Programming in R

Programming in R (11)

  • SAS(R) Certification Prep Guide: Advanced Programming for SAS(R)

    By SAS Institute Inc | This guide helps prepare you to take the SAS Advanced Programming for SAS 9 exam. New or experienced SAS users will find this guide to be an invaluable resource that covers the objectives tested on the exam. Major topics include SQL processing with SAS and the SAS macro language, advanced SAS programming techniques, and optimizing SAS programs. You will also become familiar with the enhancements and new functionality that are available in SAS®9

  • AdvancedR

    BY Marc Carlson, Valerie Obenchain, Herv´e Pag`es, Paul Shannon, Dan Tenenbaum, Martin Morgan1 | The Advanced R / Bioconductor Programming workshop provides experienced R and Bioconductor users and package developers with an opportunity to develop advanced skills for creating performant, re-usable software.


  • A First Course in Statistical Programming with R

    By W. John Braun and Duncan J. Murdoch | This is the only introduction you’ll need to start programming in R, the opensource language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R core development team, and by an established R author, this book comes with real R code that complies with the standards of the language.

  • INTRODUCTION TO: Scientific Programming and Simulation Using R

    By John M. Chambers, Torsten Hothorn, Duncan Temple Lang and Hadley Wickham | The scope of the series is wide, covering three main threads: • Applications of R to specific disciplines such as biology, epidemiology, genetics, engineering, finance, and the social sciences. • Using R for the study of topics of statistical methodology, such as linear and mixed modeling, time series, Bayesian methods, and missing data. • The development of R, including programming, building packages, and graphics.

  • Modeling Data With Functional Programming In R

    By Brian Lee Yung Rowe

  • Introduction to Scientific Programming and Simulation Using R, Second Edition

    By Owen Jones, Robert Maillardet, and Andrew Robinson | This book has two principal aims: to teach scientific programming and to introduce stochastic modelling. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems.

  • R Graphics

    By Paul Murrell | This book describes the graphics system in R. The first chapter provides an overview of the R graphics facilities. There are many pictures that demonstrate the variety and complexity of plots and diagrams that can be produced using R. There is a description of the different output formats that R graphics can produce and there is a description of the overall organization of the R graphics facilities, so that the user has some idea of where to find a function for a particular purpose.

    R Graphics

  • R Programming for Bioinformatics

    By Robert Gentleman | The purpose of this monograph is to provide a reference for scientists and programmers working on problems in bioinformatics and computational biology.

  • Hands-On Programming with R

    By Garrett Grolemund | Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools.

  • R Programming

    By Tutorialpoint | This tutorial is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.

    R Programming

  • R Programming for Data Science

    By Roger D. Peng | This book comes from my experience teaching R in a variety of settings and through different stages of its (and my) development. Much of the material has been taken from by Statistical Computing class as well as the R Programming⁵ class I teach through Coursera.