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Hands-On Exploratory Data Analysis with R
Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R: Become an expert in exploratory data analysis using R packages

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Profile Icon Radhika Datar Profile Icon Harish Garg
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$22.99
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3 (3 Ratings)
eBook May 2019 266 pages 1st Edition
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$22.99
Paperback
$32.99
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Renews at $19.99p/m
Arrow left icon
Profile Icon Radhika Datar Profile Icon Harish Garg
Arrow right icon
$22.99
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3 (3 Ratings)
eBook May 2019 266 pages 1st Edition
eBook
$22.99
Paperback
$32.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$22.99
Paperback
$32.99
Subscription
Free Trial
Renews at $19.99p/m

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Hands-On Exploratory Data Analysis with R

Setting Up Our Data Analysis Environment

In this chapter, we will look at how Exploratory Data Analysis (EDA) benefits businesses and has a significant impact on almost all vertical markets.

EDA is nothing but a pattern of analyzing datasets to summarize their main features, mostly with visual methods. We will list the R packages and tools that are required to do EDA. We will also focus on the installation procedure and setting up the packages for the EDA environment from an R perspective.

The following topics will be covered in this chapter:

  • The benefits of EDA across vertical markets
  • The most popular R packages for EDA
  • Installing the required R packages and tools

Technical requirements

R is an open source software that is platform independent. All you need to do is download the particular package from the following links:

The following steps are used to install R in your system:

The first time you open the RStudio user interface after installation, it will look as shown in the following screenshot:

  • You will also need to have prior knowledge of the R programming language. Packt has a wide range of books and video titles that are available for this purpose.
  • The code for this...

The benefits of EDA across vertical markets

Every organization today produces and relies on a lot of data in their everyday processes. Before making assumptions and decisions based on this data, organizations need to be able to understand it. EDA enables data analysts and data scientists to bring this information to the right people. It is the most important step on which a data-driven organization should focus its energy and resources.

Having practical tools in hand for carrying out EDA helps data analysts and data scientists produce reproducible and knowledgeable data analysis results. R is one of the most popular data analysis environments, so it makes sense to equip your data analysis teams with powerful R techniques to make the most of their EDA skills.

At the time of writing this book, there are more than 13,000 R packages available according to CRAN. You can get R packages...

Manipulating data

Before you can start exploring your data, you first need to import it into your data analysis environment. There are many types of data, ranging from plain data in comma-separated value files to binary data in databases. Different R packages are equipped to handle these different kinds of data expertly and to import them almost ready for use in our environment. Since we are using R and RStudio, we will describe some of the most powerful R packages to import data in the following sections:

  • readr: readr can be used to read flat, rectangular data into R. It works with both comma-separated and tab-separated values.
  • readxl: We can use the readxl package to read data from MS Excel files.
  • jsonlite: Web services have increasingly started to provide data in a JSON format. The jsonlite package is a good way to import this kind of data into R.
  • httr, rvest: httr, and rvest...

Installing the required R packages and tools

R packages can be installed in two ways: from the Terminal or from inside RStudio. Let's take a look at these.

Installing R packages from the Terminal

To install R packages from the Terminal, follow these steps:

  1. Open the Terminal
  2. Type and run the following command. Make sure to replace packagename1 with an actual package name, such as dplyr:
install.packages("packagename1")

Installing R packages from inside RStudio

To install R packages from RStudio, follow these steps:

  1. Open RStudio
  2. Click on Tools from the...

Summary

In this chapter, we have learned about the benefits that EDA can bring to businesses across various verticals. We introduced the R packages that will be used in this book to teach concepts related to EDA. We also learned how to set up and install these packages using both the Terminal and RStudio.

The next chapter will demonstrate practical, hands-on code examples that show how to handle reading all kinds of data into R for EDA. We will cover how to use advanced options while importing datasets, including delimited data, Excel data, JSON data, and data from web APIs. We will also look at how to scrape and read in data from the web and how to connect to relational databases from R. We will use R packages such as readr, readxl, jsonlite, httr, rvest, and DBI.

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Key benefits

  • Speed up your data analysis projects using powerful R packages and techniques
  • Create multiple hands-on data analysis projects using real-world data
  • Discover and practice graphical exploratory analysis techniques across domains

Description

Hands-On Exploratory Data Analysis with R will help you build a strong foundation in data analysis and get well-versed with elementary ways to analyze data. You will learn how to understand your data and summarize its characteristics. You'll also study the structure of your data, and you'll explore graphical and numerical techniques using the R language. This book covers the entire exploratory data analysis (EDA) process—data collection, generating statistics, distribution, and invalidating the hypothesis. As you progress through the book, you will set up a data analysis environment with tools such as ggplot2, knitr, and R Markdown, using DOE Scatter Plot and SML2010 for multifactor, optimization, and regression data problems. By the end of this book, you will be able to successfully carry out a preliminary investigation on any dataset, uncover hidden insights, and present your results in a business context.

Who is this book for?

Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation in data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete exploratory data analysis workflow.

What you will learn

  • Learn effective R techniques that can accelerate your data analysis projects
  • Import, clean, and explore data using powerful R packages
  • Practice graphical exploratory analysis techniques
  • Create informative data analysis reports using ggplot2
  • Identify and clean missing and erroneous data
  • Explore data analysis techniques to analyze multi-factor datasets

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 31, 2019
Length: 266 pages
Edition : 1st
Language : English
ISBN-13 : 9781789802085
Category :
Languages :
Concepts :
Tools :

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Product feature icon Access this title in our online reader with advanced features
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Product Details

Publication date : May 31, 2019
Length: 266 pages
Edition : 1st
Language : English
ISBN-13 : 9781789802085
Category :
Languages :
Concepts :
Tools :

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Table of Contents

16 Chapters
Section 1: Setting Up Data Analysis Environment Chevron down icon Chevron up icon
Setting Up Our Data Analysis Environment Chevron down icon Chevron up icon
Importing Diverse Datasets Chevron down icon Chevron up icon
Examining, Cleaning, and Filtering Chevron down icon Chevron up icon
Visualizing Data Graphically with ggplot2 Chevron down icon Chevron up icon
Creating Aesthetically Pleasing Reports with knitr and R Markdown Chevron down icon Chevron up icon
Section 2: Univariate, Time Series, and Multivariate Data Chevron down icon Chevron up icon
Univariate and Control Datasets Chevron down icon Chevron up icon
Time Series Datasets Chevron down icon Chevron up icon
Multivariate Datasets Chevron down icon Chevron up icon
Section 3: Multifactor, Optimization, and Regression Data Problems Chevron down icon Chevron up icon
Multi-Factor Datasets Chevron down icon Chevron up icon
Handling Optimization and Regression Data Problems Chevron down icon Chevron up icon
Section 4: Conclusions Chevron down icon Chevron up icon
Next Steps Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 0%
2 star 0%
1 star 66.7%
Merrill Aug 27, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent , well written Informative
Amazon Verified review Amazon
Nyxdia Nov 10, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
The book has uncountable code mistakes, there is no proper justification for some data analysis decisions. But the worst of all is that there is no coherence nor continuity from one topic to the next, dropping subjects without reaching to a conclusion. It's like loose pieces of text have been carelessly put together.
Amazon Verified review Amazon
Diego Aug 10, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Sólo una colección de ejercicios sin casi explicación.
Amazon Verified review Amazon
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