Tidy Modeling with R

Chapter 4. The Ames Housing Data

In this chapter, we’ll introduce the Ames housing data set (De Cock 2011), which we will use in modeling examples throughout this book. Exploratory data analysis, like what we walk through in this chapter, is an important first step in building a reliable model. The data set contains information on 2,930 properties in Ames, Iowa, including columns related to:

• House characteristics (bedrooms, garage, fireplace, pool, porch, etc.)

• Location (neighborhood)

• Lot information (zoning, shape, size, etc.)

• Ratings of condition and quality

• Sale price

The raw housing data are provided in De Cock (2011), but in our analyses in this book, we use a transformed version available in the modeldata package. This version has several changes and improvements to the data. For example, the longitude and latitude values have been determined for each property. Also, some columns were modified to be more analysis ready. For example:

• In the raw data, if a house did not have a particular feature, it was implicitly encoded as missing. For example, 2,732 properties did not have an alleyway. Instead of leaving these as missing, they were relabeled in the transformed version to indicate that no alley was available.

• The categorical predictors were converted to R’s factor data type. While both the tidyverse and base R have moved away from importing data as factors by default, this data type is a better approach for storing qualitative data for modeling than simple ...