Book Image

R for Data Science Cookbook (n)

By : Yu-Wei, Chiu (David Chiu)
Book Image

R for Data Science Cookbook (n)

By: Yu-Wei, Chiu (David Chiu)

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
About the Author
About the Reviewer

Conducting exact binomial tests

To perform parametric testing, one must assume that the data follows a specific distribution. However, in most cases, we do not know how the data is distributed. Thus, we can perform a nonparametric (that is, distribution-free) test instead. In the following recipes, we will show you how to perform nonparametric tests in R. First, we will cover how to conduct an exact binomial test in R.

Getting ready

In this recipe, we will use the binom.test function from the stat package.

How to do it…

Perform the following steps to conduct an exact binomial test:

  1. Let's assume there is a game where a gambler can win by rolling the number six on a dice. As part of the rules, the gambler can bring their own dice. If the gambler tried to cheat in the game, they would use a loaded dice to increase their chances of winning. Therefore, if we observe that the gambler won 92 games out of 315, we could determine whether the dice was likely fair by conducting an exact binomial test: