The stuff you need to know about R, besides statistical analysis.

Online syntax highlighting for more than 100 languages, including C#, Java, PHP, Basic, Perl, Python, Pascal, SQL, JavaScript and others including rare and obscure ones!.

Writing R Extensions covers how to create your own packages, write R help files, and the foreign language (C, C++, Fortran, …) interfaces.

DASE Data Analysis in Software Engineering

In 2019, RStudio spent over 50% of its engineering resources on open-source software, and led contributions to over 250 open-source projects, targeting a broad range of areas.

Grammarly makes sure everything you type is easy to read, effective, and mistake-free. Try it today:

This is a workshop for RStudio::conf(2020) in San Francisco, California

This guide walks through the process of developing an R package

A guide to authoring books with R Markdown, including how to generate figures and tables, and insert cross-references, citations, HTML widgets, and Shiny apps in R Markdown. The book can be exported to HTML, PDF, and e-books (e.g. EPUB). The book style is customizable. You can easily write and preview the book in RStudio IDE or other editors, and host the book wherever you want (e.g. bookdown.org).

A guide to building scalable Shiny Datbase applications

This is an example of writing an R package using the Literate Programming (LP) technique, implemented through the knitr package and Makefile. It only shows you the idea, and I do not mean you must use …

This book showcases short, practical examples of lesser-known tips and tricks to helps users get the most out of these tools. After reading this book, you will understand how R Markdown documents are transformed from plain text and how you may customize nearly every step of this processing. For example, you will learn how to dynamically create content from R code, reference code in other documents or chunks, control the formatting with customer templates, fine-tune how your code is processed, and incorporate multiple languages into your analysis.

Master the art of data wrangling with the R programming language.

Gérer l'appel dynamique aux modules.

Handsontable is a composite spreadsheet component for apps and websites written in JavaScript and HTML

Learn how to program, improve your career and develop your people skills. Let’s make thecomplex simple and tackle the mental aspects of being a software developer together.

A guide to making visualizations that accurately reflect the data, tell a story, and look professional.

Teaching researchers the foundational computing skills they need to get more done in less time

Yixuan's blogs on statistcs, programming, and more

R-Shiny developer and consultant with a MSc in Bioinformatics and a Bachelor of Computer Science. Previously a software engineer at Google, IBM, and Wish.com.

In this post, I talk about performance through an efficient algorithm I developed for finding closest points on a map. This algorithm uses both concepts from mathematics and algorithmics. Problem to solve This problem comes from a recent question on StackOverflow. I have two matrices, one is 200K rows long, the other is 20K. For each row (which is a point) in the first matrix, I am trying to find which row (also a point) in the second matrix is closest to the point in the first matrix. This is the first method that I tried on a sample dataset: # Test dataset: longitude and latitude pixels.l...

Here are my notes from a recent talk I gave on vectorization at a Davis R Users’ Group meeting. Thanks to Vince Buffalo, John Myles White, and Hadley Wickham for their input as I was preparing this. Feedback welcome! Beginning R users are often told to “vectorize” their code. Here, I try to explain why vectorization can be advantageous in R by showing how R works under the hood. Now, remember, premature optimization is the root of all evil (Knuth).