R Programming By Example: Practical, hands-on projects to help you get started with R

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Understanding Votes with Descriptive Statistics

This chapter shows how to perform a descriptive statistics analysis to get a general idea about the data we're dealing with, which is usually the first step in data analysis projects and is a basic ability for data analysts in general. We will learn how to clean and transform data, summarize data in a useful way, find specific observations, create various kinds of plots that provide intuition for the data, use correlations to understand relations among numerical variables, use principal components to find optimal variable combinations, and put everything together into code that is reusable, understandable, and easily modifiable.

Since this is a book about programming with R and not about doing statistics with R, our focus will be on the programming side of things, not the statistical side. Keep that in mind while reading it...

Key benefits

Get a firm hold on the fundamentals of R through practical hands-on examples

Get started with good R programming fundamentals for data science

Exploit the different libraries of R to build interesting applications in R

Description

R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.

What you will learn

Discover techniques to leverage R's features, and work with packages

Perform a descriptive analysis and work with statistical models using R

Work efficiently with objects without using loops

Create diverse visualizations to gain better understanding of the data

Understand ways to produce good visualizations and create reports for the results

Read and write data from relational databases and REST APIs, both packaged and unpackaged

Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

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John Beck Aug 29, 2018

Great content and very well explained. At first I was hesitating about buying it because of the other comment here, but I had heard good things about the book so I bought it, and I’m glad I did. Never had a problem with the data files or source code files. Easy to download and follow, and great material. Thanks! Definitely recommended.

Amazon Verified review

John Stewart Sep 06, 2019

I know programming at an intermediate level, and I've recently started to focus in Data Science. This book was the right one for me! It does a great job at introducing R from a programmer's perspective, not only showing you how to do interesting Data Science projects, but actually explaining the underlying programming concepts which I had not seen well explained in other R books. Thanks!

S N Jul 10, 2018

Pros:-The book seems to have practical, detailed examples and quality instruction.Major Con:-The data files required to use the book are NOT available. The author only provides links to Git Hub "pointer files", which have to be downloaded via a separate command prompt utility (Git LFS). The author provides vague, incorrect instructions for how to download the files. You will need to spend several hours researching and troubleshooting just to figure out how to correctly run the utility and which commands to run. What's worse: once you get the utility commands to successfully execute, you'll get an error message indicating that the .csv files are not available on the server.-The publisher and author REFUSE to provide accessible links to the .csv files, or correct/detailed instructions for how to retrieve them. If you contact the publisher or author, the author will ignore your feedback about error messages and will simply copy/paste vague instructions from Errata which he posted previously on the publisher's website (i.e. indicating that previous people have complained).I cannot recommend this book at all until the author/publisher corrects this. Very unprofessional behavior.

Amazon Customer Oct 22, 2018

R Programming By Example is not worth. Instead to buy this book we have somany alternate books which they explain very clear.

Package	Used for
`ggplot2`	High-quality graphs
`viridis`	Color palette for graphs
`corrplot`	Correlation plots
`ggbiplot`	Principal components plots
`progress`	Show progress for iterations

R Programming By Example: Practical, hands-on projects to help you get started with R

What do you get with Print?

R Programming By Example

Understanding Votes with Descriptive Statistics

This chapter's required packages

The Brexit votes example

Cleaning and setting up the data

Summarizing the data into a data frame

Getting intuition with graphs and correlations

Visualizing variable distributions

Creating a new dataset with what we've learned

Building new variables with principal components

Putting it all together into high-quality code

Summary

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Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Contact Details

Shipping Address

Billing Address

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

People who bought this also bought

About the authors

FAQs