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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Rewriting code using dplyr


In the previous chapter, R was used to find the estimate of the total road length in 2011. Here are the steps that were completed in the previous chapter, written using dplyr verbs:

  • Filter out the rows with a mean greater than 2000
  • Filter out the rows in which all values are NA
  • Mutate the 2011 column to create a copy in which the NA values are replaced with the row mean
  • Select the new 2011 column and find the sum of its values

At the beginning of dplyr_intro.R, the first step should be to read artificial_roads_by_region.csv to an R dataframe as follows:

roads.lengths <- read.csv("data/artificial_roads_by_region.csv")

Next, In the following continuation of dplyr_intro.R, a copy of the original roads length data called roads.length2 is created. The row averages and the row sums of the roads.length2 dataframe are calculated and added as columns to the dataframe. These columns will help with the filtering steps.

roads.lengths2<-roads.lengths
roads.lengths2$mean_val ...