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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

By Radhika Datar , Harish Garg
$22.99 $9.99
Book May 2019 266 pages 1st Edition
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eBook
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$15.99 Monthly

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Hands-On Exploratory Data Analysis with R

Section 1: Setting Up Data Analysis Environment

We will start by setting up the R toolkit for exploratory data analysis, dig deep into the concepts of importing data into R, cleaning and manipulating data, and then move onto visualizing data, before producing reproducible data reports.

The following chapters will be covered in this section:

  • Chapter 1, Setting Up Our Data Analysis Environment
  • Chapter 2, Importing Diverse Datasets
  • Chapter 3, Examining, Cleaning, and Filtering
  • Chapter 4, Graphically Visualize Data with ggplot2
  • Chapter 5, Creating Aesthetically Pleasing Reports with Knitr and R Markdown
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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.

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 : May 31, 2019
Length 266 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789804379
Category :
Languages :
Concepts :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details


Publication date : May 31, 2019
Length 266 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789804379
Category :
Languages :
Concepts :

Table of Contents

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

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