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Exploring Data - Creating Reactive Web Apps with R and Shiny
Exploring Data - Creating Reactive Web Apps with R and Shiny
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.
·blog.scottlogic.com·
Exploring Data - Creating Reactive Web Apps with R and Shiny
RStudio IDE Easy Tricks You Might've Missed · R Views
RStudio IDE Easy Tricks You Might've Missed · R Views
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.
·rviews.rstudio.com·
RStudio IDE Easy Tricks You Might've Missed · R Views
Quick list of useful R packages – RStudio Support
Quick list of useful R packages – RStudio Support
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...
·support.rstudio.com·
Quick list of useful R packages – RStudio Support
HTTPS to Secure Your RStudio Shiny App Work Environment
HTTPS to Secure Your RStudio Shiny App Work Environment
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.
·jagg19.github.io·
HTTPS to Secure Your RStudio Shiny App Work Environment
Good practices in R programming
Good practices in R programming
R is a free software environment for statistical computing and graphics, available from The R Project for Statistical Computing. At Indiana University, R is ...
·kb.iu.edu·
Good practices in R programming
Package management: Using repositories in production systems | R-bloggers
Package management: Using repositories in production systems | R-bloggers
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 ...
·r-bloggers.com·
Package management: Using repositories in production systems | R-bloggers
Shiny 1.0.4
Shiny 1.0.4
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.
·blog.rstudio.com·
Shiny 1.0.4
‎R Programming Compiler on the App Store
‎R Programming Compiler on the App Store
‎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…
·apps.apple.com·
‎R Programming Compiler on the App Store
Building a shiny app with drag and drop data interface
Building a shiny app with drag and drop data interface
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…
·pradeepadhokshaja.wordpress.com·
Building a shiny app with drag and drop data interface
pins: Pin, Discover and Share Resources | RStudio Blog
pins: Pin, Discover and Share Resources | RStudio Blog
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().
·blog.rstudio.com·
pins: Pin, Discover and Share Resources | RStudio Blog
Productionizing Shiny and Plumber with Pins · R Views
Productionizing Shiny and Plumber with Pins · R Views
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.
·rviews.rstudio.com·
Productionizing Shiny and Plumber with Pins · R Views
Odd Hypothesis: Deploying Desktop Apps with R
Odd Hypothesis: Deploying Desktop Apps with R
(Update 4: 2016-08-19) I made many significant updates / improvements to this deployment method which are documented in a more recent blog p...
·oddhypothesis.blogspot.com·
Odd Hypothesis: Deploying Desktop Apps with R
Production
Production
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.
·rstudio.com·
Production