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

Getting started with dplyr

To start off with, I will create an R script called dplyr_intro.R and set up my R environment. First, you should set your working directory to the ch7 project folder. Next, you should read the fuel economy dataset into a dataframe as follows:


The next step is to import the dplyr and tibble packages. In R, you can import a package using the library() function. The following lines import the dplyr package and the tibblepackage:


I will start with the select() function. The select() function allows you to select a certain number of columns from a dataframe and returns another dataframe containing only those selected columns. As its first argument, the select() function takes a dataframe. The following arguments to the select() function after the first argument are the names of the columns...