San Francisco, California
London, United Kingdom
RStudio is hosting Hadley Wickham's two day, R Master Development course in New York City this September:
Sept. 8, 2014 - Advanced R programming
Sept. 9, 2014 - R package development
Course Instructor: Hadley Wickham - RStudio Chief Scientist
This is a two-day workshop. All participants will receive a copy of all slides, exercises, data sets, and R scripts used in the course.
What should I bring?
Day 1 - Advanced R programming
Monday, Sept. 8, 2014
Do more with less code, by mastering advanced features of the R programming language.
Who should take this course?
This class will be a good fit for you if you have some experience programming in R already. You should have written a number of functions, and be comfortable with R’s basic data structures (vectors, matrices, arrays, lists, and data frames). You will find the course particularly useful if you’re an experienced R user looking to take the next step, or if you’re moving to R from other programming languages and you want to quickly get up to speed with R’s unique features.
Learn to write better R code by using the advanced features of the R programming language. Based on the programming experience of Hadley Wickham (author of over 30 R packages) and the RStudio team, this course will teach you how to use R to solve harder problems with fewer lines of code.
- Become a skilled R programmer who knows the best ways to craft R functions and to use R’s object oriented programming (OOP) features
- Learn advanced R techniques to compute on the language, control object evaluation within R functions, and apply R’s scoping rules
- Write correct, fast, and maintainable R code built around the mantra, “Don’t repeat yourself!”
What will you learn?
How to write R programs like an expert. Through a series of demonstrations and hands on exercises, you will learn about advanced R features to write fast and maintainable code.
Controlling evaluations - Unlike most languages, R provides powerful tools for controlling when and where evaluation occurs. This lets you create functions tailored for interactive use that minimize typing with a little magic.
- Mastering base functions such as
- Capture user input without evaluating it
- Control when and where R evaluates expressions and calls
- R’s rules for dynamic and lexical scoping
- Writing code that modifies code
First class functions - At heart, R is a functional programming language, and functions can be used in many more ways than most R users assume. R has first class functions which means you can write functions that return functions, take functions as input, and save function in lists. This gives you a powerful set of tools for dealing with a broad class of problems.
- Create anonymous functions
- Write closures – functions that return functions
- Build higher-order functions – functions that take other functions as input
- Work with lists of functions
Object oriented programming - Though a functional language, R contains three systems of object oriented programming (OOP) features. These features revolve around the concepts of classes and methods and can dramatically simplify code. We’ll focus on S3, the oldest and simplest form of OOP, but will also touch on S4 and R5 (reference classes).
- How to interpret base R functions that use OOP techniques
- The details of S3 generic functions, methods, and classes
- The differences between R’s three OOP classes: S3, S4, and R5
Best practices in R - Even advanced techniques can be ruined by poor planning. When you use advanced techniques, you must be especially careful to make your code clear and lucid. Throughout this course you’ll learn practical coding tips and techniques.
- Create correct, maintainable, and fast R code
- Create understandable code that communicates
- Organize R programs around the “DRY” principle – “Don’t repeat yourself!”
Day 2 - Software development with R
Tuesday, Sept. 9, 2014:
Learn basic principles of software development in R to produce well-tested, well-documented, and easily distributed code.
Who should take this course?
This class will be a good fit for you if you've developed a number of R scripts, and now you want to learn:
a more efficient workflow, iterating between writing code and checking that it works
how to document your code so others (including future you!) can understand what's going on
automated testing principles, so that if you accidentally break something in your code you find out right away
how to package and distribute your code to others, whether it's inside your research group, your company, or to the whole world.
The key to well-documented, well-tested and easily-distributed R code is the package. In this course you’ll learn that creating packages is actually really easy. The key to making packages so easy that they become your default way of organizing code are the R packages
testthat, all of which you'll learn about in this class.
Transform existing R code into packages that others can easily download and use.
Learn a fluid development process facilitated by the
Write inline documentation with
Develop automated tests with the
testthatpackage to ensure that your code is correct today, and continues to be correct in the future.
Recognize common errors detected by the strict
R CMD check.
Release your package into the wild, through the official CRAN repository or to github.
What will you learn?
How to develop an idea into a published, stable R package. Through a series of demonstrations and hands on exercises, you will learn to use advanced R features to quickly build, document, test, and publish R packages.
Introduction to R packages - Packages are one of the most useful tools in the R programming language. Packages make it easy to share your code with friends, coworkers, or even the global R community by building.
- What is a package?
- Working with libraries and installing packages
- The development cycle
Documentation and namespaces - For your code to be useful, it must be both well-documented, and it must work regardless of what other packages are currently loaded. We will show you how to take solve these problems using roxygen2 to generate both documentation and a namespace.
- Documenting functions, objects, methods and datasets
- Formatting text in help pages
- Exporting functions to a package namespace
- Using functions from other packages in your package
Automated testing - Maintaining R code requires advanced planning. You can simplify debugging, quickly spot unintended consequences, and generally ensure that your package is stable by creating thoughtful unit tests.
- Transition from the informal interactive tests you're currently doing to formal automated tests
- Make code maintenance easier with unit tests
- Seamlessly integrate unit testing into your workflow with devtools
- Use travis-CI to automatically run all tests every time you push a change to github
Releasing your package - If you want others to use your package, you need to be aware of the options for distributing it from the very informal (email), to the more formal (github) to the most strict and rigorous (CRAN). You’ll learn about R Core’s strict standards for inclusion in CRAN (which are worthwhile understanding even if you don't want to submit) and how to market your package after it has been released.
- Using and passing R CMD check
- Submitting a package to CRAN
- Distributing a package via github
- Marketing your package after its release
- Simplifying package development with source code control
Due to limited seating, discount pricing is not available for this workshop.
In certain cases, we may need to cancel this workshop due to circumstances beyond our control or otherwise. If this happens, RStudio will refund all registration fees for those who signed up. RStudio is not responsible for any related expenses incurred by registered attendees (including but not limited to travel and hotel expenses).
Until Aug 24, 2014 - Full refund, less 10% of registration fees
Aug 24, 2014 to Aug 31, 2014 - 50% refund of registration fees
Sept 01, 2014 and after - No refund available
All public workshops hosted by RStudio come with a no-questions-asked money-back guarantee.
When & Where
RStudio™ offers open source and enterprise-ready professional software packages and products for R. The free RStudio integrated development environment (IDE), Shiny interactive application framework, and R Markdown reproducible reporting package, are just a few of the many popular tools we provide to make using R a better experience. Please contact us at http://www.rstudio.com to learn how RStudio Server Pro and Shiny Server Pro can give your organization the professional environment R developers need to deliver the interactive dashboards, applications and reporting experiences business users want.