Links to Resources and Knowledge

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Resources

R

Page Description
R Self Assessment Are you ready for Shiny? Self Assessment Quiz
Using Python with RStudio Description of using Python with Rstudio
Happy Git with R Install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE. Develop a few key workflows that cover your most common tasks. Integrate Git and GitHub into your daily work with R and R Markdown.
Using R in a Regulatory Environment: some FDA perspectives PDF file form Paul Schuette (FDA) on using R in a Regulatory Environment
The Coprehensive R Archive Network CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R.
CRAN R Packages Available CRAN Packages by Name
Download RStudio RStudio is a set of integrated tools designed to help you be more productive with R. It includes a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting, viewing history, debugging and managing your workspace.
Download RStudio Server RStudio Server enables you to provide a browser based interface to a version of R running on a remote Linux server, bringing the power and productivity of the RStudio IDE to server-based deployments of R.
A Quick introduction to RStudio RStudio is not R or a “type” of R. It is a program that runs R and provides extra tools that are helpful when writing R code, kind of like how your operating system can run a web browser. This workshop will assume you are using RStudio to interact with R, although everything here can be done without RStudio. Most R users seem to use RStudio and we like it, so we recommend using it.
Installing RStudio for Windows A guide to installing RStudio for Windows
Setup an RStudio Server in Ubuntu A concise step-by-step guide to setup a Rstudio Server in Ubuntu Linux. The assumption is made that the server is already setup.
Setup a Shiny Server in AWS (Amazon) A step-by-step guide to setup a Shiny Server in AWS along with a method that makes publishing apps easier
RStudio Quickstart Experience RStudio Team using a virtual machine on your desktop. RStudio Team QuickStart VM makes it quick and easy to learn through hands-on experience.
Learning Analytic Administration through a Sandbox It all starts with sandboxes. Development sandboxes are dedicated safe spaces for experimentation and creativity. A sandbox is a place where you can go to test and break things, without the ramifications of breaking the real, important things. If you’re an analytic administrator who doesn’t have access or means to get a sandbox, I recommend that you consider advocating to change that. Here are just some of the arguments for why sandboxes are a powerful tool for the R admin that you may find helpful.
Tidyverse The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
RStudio Cloud Created to make it easy for professionals, hobbyists, trainers, teachers and students to do, share, teach and learn data science using R.
RStudio Cloud Cheatsheets Cheatsheets for working with popular R packages
RStudio Cloud Guide Guide to using RStudio Cloud
R Start Here A Guide to some of the most useful R Packages
Pharmacometrics: Some Shiny applications Example of Live Data Visualization Pharma Tool / App
ICGC Pancreatic Cancer (Ductal Adenocarcinoma) - Genome Viewer Example of Live Data Visualization Pharma Tool / App
Visualisations of proteomics data using R and Bioconductor Example of Live Data Visualization Pharma Tool / App
CanvaseXpress Example of Live Data Visualization Pharma Tool / App
GGPLOT Toolbox Plotting System for R
Radiant Radiant is an open-source platform-independent browser-based interface for business analytics in R. The application is based on the Shiny package and can be run locally or on a server.
bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis bioCancer is a platform-independent interface for dynamic interaction with cancer genomics data. The web is implemented in the R language and based on the Shiny package. It runs on any modern Web browser and requires no programming skills, increasing the accessibility to the huge, complex and heterogeneous cancer genomic data.
GGPLOT GUI This package allows users to visualize their data using an online graphical user interface (GUI) that makes use of R's visualization package ggplot. There are two ways of using this functionality: 1) online, where users can upload their data and visualize it without needing R, by visiting this link: https://site.shinyserver.dck.gmw.rug.nl/ggplotgui/; 2) from within the R-environment (by using the ggplot_shiny() function). In either case, R-code will be provided such that the user can recreate the graphs within the R-environment.
Managing libraries for Rstudio Server R users have access to thousands of community contributed packages. Most users rely on dozens if not hundreds of packages. Organizing these packages can take a fair amount of administrative effort, especially when multiple versions of R exist across multiple servers. This document lays out a simple strategy for managing packages for a team of analysts on a server.
R Installation and Administration This is a guide to installation and administration for R. This manual is for R, version 3.6.2 (2019-12-12)
Strategy Maps Strategies to Reproduce Environments Over Time
Crandash A live visualization of the most popular R packages
Navigating the R Package Universe There are more than 11,000 packages on CRAN, and R users must approach this abundance of packages with effective strategies to find what they need and choose which packages to invest time in learning how to use. Our session centered on this issue, with three themes in our discussion.
How do I select an R Package for my clinical workflow? Paper TT11 PHUSE US Connect 2019, Sean Lopp and Phil Bowsher, RStudio

Python

Page Description
Using Python with RStudio Description of using Python with Rstudio

Git and GitHub

Page Description
Happy Git with R Install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE. Develop a few key workflows that cover your most common tasks. Integrate Git and GitHub into your daily work with R and R Markdown.

Books to Read

Book Author
Mastering Shiny Hadley Wickham
R for Data Science Hadley Wickham
R Graphics Cookbook, 2nd Edition Winston Chang
An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Applied Predictive Modeling Max Kuhn and Kjell Johnson
R Markdown: the Definitive Guide Yihui Xie, J. J. Allaire, and Garrett Grolemund
Text Mining with R A Tidy Approach Julia Silge and David Robinson
Advanced R Hadley Wickham

If you would like to provide information about resources you know about please email the leads. ostcr.leads@phuse.eu