#### 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.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
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

## Working with the Pearson's chi-squared tests

In this recipe, we introduced Pearson's chi-squared test, which is used to examine whether the distribution of categorical variables of two groups differ. We will discuss how to conduct Pearson's chi-squared Test in R.

In this recipe, we will use the `chisq.test` function that originated from the `stat` package.

### How to do it…

Perform the following steps to conduct a Pearson's chi-squared test:

1. First, build a matrix containing the number of male and female smokers and nonsmokers:

```>mat<- matrix(c(2047, 2522, 3512, 1919), nrow = 2, dimnames = list(c("smoke","non-smoke"), c("male","female")))
>mat
malefemale
smoke2047   3512
non-smoke 2522   1919
```
2. Then, plot the portion of male and female smokers and nonsmokers in a mosaic plot:

```>mosaicplot(mat, main="Portion of male and female smokers/non-smokers", color = TRUE)
```

Figure 9: The mosaic plot

3. Next, perform a Pearson's chi-squared test on the contingency table to test whether the factor...