Data Wrangling with R

Leverage the flexibility of rvest, tidyr, stringr, and other data wrangling libraries of R to extract more out of data and take clever business decisions.
About This Book
* This course provides you with a hands-on experience on the full data wrangling cycle, starting with gathering and exploring, going through visualizing, cleaning, and finally transforming the data to make it ready for analysis, further processing, or reporting. This equips you with the essential skills required for a data engineer or data analyst role.
* Each chapter will be self-contained so that you can focus on specific topics or come back later to review them.
* The course features a learning-by-doing paradigm. Each concept will be explained with code that every student will be able to reproduce in their environments.
* Each chapter will feature a practical case/exercise in which they will make use of the concepts learned in class.
Who This Book Is For
This book is catered to data analysts who need to understand how to use R to perform Data Wrangling activities.
R developers who want to learn a methodology and practical examples for performing Data Wrangling can also benefit from this book.
What You Will Learn
* Gather and store data from different sources and various file formats
* Apply basic concepts of statistics to better describe data
* Use ggplot to create scatter plots, histograms, box plots, and facets
* Use regular expressions to detect, extract, fix, and replace data patterns
* Apply shuffling, sampling, and partitioning techniques to transform data
* Use packages like plyr, dplyr, map, and mutate for transforming data
In Detail
With this book, you will apply the popular language R to perform the data wrangling tasks, and step by step, chapter by chapter, will become proficient in data wrangling.
You will learn how to explore and characterize a dataset using different R functions to perform descriptive statistics. Then, you will be shown all the different ways data can be gathered and stored. You will understand how visualizations can be used to enhance understanding of the data, and for this you will use ggplot2 to generate different plot types.
Moving on, the book will cover the data cleaning stage of data wrangling, showing how in practice there's a high probability that data is either incomplete or dirty, and explaining diverse techniques to take care of those issues. Also you will understand the different issues that arise while working with different data types especially when importing or exporting data. You'll learn the tools that can be used to solve them (formatting strings/numbers/dates, converting from string to numeric or date data types and the other way around, string manipulation functions, and so on). Then, the book will cover regular expressions, a powerful tool for matching and replacing complex string patterns.
This book then deals with data transformation. It explains why once data is cleaned, we would need to transform it to a different format or structure.
The goal of this book is to guide you through the whole data wrangling journey, from gathering to making it ready for reporting or processing.