This guide for practicing statisticians data scientists and R users and programmers will
teach the essentials of preprocessing: data leveraging the R programming language to easily and
quickly turn noisy data into usable pieces of information. Data wrangling which is also
commonly referred to as data munging transformation manipulation janitor work etc. can be
a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and
preparing data however being a prerequisite to the rest of the data analysis workflow
(visualization analysis reporting) it is essential that one become fluent and efficient in
data wrangling techniques. This book will guide the user through the data wrangling process via
a step-by-step tutorial approach and provide a solid foundation for working with data in R. The
author's goal is to teach the user how to easily wrangle data in order to spend more time on
understanding the content of the data. By the end of the book the user will have learned: How
to work with different types of data such as numerics characters regular expressions factors
and dates The difference between different data structures and how to create add additional
components to and subset each data structure How to acquire and parse data from locations
previously inaccessible How to develop functions and use loop control structures to reduce code
redundancy How to use pipe operators to simplify code and make it more readable How to reshape
the layout of data and manipulate summarize and join data sets
