Book Image

Practical Data Wrangling

By : Allan Visochek
Book Image

Practical Data Wrangling

By: Allan Visochek

Overview of this book

Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You’ll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You’ll work with different data structures and acquire and parse data from various locations. You’ll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases. The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you’ll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.
Table of Contents (16 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Introducing R and RStudio

R is a programming language and environment for statistical computing and graphics. R differs from Python in that it is specialized for statistical computing and contains a number of features in addition to the programming language. The R project website lists the features of R as:

  • An effective data handling and storage facility
  • A suite of operators for calculations on arrays - in particular, matrices
  • A large, coherent, integrated collection of intermediate tools for data analysis
  • Graphical facilities for data analysis and display either onscreen or on hard copy
  • A well-developed, simple and effective programming language that includes conditionals, loops, user-defined recursive functions, and input and output facilities

The most common way of using R is through RStudio, an IDE for interfacing with the R programming language and environment. RStudio combines several components to centralize and facilitate the process of working with data. These include:

  • A console for executing...