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Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R

By : Radhika Datar, Harish Garg
2.3 (3)
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Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R

2.3 (3)
By: Radhika Datar, Harish Garg

Overview of this book

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.
Table of Contents (17 chapters)
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Section 1: Setting Up Data Analysis Environment
7
Section 2: Univariate, Time Series, and Multivariate Data
11
Section 3: Multifactor, Optimization, and Regression Data Problems
14
Section 4: Conclusions

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...

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