Tidy Modeling with R

Chapter 3. A Review of R Modeling Fundamentals

Before describing how to use tidymodels for applying tidy data principles to building models with R, let’s review how models are created, trained, and used in the core R language (often called base R). This chapter is a brief illustration of core language conventions that are important to be aware of even if you never use base R for models at all. This chapter is not exhaustive, but it provides readers (especially those new to R) the basic, most commonly used motifs.

The S language, on which R is based, has had a rich data analysis environment since the publication of Chambers and Hastie (1992) (commonly known as The White Book). This version of S introduced standard infrastructure components familiar to R users today, such as symbolic model formulas, model matrices, and data frames, as well as standard object-oriented programming methods for data analysis. These user interfaces have not substantively changed since then.

An Example

To demonstrate some fundamentals for modeling in base R, let’s use experimental data from McDonald (2009), by way of Mangiafico (2015), on the relationship between the ambient temperature and the rate of cricket chirps per minute. Data were collected for two species: O. exclamationis and O. niveus. The data are contained in a data frame called crickets with a total of 31 data points. These data are shown in Figure 3-1 using the following ggplot2 code:

library(tidyverse)

data(crickets, package = "modeldata" ...