#### 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

## Introduction

The most prominent feature of R is that it implements a wide variety of statistical packages. Using these packages, it is easy to obtain descriptive statistics about a dataset or infer the distribution of a population from sample data. Moreover, with R's plotting capabilities, we can easily display data in a variety of charts.

To apply statistical methods in R, the user can categorize the method of implementation into descriptive statistics and inferential statistics, described as follows:

• Descriptive statistics: These are used to summarize the characteristics of data. The user can use mean and standard deviation to describe numerical data, and they can use frequency and percentages to describe categorical data.

• Inferential statistics: This is when, based on patterns within sample data, the user can infer the characteristics of the population. Methods relating to inferential statistics include hypothesis testing, data estimation, data correlation, and relationship modeling. Inference...