R

Customizing Package Build Options Overview There are three R package build commands used by the RStudio package development tools: R CMD check R CMD build R CMD INSTALL It's possible to c...

Shiny Visualisation modules, gadgets and functions - mrhopko/shinyVizModules

Provides functions that make it easier to construct Shiny apps that can dynamically construct reproducible R scripts.

I developed a web application to enable exploration of the data collected by a survey of software testers. I explain how R and Shiny can be used to create reactive web applications which make data accessible to a wider audience.

Overview Code snippets are text macros that are used for quickly inserting common snippets of code. For example, the fun snippet inserts an R function definition: If you select the snippet from th...

Bored and taught myself to make shiny apps today. Curious if anyone actually uses these at work though and some examples if so

The RStudio IDE reached version 1.0 this month. The IDE has come a long way since the initial release 5 and a half years ago. Many major features have been built: projects, package building tools, notebooks. During that same period, often hidden in the shadows, a growing list of smaller features has been changing lives. In celebration of version 1.0 this post hopes to spread fanfare for a few of these easy-to-miss tools.

The R task Force - R experts for all your needs

A general shiny app to import and export data to R. Note that this can be used as a starting point for any app that requires data to be loaded into Shiny. · GitHub

Live theme switcher for R Shinydashboard applications - nik01010/dashboardThemeSwitcher

A simple Shiny application to demonstrate the use of saving and restoring state using an RDS file. - radmuzom/shiny_module_save_restore_state

This R Shiny Tutorial will provide you with a detailed and comprehensive knowledge of R Shiny and how to create interactive Web Applications.

shiny.info is an open source package for R Shiny that allows developers to display diagnostic information in a div located in the corner of a Shiny app.

Recommended Packages Many useful R function come in packages, free libraries of code written by R's active user community. To install an R package, open an R session and type at the command line in...

Shiny app for building charts with a point-and-click interface - sicarul/shiny-chart-builder

HTTPS to Secure Your RStudio + Shiny App Work Environment Click any link in list below to jump to topic Creating a Friendly URL Route 53 to Host Domain and Create Subdomains AWS Certificate Manager for SSL keys AWS Elastic Load Balancers: HTTPS Redirection Installing Nginx & Creating Configuration Files I wanted to create this post as an addition to my previous post Running R on AWS EC2 and Logging into RStudio from Anywhere to show how to secure your AWS environment.

Creating a Shiny application to scrape data from IMDB's top movies.

R is a free software environment for statistical computing and graphics, available from The R Project for Statistical Computing. At Indiana University, R is ...

Data science is characterized among other things using open source tools. An advantage when working with open source languages such as R or Python is the large package world. This provides tools for numerous use cases and problems through the development within huge communities. The packages are organized in digital ...

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

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

‎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…

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().

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.

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,