variable importance mode:         none 
## Splitrule:                        variance 
## OOB prediction error (MSE):       8.182708 
## R squared (OOB):                  0.7496369

Model evaluation

predictions <- predict(model_fit, test_data)

results <- test_data %>%
  mutate(preds = predictions$.pred)

oos_metrics(results$n_bikes, results$preds)
## # A tibble: 1 × 4
##    rmse   mae   ccc    r2
##   <dbl> <dbl> <dbl> <dbl>
## 1  4.81  3.80 0.443 0.219

Model deployment

`vetiver`

Create a vetiver model object.

model_name <- "bike_predict_model_r"
pin_name <- glue("katie.masiello@posit.co/{model_name}")

# Get the train and test data ranges. This will be passed into the pin metadata
# so that other scripts can access this information.
date_metadata <- list(
  train_dates = c(
    as.character(min(train_data$date)), 
    as.character(max(train_data$date))
  ),
  test_dates = c(
    as.character(min(test_data$date)), 
    as.character(max(test_data$date))
  )
)

print(date_metadata)
## $train_dates
## [1] "2023-12-10" "2024-01-05"
## 
## $test_dates
## [1] "2024-01-06" "2025-11-13"

# Create the vetiver model.
v <- vetiver_model(
  model_fit, 
  model_name,
  versioned = TRUE,
  save_ptype = train_data %>%
    head(1) %>%
    select(-n_bikes),
  metadata = date_metadata
)

v
## 
## ── bike_predict_model_r ─ <bundled_workflow> model for deployment 
## A ranger regression modeling workflow using 7 features

`pins`

Save the model as a pin to Posit Connect:

# Use Posit Connect as a board.
board <- pins::board_connect(
  server = Sys.getenv("CONNECT_SERVER"),
  key = Sys.getenv("CONNECT_API_KEY"),
  versioned = TRUE
)
# Write the model to the board.
board %>%
 vetiver_pin_write(vetiver_model = v)

`plumber`

Then, deploy the model as a plumber API to Posit Connect.

# Add server
rsconnect::addServer(
  url = "https://pub.current.posit.team/__api__",
  name = "pub.current"
)

# Add account
rsconnect::connectApiUser(
  account = "katie.masiello@posit.co",
  server = "pub.current",
  apiKey = Sys.getenv("CONNECT_API_KEY"),
)

# Deploy to Connect
vetiver_deploy_rsconnect(
  board = board,
  name = pin_name,
  appId = "442",
  launch.browser = FALSE,
  appTitle = "Bikeshare Prediction: 03b - Model - API",
  predict_args = list(debug = FALSE),
  account = "katie.masiello@posit.co",
  server =  "pub.current"
)
## Building Plumber API...
## Bundle created with R version 4.4.1 is compatible with environment Kubernetes::654654567442.dkr.ecr.us-east-2.amazonaws.com/ptd-adhoc-pct:content-r4.4.1-py3.10.14-quarto1.4.557::e0104ce5-5024-43cd-b374-12c7d9bf0ea4 with R version 4.4.1 from /opt/R/4.4.1/bin/R 
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## [connect-session] Content GUID: 6570e768-2118-4e5c-aee5-97b7027ab1b0
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## Vendored packrat SHA: b44ff11f4c1e42a04461e2d46673512d8d900eb5
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## Completed packrat build using Kubernetes::654654567442.dkr.ecr.us-east-2.amazonaws.com/ptd-adhoc-pct:content-r4.4.1-py3.10.14-quarto1.4.557::e0104ce5-5024-43cd-b374-12c7d9bf0ea4 against R version: '4.4.1'
## Stopped session pings to http://service-b28a503a-798b-4e05-9c0e-a211654d29b1.posit-team:50734
## Launching Plumber API...

DBI::dbDisconnect(con)