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Learn Rstudio Ide: Quick, Effective, and Productive Data Science
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Matthew Campbell
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Apress
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2019
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Discover how to use the popular RStudio IDE as a professional tool that includes code refactoring support, debugging, and Git version control integration. This book gives you a tour of RStudio and shows you how it helps you do exploratory data analysis; build data visualizations with ggplot; and create custom R packages and web-based interactive visualizations with Shiny. In addition, you will cover common data analysis tasks including importing data from diverse sources such as SAS files, CSV files, and JSON. You will map out the features in RStudio so that you will be able to customize RStudio to fit your own style of coding.Finally, you will see how to save a ton of time by adopting best practices and using packages to extend RStudio. Learn RStudio IDE is a quick, no-nonsense tutorial of RStudio that will give you a head start to develop the insights you need in your data science projects.What You Will Learn- Quickly, effectively, and productively use RStudio IDE for building data science applications- Install RStudio and program your first Hello World application- Adopt the RStudio workflow - Make your code reusable using RStudio- Use RStudio and Shiny for data visualization projects- Debug your code with RStudio - Import CSV, SPSS, SAS, JSON, and other dataWho This Book Is ForProgrammers who want to start doing data science, but dont know what tools to focus on to get up to speed quickly.

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Matthew Campbell

Learn RStudio IDE Quick, Effective, and Productive Data Science

Matthew Campbell

Yardley, PA, USA

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484245101 . For more detailed information, please visit http://www.apress.com/source-code .

ISBN 978-1-4842-4510-1 e-ISBN 978-1-4842-4511-8

https://doi.org/10.1007/978-1-4842-4511-8

Matthew Campbell 2019

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.

Table of Contents

About the Author and About the Technical Reviewer

About the Author

Matthew Campbell

is a research data analyst who has worked on data problems in educational psychology, psychometrics and other research areas for over 15 years. Matt is passionate about technology which he uses to create stories with data, find insights that inform analysis and solves problems for businesses. He received his Masters in Management of Information Systems from the University of Phoenix and Bachelors in Psychology from Pennsylvania State University. Matt has authored 4 books on mobile app development, lead coding bootcamps and worked on various large-scale research projects.

About the Technical Reviewer

Prachee Chaturvedi

is Data Scientist and Digital Strategy Lead in Regulatory RD in Bayer and - photo 3

is Data Scientist and Digital Strategy Lead in Regulatory, R&D, in Bayer and was a member of Monsantos Leadership Development Program. She received her PhD in Biological Engineering from University of Florida, Gainesville, FL, and Bachelors in Mechanical Engineering from HBTI, Kanpur, India. Currently, Prachee is focused on establishing comprehensive digital strategy for Regulatory Sciences and developing digital tools for automated and on-demand analysis for Regulatory Safety Data Submission at Bayer Crop Science R&D. She has authored 15 scientific publications, 2 book chapters, and led various cross functional scientific projects.

Matthew Campbell 2019

Matthew Campbell Learn RStudio IDE https://doi.org/10.1007/978-1-4842-4511-8_1

1. Installing RStudio

Matthew Campbell

(1)

Yardley, PA, USA

RStudio is an integrated development environment (IDE) that adds modern features like syntax highlighting and code refactoring to R. The strength of RStudio is that it brings all the features that you need together in one place. Before we install RStudio, we will need to install the latest version of R for our operating system as well as another program called git. Both of these software packages are integrated into RStudio, but they do not come with the RStudio IDE.

R is a statistical programming language and we need this language to use RStudio. Git is a popular version control system that will be integrated into RStudio. Version control systems like git are used to manage copies of the code you are working on. Git helps you see the differences between versions of a file so that you can get insight into why one version of your code behaves differently than another version. Git is also required to use Github, an online community where programmers share code and projects.

Install R

R is a free open source software used for statistical programming and graphics. You can get the latest version of R from CRAN, the Comprehensive R Network . This is a network of mirrored servers that have copies of R as well as R packages. R packages are extensions to the core R programming language contributed by R users in the community.

To install R, go to this URL: .

Figure 1-1

CRAN mirrors

Scroll down the list of servers and find the one that is geographically closest to you and then click the link. Dont worry too much about getting the absolute closest server , just picking any from your country should be fine. If you find a broken link, simply go back and try another server. You will be brought to a new screen that lists versions of R for Windows and Mac as you can see in Figure .

Figure 1-2

Installing R on CRAN

Click the link for your operating system. Each operating system has different instructions to set up R .

Installing R on Mac

If you are installing R on Mac , you will be presented with a lot of detail about the different R releases. Most people will simply go to the area toward the middle of the page under the heading Latest release as shown in Figure : and then click the file that ends in with the .pkg file extension. The exact name of the file will be different based on the most current release of the installer.

Figure 1-3

Mac R installer

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