If you need to create and manage complex statistical analysis projects, this book could be a catalyst for great things. In clear, practical chapters it teaches you how to employ Rstudio's powerful features to perform R statistical computing.
Overview
- 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.
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
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
Approach
A practical tutorial covering how to leverage RStudio functionality to effectively perform R Development, analysis, and reporting with RStudio.
Who this book is written 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.
Mark 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 viahttp://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.
