Learning RStudio for R Statistical Computing


Learning RStudio for R Statistical Computing
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  • A complete practical tutorial for RStudio, designed keeping in mind the needs of analysts and R developers alike
  • Step-by-step examples that apply the principles of reproducible research and good programming practices to R projects
  • Learn to effectively generate reports, create graphics, and perform analysis, and even build R-packages with RStudio

Book Details

Language : English
Paperback : 126 pages [ 235mm x 191mm ]
Release Date : December 2012
ISBN : 1782160604
ISBN 13 : 9781782160601
Author(s) : Mark P.J. van der Loo, Edwin de Jonge
Topics and Technologies : All Books, Big Data and Business Intelligence, Open Source

Table of Contents

Preface
Chapter 1: Getting Started
Chapter 2: Writing R Scripts and the R Console
Chapter 3: Viewing and Plotting Data
Chapter 4: Managing R Projects
Chapter 5: Generating Reports
Chapter 6: Using RStudio Effectively
Index
  • Chapter 1: Getting Started
    • RStudio at a glance
    • Installing RStudio
      • Installing R
        • Installing R on Windows and Mac OS X
        • Installing R on Linux
    • Building R from source
    • Building R using Windows
    • Installing RStudio
      • Installing RStudio Server
      • Installing R packages
    • Overview: A first R session
      • Keyboard shortcuts
      • Getting help
        • What if I uninstall RStudio?
      • Further reading
      • Summary
    • Chapter 2: Writing R Scripts and the R Console
      • Moving around RStudio
      • Features of the R console
        • Executing commands
        • Command history
        • Command completion
          • Completion of functions and arguments
          • Object completion
          • Completion of filenames
        • Keyboard shortcuts for the console
      • Features of the source editor
        • Editing R scripts
          • Syntax highlighting
          • Indenting code
          • Commenting code
          • Find and replace
        • Folding, sectioning, and navigation
          • Code folding
          • Code navigation
          • Code sections
        • Code execution
        • Summary
      • Chapter 3: Viewing and Plotting Data
        • Viewing data and the object browser
        • Plotting
          • Zoom
          • Export
          • Navigation
        • Interactive plotting with the manipulate package
          • The manipulate function
          • Using more options of manipulate
          • Advanced topic: retrieving plot parameters from manipulate
        • Summary
        • Chapter 4: Managing R Projects
          • R projects
            • Creating an R project
            • Directory structure and file manipulations
          • Version control
            • Introduction to version control
              • Installing GIT or Subversion
            • Version control for single-person projects
              • GIT
              • Subversion
          • Working with a team
          • Further reading
          • Summary
          • Chapter 5: Generating Reports
            • Prerequisites for report generation
            • Notebook
              • Notebook options
              • Publishing a notebook
            • R Markdown and Rhtml
              • Workflow for R Markdown
              • An extended example
              • An introduction to Markdown syntax
              • Rhtml
            • Code chunks
              • Chunk syntax and options
                • RMarkdown: .Rmd files
                • Rhtml: .Rhtml files
                • LaTeX: .Rnw files
              • RStudio's chunk support and keyboard shortcuts
            • LaTeX
            • Further reading
            • Summary
            • Chapter 6: Using RStudio Effectively
              • Additional features for function writing
                • Function extraction
                • Function navigation
              • Introduction to package writing
                • Prerequisites
                • Basic structure and workflow
                • Creating the package directory structure
                  • Documenting functions with Roxygen2
                • Building your package with devtools
                  • More about the devtools package
                  • Publishing your package
              • Summary

              Mark P.J. van der Loo

              Mark van der Loo obtained his PhD at the Institute for Theoretical Chemistry at the University of Nijmegen (The Netherlands). Since 2007 he has worked at the statistical methodology department of the Dutch official statistics office (Statistics Netherlands). His research interests include automated data cleaning methods and statistical computing. At Statistics Netherlands he is responsible for the local R center of expertise, which supports and educates users on statistical computing with R. Mark has been teaching R for several years and coauthored a number of R packages that are available via CRAN: editrules, deducorrect, rspa, and extremevalues. A list of publications can be found via http://www.markvanderloo.eu.

              Edwin de Jonge

              Edwin de Jonge has worked for more than 15 years at the Dutch official statistics office (Statistics Netherlands). With a background in theoretical and computational solid state physics (MSc), he started in the statistical computing department. Currently he works in the statistical methodology department. His research interests include data visualization, data analysis, and statistical computing. He trained over 150 people in a workshop entitled “Graphical Analysis with R”. Edwin has coauthored several R packages that are available via CRAN: tabplot, tabplotd3, ffbase, whisker, editrules, and deducorrect.

              Submit Errata

              Please let us know if you have found any errors not listed on this list by completing our errata submission form. Our editors will check them and add them to this list. Thank you.


              Errata

              - 7 submitted: last submission 19 Jul 2013

              Errata type: typo | Page number: 33 | Errata date: 13 May 2013

              This line on fifth row under Mac column in the table

              Ctrl+Shift+F

              Should be

              Command+Shift+F

               

              Errata type: typo | Page number: 32 | Errata date: 14 May 2013

              This line on fourth and fifth row under Mac column in the table 

              Ctrl+Option+right arrow

               

              Ctrl+Up / Ctr+Alt+left arrow


              Should be

              Command + Up / Command+Option+right arrow

              Command + Up / Command+Option+left arrow

               

               

              Errata type: typo | Page number: 37 | Errata date: 14 May 2013

              This values under Mac column in the table should be

              Option+L

              Shift+Option+L

              Option+A

              Shift+Option+A

              Errata type: typo | Page number: 39 | Errata date: 14 May 2013

              This values under Mac column in the table should be

              Option+Shift+J

              Command+.

              F2

              Command+F9

              Command+F10

               

              You can find the shortcuts, which RStudio provide, on the following page:

              http://www.rstudio.com/ide/docs/using/keyboard_shortcuts

              Errata type: code | Page number: 14 | Errata date: 25 May 2013

              This code

              names(abalone) <- c("Sex","Length","Diameter","Height","Whole weight","Shucked weight","Viscera weight","Shell weight","Rings")

              Should be

              names(abalone) <- c("Sex","Length","Diameter","Height","Whole.weight","Shucked.weight","Viscera.weight","Shell.weight","Rings")

              Errata type: code | Page number: 50 | Errata date: July 19, 2013

              This code

              plot(abalone$Length, predict(model), col=col)

              Should be

              plot(abalone$Length, predict(fit), col=col)

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              Frequently bought together

              Learning RStudio for R Statistical Computing +    R Statistical Application Development by Example Beginner's Guide =
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              the second eBook
              Price for both: €24.85

              Buy both these recommended eBooks together and get 50% off the cheapest eBook.

              What you will learn from this book

              • Learn to install and run RStudio on a desktop or a web server
              • Acquaint yourself with the latest and advanced R console features
              • Perform code editing and navigation
              • Learn to create advanced and interactive graphics
              • Effectively manage your R project and project files
              • Learn to build R extension packages
              • Perform reproducible statistical analyses within your R projects
              • Learn your way through getting

              In Detail

              Data is coming at us faster, dirtier, and at an ever increasing rate. The necessity to handle many, complex statistical analysis projects is hitting statisticians and analysts across the globe. This book will show you how to deal with it like never before, thus providing an edge and improving productivity.

              "Learning RStudio for R Statistical Computing" will teach you how to quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics. R developers will learn about package development, coding principles, and version control with RStudio.

              This book will help you to learn and understand RStudio features to effectively perform statistical analysis and reporting, code editing, and R development.

              The book starts with a quick introduction where you will learn to load data, perform simple analysis, plot a graph, and generate automatic reports. You will then be able to explore the available features for effective coding, graphical analysis, R project management, report generation, and even project management.

              "Learning RStudio for R Statistical Computing" is stuffed with feature-rich and easy-to-understand examples, through step-by-step instructions helping you to quickly master the most popular IDE for R development.

              Approach

              A practical tutorial covering how to leverage RStudio functionality to effectively perform R Development, analysis, and reporting with RStudio.

              Who this book is for

              The book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio functionality to ease their development efforts. Familiarity with R is assumed. Those who want to get started with R development using RStudio will also find the book useful. Even if you already use R but want to create reproducible statistical analysis projects or extend R with self-written packages, this book shows how to quickly achieve this using RStudio.

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