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


Hands-On Exploratory Data Analysis with R: Become an expert in exploratory data analysis using R packages
Authors: Radhika Datar - Harish Garg
ISBN-10 书号: 178980437X
ISBN-13 书号: 9781789804379
Publisher Finelybook 出版日期: 2019-05-31
pages 页数: 266 pages


Book Description
Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills
Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You’ll also uncover the structure of your data,and you’ll learn 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 learn how to set up a data analysis environment with tools such as ggplot2,knitr,and R Markdown,using tools such as 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,identify hidden insights,and present your results in a business context.

What you will learn
Learn powerful R techniques to speed up 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
contents
1 Setting Up Our Data Analysis Environment
2 Importing Diverse Datasets
3 Examining,Cleaning,and Filtering
4 Visualizing Data Graphically with ggplot2
5 Creating Aesthetically Pleasing Reports with knitr and R Markdown
6 Univariate and Control Datasets
7 Time Series Datasets
8 Multivariate Datasets
9 Multi-Factor Datasets
10 Handling Optimization and Regression Data Problems
11 Next Steps

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