As a high-performing leader, we know you love using productivity products like the Full Focus Planner™. But any new system can seem overwhelming at first. Honestly, our planner can seem that way at first. With so many features built in, it’s hard to know where to start. You may wonder if you’re actually getting the […]

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().

In the pursuit of getting more things done, how do you build a productivity system that works for you? These 3 important elements will help you get started.

When used correctly, Google Calendar can be one of the best productivity tools. Learn how to effectively plan out your workday & stay on schedule.

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...

When you're working from home, even just on a pet project, staying on task and knowing when to do what can be a real challenge. We've put together some of the very best productivity apps that will help you keep focused and get it done.

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

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.

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,

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.

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

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…

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.

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...

‎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.

These productivity apps will help you keep track of todos, manage your email, back up your files, and stay organized whether you're working or at home.

The worlds most comprehensive Evernote skills training course. Created by top Evernote Certified Consultant Stacey Harmon.

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

Imagine if we had a learning curriculum for modern knowledge work. This curriculum would reliably produce elite performers, training them in the fundamental skills required to thrive in the digital age. It would impart concrete skills that could be generalized to any kind of knowledge work, not just one discipline or career path. In our ... Read more

By Tasshin Fogleman and James Stuber Introducing the Digital Productivity Coach – an interactive guide to digital productivity, available 24/7 to give you a concrete next step to improve your productivity skills. If you’re interested in improving your productivity, it’s not easy to identify where to start. There are dozens of tools and systems out ... Read more

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.