
Hands-On Exploratory Data Analysis with R
Become an expert in exploratory data analysis using R packages
Packt Publishing
Published on 31. May 2019
Book
Paperback/Softback
266 pages
978-1-78980-437-9 (ISBN)
Description
Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills
Key Features
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
Book DescriptionHands-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
Who this book is forHands-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.
Key Features
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
Book DescriptionHands-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
Who this book is forHands-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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 14 mm
Weight
503 gr
ISBN-13
978-1-78980-437-9 (9781789804379)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Radhika Datar | Harish Garg
Hands-On Exploratory Data Analysis with R
Become an expert in exploratory data analysis using R packages
E-Book
09/2024
Packt Publishing
€27.99
Available for download
Persons
Radhika Datar has more than 5 years' experience in software development and content writing. She is well versed in frameworks such as Python, PHP, and Java, and regularly provides training on them. She has been working with Educba and Eduonix as a training consultant since June 2016, while also working as a freelance academic writer in data science and data analytics. She obtained her master's degree from the Symbiosis Institute of Computer Studies and Research and her bachelor's degree from K. J. Somaiya College of Science and Commerce. BignumWorks Software LLP is an India-based software consultancy that provides consultancy services in the area of software development and technical training. Our domain expertise includes web, mobile, cloud app development, data science projects, in-house software training services, and up-skilling services
Content
Table of Contents
Setting Up Our Data Analysis Environment
Importing Diverse Datasets
Examining, Cleaning, and Filtering
Visualizing Data Graphically with ggplot2
Creating Aesthetically Pleasing Reports with knitr and R Markdown
Univariate and Control Datasets
Time Series Datasets
Multivariate Datasets
Multi-Factor Datasets
Handling Optimization and Regression Data Problems
Next Steps
Setting Up Our Data Analysis Environment
Importing Diverse Datasets
Examining, Cleaning, and Filtering
Visualizing Data Graphically with ggplot2
Creating Aesthetically Pleasing Reports with knitr and R Markdown
Univariate and Control Datasets
Time Series Datasets
Multivariate Datasets
Multi-Factor Datasets
Handling Optimization and Regression Data Problems
Next Steps