R

‎Write R code directly on your iPhone, iPad and iPod Touch! This app is ideal for learning and testing code snippets! R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used am…

Introduction Data visualization is an important aspect of the data science work flow. This app enables the analyst to understand the data in question. In this post, we will build an application whi…

Describes the best practices for setting automated test architecture for R Shiny apps. Automate and test early and often with unit tests, user interface tests, and performance tests.

Today we are excited to announce the pins package is available on CRAN! pins allows you to pin, discover and share remote resources, locally or in remote storage. If you find yourself using download.file() or asking others to download files before running your R code, use pin() to achieve fast, simple and reliable reproducible research over remote resources. Pins You can use the pins package to: Pin remote resources locally to work offline and cache results with ease, pin() stores resources in boards which you can then retrieve with pin_get().

Producing an API that serves model results or a Shiny app that displays the results of an analysis requires a collection of intermediate datasets and model objects, all of which need to be saved. Depending on the project, they might need to be reused in another project later, shared with a colleague, used to shortcut computationally intensive steps, or safely stored for QA and auditing. Some of these should be saved in a data warehouse, data lake, or database, but write access to an appropriate database isn’t always available.

Three methods for using javascript code in Shiny applications to build faster apps, avoid unnecessary re-rendering, and add components beyond Shiny's limits. Part 2 of a 5 part series on super solutions for Shiny architecture.

config package for R. Contribute to rstudio/config development by creating an account on GitHub.

hello, How can I clean the R environment both using RStudio and the R console? Thanks,

(Update 4: 2016-08-19) I made many significant updates / improvements to this deployment method which are documented in a more recent blog p...

Transferring your Excel sheet to a Shiny app is perhaps the easiest way to create an enterprise ready dashboard. Shiny offers a comparable feature set to Excel as well as exciting new possibilities.

Note: This article is now several years old. If you have RStudio Connect, there are more modern ways of updating data in a Shiny app. Shiny applications are often backed by fluid, changing data. Data updates can occur at different time scales: from scheduled daily updates to live streaming data and ad-hoc user inputs. This article describes best practices for handling data updates in Shiny, and discusses deployment strategies for automating data updates.

RStudio's webinars offer helpful perspective and advice to data scientists, data science leaders, DevOps engineers and IT Admins. Presenters come from companies around the globe, as well as the RStudio staff.

Below we list the tools and innovations from the RStudio Conference in Austin, Texas that we found the most useful and exciting. As with last year’s conference, the event is well organized an…

James Blair, RStudio Scalability is a hot word these days, and for good reason. As data continues to grow in...

Running R scripts from the command line can be a powerful way to: Automate your R scripts Integrate R into production Call R through other tools or systems There are basically two Linux command...

If you use RStudio Connect to publish your Shiny app (and even if you don’t) take care with how your...

Contribute to yonicd/shinycovr development by creating an account on GitHub.

Design a computing infrastructure and choose packages that can set you and your learners on the happy path.

Safely Deploy R to Production

Does your R package work best with some configuration? You probably want it to be easily found by your package. Does your R package download huge datasets that don’t change much on the provider side? Maybe you want to save the corresponding data somewhere persistent so that things will go faster during the next R session. In this blog post we shall explain how an R package developer can go about using and setting persistent configuration and data on the user’s machine.

Learn R, R tutorials, R resources, blog posts and the latest updates about the statistical programming R language.

We deliver software development and ML solutions for Fortune 500 companies. We are an RStudio Full Service Certified Partner and global leaders in R Shiny.

A "work bench" to manage projects from within R.

As an update to this post, here's a list of the major events in R history since its creation: 1992: R development begins as a research project in Auckland, NZ by Robert Gentleman and Ross Ihaka 1993: First binary versions of R published at Statlib 1995: R first distributed as open-source software, under GPL2 license 1997: R core group formed 1997: CRAN founded (by Kurt Hornik and Fritz Leisch) 1999: The R website, r-project.org, founded 1999: First in-person meeting of R Core team, at inaugural Directions in Statistical Computing conference, Vienna 2000: R 1.0.0 released (February 29) 2000:...

Technical details and select highlights of my project to bring RStudio addins to VSCode.

Both R and distributed programming rank highly on my list of “good things”, so imagine my delight when two new... The post The Evolution of Distributed Programming in R appeared first on Mango Solutions.

Interact with basecamp api in R. Contribute to yonicd/basecamper development by creating an account on GitHub.