Development

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.

"How do you get so much done?" This is a question I receive several times a week. And up until today...

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.

Learn Both SQL Server & PostgreSQL By Doing. Enhance Your Data Analytics Career With Real World Data Science Exercises

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

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.

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.

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…

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

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...

‎Learn about collection Powerful Apps for Developers featuring Working Copy - Git client, Prompt 2, Sketch Mirror, and many more on App Store. Enjoy these apps on your iPhone, iPad, and iPod touch.

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

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

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

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.

Article posted by Andy Merlino, on Friday, July 17nd, 2020

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.

The Keep Productive Blog is dedicated to personal and work productivity software reviews, how-to tutorials, PDFs, interviews & so much more.

News, Forums, Reviews, Help for Windows 10 and all things Microsoft.

I want to understand how the world works. This blog tracks my growth, the things I've learned, and how I'm leveraging them to do epic things.

In the following list we’ve collected 10 most popular tools which software developments experts, like Softarex, use in their everyday work.

Whether you work for a company, or as an indie, stay in the office or enjoy the digital nomad's lifestyle, being a software developer means everyday learning on the job. With that said, we suggest you considered the highly recommended reads on what proven #tools #software #developers #choose #in2019.

Software development is divided into several phases. Here are some best useful tools for software developers in 2019.