roads
: The R dataframe containing the original dataroads.2011
: The2011
column of the roads dataframenot.na
: An array of logical that corresponds to the non-NA values of the 2011 columnroads.2011.cleaned
: The2011
column from the roads dataframe with theNA
values removedtotal.2011
: The sum of the2011
valuesroads.num
: The roads dataframe without the first column (just the numerical data)roads.means
: A vector containing the mean value of each rowroads.keep
: A vector of logical that is True for rows for which the mean is less than 2000 (non-outliers)roads2
: The roads dataframe with outliers removedroads.num2
: The roads dataframe with the first column removed (just the numerical data) and the outliers removedroads.means2
: The vector of means with outliers removedroads.num2.rowsums
: The sum of the values in each row with the outliers removed (where a sum of 0 indicates that all values in the row are NA)roads.keep2
: A logical vector used to index the rows for which...
Practical Data Wrangling
By :
Practical Data Wrangling
By:
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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Programming with Data
Introduction to Programming in Python
Reading, Exploring, and Modifying Data - Part I
Reading, Exploring, and Modifying Data - Part II
Manipulating Text Data - An Introduction to Regular Expressions
Cleaning Numerical Data - An Introduction to R and RStudio
Simplifying Data Manipulation with dplyr
Getting Data from the Web
Customer Reviews