SLR: Fitting models in R with tidymodels

Prof. Maria Tackett

Sep 05, 2022

Announcements

  • No office hours today. Office hours start Tuesday, September 6. Click here for full schedule

  • Check your email for an email to join the course GitHub organization. You will receive one by Tuesday, September 6.

  • See Week 02 for this week’s activities.

Topics

  • Introduce the computing toolkit - RStudio and GitHub
  • Use tidymodels to fit and summarize regression models in R
  • Complete an application exercise on exploratory data analysis and modeling

Computational setup

# load packages
library(tidyverse)       # for data wrangling
library(tidymodels)      # for modeling
library(fivethirtyeight) # for the fandango dataset

# set default theme and larger font size for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 16))

# set default figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 8,
  fig.asp = 0.618,
  fig.retina = 3,
  dpi = 300,
  out.width = "80%"
)

Computing toolkit

Reproducibility checklist

What does it mean for an analysis to be reproducible?

Near term goals:

✔️ Are the tables and figures reproducible from the code and data?

✔️ Does the code actually do what you think it does?

✔️ In addition to what was done, is it clear why it was done?

Long term goals:

✔️ Can the code be used for other data?

✔️ Can you extend the code to do other things?

Toolkit

  • Scriptability \(\rightarrow\) R

  • Literate programming (code, narrative, output in one place) \(\rightarrow\) Quarto

  • Version control \(\rightarrow\) Git / GitHub

    • More on this in this week’s lab

What are R and RStudio?

  • R is a statistical programming language

  • RStudio is a convenient interface for R (an integrated development environment, IDE)

Source: Modern Dive

RStudio IDE

Quarto

  • Fully reproducible reports – the analysis is run from the beginning each time you render

  • Code goes in chunks and narrative goes outside of chunks

  • Visual editor to make document editing experience similar to a word processor (Google docs, Word, Pages, etc.)

Quarto

How will we use Quarto?

  • Every application exercise and assignment is written in a Quarto document

  • You’ll have a template Quarto document to start with

  • The amount of scaffolding in the template will decrease over the semester

Application exercise

📋 github.com/sta210-fa22/ae-02-bikeshare

Using R for SLR

Recap of last lecture

  • Used simple linear regression to describe the relationship between a quantitative predictor and quantitative outcome variable.

  • Used the least squares method to estimate the slope and intercept.

  • We interpreted the slope and intercept.

    • Slope: For every one unit increase in \(x\), we expect y to be higher/lower by \(\hat{\beta}_1\) units, on average.
    • Intercept: If \(x\) is 0, then we expect \(y\) to be \(\hat{\beta}_0\) units.

  • Predicted the response given a value of the predictor variable.

  • Defined extrapolation and why we should avoid it.

Data

Movie ratings

Data prep

  • Rename Rotten Tomatoes columns as critics and audience
  • Rename the dataset as movie_scores
movie_scores <- fandango |>
  rename(
    critics = rottentomatoes, 
    audience = rottentomatoes_user
  )

\[ \widehat{\text{audience}} = 32.3142 + 0.5187 \times \text{critics} \]

tidymodels

The tidymodels framework is a collection of packages for modeling and machine learning using tidyverse principles.

library(tidymodels)
── Attaching packages ────────────────────────────────────── tidymodels 1.0.0 ──
✔ broom        1.0.0     ✔ rsample      1.0.0
✔ dials        1.0.0     ✔ tune         1.0.0
✔ infer        1.0.2     ✔ workflows    1.0.0
✔ modeldata    1.0.0     ✔ workflowsets 1.0.0
✔ parsnip      1.0.0     ✔ yardstick    1.0.0
✔ recipes      1.0.1     
── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
✖ scales::discard() masks purrr::discard()
✖ dplyr::filter()   masks stats::filter()
✖ recipes::fixed()  masks stringr::fixed()
✖ dplyr::lag()      masks stats::lag()
✖ yardstick::spec() masks readr::spec()
✖ recipes::step()   masks stats::step()
• Search for functions across packages at https://www.tidymodels.org/find/

Why tidymodels?

  • Consistent syntax for different model types (linear, logistic, random forest, Bayesian, etc.)
  • Streamline modeling workflow
    • Split data into train and test sets
    • Transform and create new variables
    • Assess model performance
    • Use model for prediction and inference

Fitting the model

Step 1: Specify model

linear_reg()
Linear Regression Model Specification (regression)

Computational engine: lm

Step 2: Set model fitting engine

linear_reg() |>
  set_engine("lm") # lm: linear model
Linear Regression Model Specification (regression)

Computational engine: lm

Step 3: Fit model & estimate parameters

using formula syntax

linear_reg() |>
  set_engine("lm") |>
  fit(audience ~ critics, data = movie_scores)
parsnip model object


Call:
stats::lm(formula = audience ~ critics, data = data)

Coefficients:
(Intercept)      critics  
    32.3155       0.5187

A closer look at model output

movie_fit <- linear_reg() |>
  set_engine("lm") |>
  fit(audience ~ critics, data = movie_scores)

movie_fit
parsnip model object


Call:
stats::lm(formula = audience ~ critics, data = data)

Coefficients:
(Intercept)      critics  
    32.3155       0.5187

\[\widehat{\text{audience}} = 32.3155 + 0.5187 \times \text{critics}\]

Note: The intercept is off by a tiny bit from the hand-calculated intercept, this is likely just due to rounding in the hand calculation.

The regression output

We’ll focus on the first column for now…

linear_reg() |>
  set_engine("lm") |>
  fit(audience ~ critics, data = movie_scores) |>
  tidy() 
# A tibble: 2 × 5
  term        estimate std.error statistic  p.value
  <chr>          <dbl>     <dbl>     <dbl>    <dbl>
1 (Intercept)   32.3      2.34        13.8 4.03e-28
2 critics        0.519    0.0345      15.0 2.70e-31

Prediction

# create a data frame for a new movie
new_movie <- tibble(critics = 70)

# predict the outcome for a new movie
predict(movie_fit, new_movie)
# A tibble: 1 × 1
  .pred
  <dbl>
1  68.6

Application exercise

📋 github.com/sta210-fa22/ae-02-bikeshare

Recap

  • Introduced the computing toolkit - RStudio and GitHub
  • Used tidymodels to fit and summarize regression models in R
  • Completed an application exercise on exploratory data analysis and modeling

🔗 Week 02