#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
Table of Contents (19 chapters)
R for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Extracting, Transforming, and Loading
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

## Selecting the best-fitted regression model with stepwise regression

In order to find the best-fitted regression model, one can perform stepwise regression to step-wisely add or remove a term from a fitted model, and finally output a model with the least AIC. In the following recipe, we demonstrate how to perform stepwise regression with the step function.

### Getting ready

You need to have completed the previous recipe by fitting house rental data into a multiple regression model, `fit`.

### How to do it…

Perform the following steps to search for the best-fitted regression model with the `step` function:

1. First, you can use `step` to select the optimum model with backward elimination:

```> step(fit, direction="backward")
Start:  AIC=12753.77
Price ~ Sqft + Floor + TotalFloor + Bedroom + Living.Room + Bathroom

Df  Sum of Sq        RSS   AIC
- TotalFloor   1 1.8081e+08 2.4428e+11 12752
<none>                      2.4410e+11 12754
- Bathroom     1 8.7580e+08 2.4497e+11 12754
- Living.Room...```