Development

This Community Page article describes a data management workflow that can be readily implemented by small research teams and which solves the core challenges of managing regularly updating data. It includes a template repository and tutorial to assist others in setting up their own regularly updating data management systems.

This is a guide on how to conduct Meta-Analyses in R.

When working on large and complicated financial models in Microsoft Excel, it can be quite challenging to document them clearly for the users’ ease of use and understandability. We will discuss here several Excel models best practices and other useful tips and tricks that users can apply to maintain and audit your

Unlock Excel's most powerful features with these advanced pivot table tips and tricks that will help you master pivot tables and become a data ninja!

To help you land your ideal data science job, we've come up with Python interview questions you should be prepared to answer.

This tour shows how to create and execute Excel workflows with Spreadsheet Router.

Explore the concept of Data Analytics with R, Tableau and Excel. Also, understand the features, applications and working of these tools.

A curated list of awesome ETL frameworks, libraries, and software. - pawl/awesome-etl

Shiny 1.0.4 is now available on CRAN. To install it, run: install.packages("shiny") For most Shiny users, the most exciting news is that file inputs now support dragging and dropping: It is now possible to add and remove tabs from a tabPanel, with the new functions insertTab(), appendTab(), prependTab(), and removeTab(). It is also possible to hide and show tabs with hideTab() and showTab(). Shiny also has a new a function, onStop(), which registers a callback function that will execute when the application exits.

shinytest

Data Science is not just for Engineers and Statisticians. Exploratory makes it for Everyone.

Excel is great, but trying to figure it out how to use Excel on your own can get you only so far because it isn’t intuitive. But if you use the techniques and tips in this tutorial, you’ll be able...

Learn more about how and when to perform Exploratory Data Analysis - and how to use EDA data for powerful Business Intelligence.

Learn how to use Chrome DevTools to find and fix JavaScript bugs.

‎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.

"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...