Data Manipulation with R

Description

Perform groupwise data manipulation and deal with large datasets using R efficiently and effectivelyAbout This BookPerform factor manipulation and string processing Learn group-wise data manipulation using plyr Handle large datasets, interact with database software, and manipulate data using sqldf Who This Book Is For This book is aimed at intermediate to advanced level users of R who want to perform data manipulation with R, and those who want to clean and aggregate data effectively. Readers are expected to have at least an introductory knowledge of R and some basic administration work in R, such as installing packages and calling them when required. What You Will LearnLearn R data types and their basic operations Deal efficiently with string, factor, and date Understand group-wise data manipulation Work with different layouts of the R dataset and interchange between layouts for different purposes Connect R with database software to manage relational databases Manage bigger datasets using R Manipulate datasets using SQL statements through the sqldf package In Detail One of the most important aspects of computing with data is the ability to manipulate it to enable subsequent analysis and visualization. R offers a wide range of tools for this purpose. Data from any source, be it flat files or databases, can be loaded into R and this will allow you to manipulate data format into structures that support reproducible and convenient data analysis. This practical, example-oriented guide aims to discuss the split-apply-combine strategy in data manipulation, which is a faster data manipulation approach. After reading this book, you will not only be able to efficiently manage and check the validity of your datasets with the split-apply-combine strategy, but you will also learn to handle larger datasets. This book starts with describing the R object's mode and class, and then highlights different R data types, explaining their basic operations. You will focus on group-wise data manipulation with the split-apply-combine strategy, supported by specific examples. You will also learn to efficiently handle date, string, and factor variables along with different layouts of datasets using the reshape2 package. You will learn to use plyr effectively for data manipulation, truncating and rounding data, simulating data sets, as well as character manipulation. Finally you will get acquainted with using R with SQL databases. Cover Copyright Credits About the Author About the Reviewers www.PacktPub.com Table of Contents Preface Chapter 1: R Data Types and Basic Operations Modes and classes of R objects R object structure and mode conversion Vector Factor and its types Data frame Matrices Arrays list Missing values in R Summary Chapter 2: Basic Data Manipulation Acquiring data Factor manipulation Factors from numeric variables Date processing Character manipulation Subscripting and subsetting Summary Chapter 3: Data Manipulation Using plyr The split-apply-combine strategy Split-apply-combine without a loop Split-apply-combine with a loop Utilities of plyr Intuitive function names Input and arguments Comparing default R and plyr Multiargument functions Summary Chapter 4: Reshaping Datasets Typical layout of a dataset Long layout Wide layout The new layout of a dataset Reshaping the dataset from the typical layout Reshaping the dataset with the reshape package Melting data Missing values in molten data Casting molten data The reshape2 package Summary Chapter 5: R and Databases R and different databases R and Excel R and MS Access Relational databases in R The filehash package The ff package R and sqldf Data manipulation using sqldf Summary Bibliography Index