In jimbrig2011/knowledge-repo: What the Package Does (One Line, Title Case)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(fontawesome)
library(htmltools)
library(rmarkdown)
library(knitr)
htmltools::tagList(rmarkdown::html_dependency_font_awesome())
R Resources Knowledge Repository
The goal of knowledge-repo is to provide R users and developers a central hub
for storing useful resources related to R.
NOTE: Favorite resources are identified with a r fa("star", fill = "steelblue") icon.
Table of Contents
- Learning R
- Getting Started - Basic Resources
- Progression - Intermediate and Advanced Resources
- General R
- Books
- Repositories
- Addins
- Style Guides
- Blogs
- Articles
- Awesome Lists
Learning R - A Curated Path {#learn-r}
The R language has been said to have a steep learning curve and although there
are many introductory courses, books, and online resources, it’s hard to decide
where to start.
Fortunitely, I have compiled some of the more helpful resources to help guide
newcomers to more efficiently learn R and advance their skills.
If you already feel comfortable with the basics of R - you can start with the
Progression - Intermediate and Advanced R section.
Getting Started - Introductory Resources {#getting-started}
To get started with learning how to program in R, I recommend starting with
some beginner level books and manuals which cover the fundamentals of R:
- A (Very) Short Introduction to R (Torfs & Bauer, 2014)
r fa("star", fill = "steelblue")
- Intro to R Manual (R Core Team, 1999)
- R Language Definition (R Core Team, 2000)
- Data Import and Export (R Core Team, 2000)
- SimpleR (Verzani, 2002)
- Introduction to R (Spector, 2004)
- R In a Nutshell (Adler, 2010)
- ComputerWorld Beginner Guide
- R In Action Book
When first starting R, some of the fundamental basics to focus on include, but are
not limited to:
- Installing necessary software and setting up your machine
- How to install and library R Packages
- Getting and setting your working directory
- Setting up an R project
- Loading, Saving, and Managing Data
- Basic data manipulation with base R
- Reading and Writing Code
- Objects and functions
- Scalars and vectors
- Debugging
Progression to Intermediate and Advanced R Resources {#progression}
After honing in the beginner level skills, further advance your abilities
with these intermediate and advanced R resources:
Further Your Understanding of R
- Pirate's Guide to R
- R For Data Science
r fa("star", fill = "steelblue")
- Hands On Programming with R
- Efficient R Programming
r fa("star", fill = "steelblue")
- Advanced R
r fa("star", fill = "steelblue")
- R Tips
- Mastering Software Development in R
r fa("star", fill = "steelblue")
Once you feel comfortable with the fundamentals of R coding, expand your horizons
by learning other various areas using the resources below:
Functional Programming, Tidy Evaluation, and Creating R Packages
- Functional Programming and Unit Testing for Data Munging with R
- Tidy Evaluation
- R Packages
r fa("star", fill = "steelblue")
- rOpenSci Package Development Guide
- Writing R Extensions
r fa("star", fill = "steelblue")
- R Notes for Professionals
r fa("star", fill = "steelblue")
Dealing with Strings and Text Mining
Version Control with Git
- Happy Git and GitHub for the useR
r fa("star", fill = "steelblue")
Reproducible, Literate Programming with R Markdown
- R Markdown - Definitive Guide
r fa("star", fill = "steelblue")
Developing Website Applications with Shiny
- Shiny Home Page
- Shiny Gallery
- Getting Started with ShinyDashboard
- R Shiny Function Reference
- How to Build a Shiny Application from Scratch
- Building Shiny Apps - A Workflow (WIP)
r fa("star", fill = "steelblue")
- Mastering Shiny
r fa("star", fill = "steelblue")
Continued Learning and Staying Up-To-Date
The R programming language is always contrinuously evolving and progressing making
it important to be able to quickly adapt and stay up to date with your abilites.
Blogs {#blogs}
- RStudio Community
- CRAN Task Views
- R-Bloggers
- RStudio Blog
- RTask
- TensorFlow
- Tidyverse
- Business Science
- Paul Vanderlaken
- URSA Labs
- Colin Fay
- WinVector
- Google R Group
- Applison
- Mango Solutions
- R Weekly
Asking Questions
- Stackoverflow
- Reprex
Other Helpful Resources
Style Guides {#styleguides}
- Google's R Style Guide
- Tidyverse Style Guide
- Advanced R Style Guide
- R Style Guide
- R Code - Best Practices
Miscellaneous
Books {#books}
- R For Data Science
- Advanced R
- Efficient R Programming
- R Markdown - Definitive Guide
- Pirate's Guide to R
- R Packages
- R Tips
- Happy Git and GitHub for the useR
- Mastering Software Development in R
- Functional Programming and Unit Testing for Data Munging with R
- Mastering Shiny
- How to Build a Shiny Application from Scratch
- rOpenSci Package Development Guide
- Introduction to Open Data Science
- Data Science with R: A Resource Compendium
- Tidy Evaluation
- Building Shiny Apps - A Workflow (WIP)
- Writing R Extensions
- Hands On Programming with R
- R Graphics Cookbook
- R Notes for Professionals
- Tidy Text Mining
- Handling Strings in R
Repositories {#repos}
R-studio Addins {#addins}
- Dean Attali's List
- Copydat
- Rstudio-Snippets
- view Enhance
- Extra Inserts
- Get Function Args
- Git Gadget
- GREA
- Image Clipper
- Inert Image
- Keyboard Shortcuts
- Sinew
- Common Utils
- Object Remover
- Path to Clip
Articles {#articles}
- Pimp My RMD
- How to Build a Shiny Truck
- A Journey From Basic Prototype To Production-Ready Shiny Dashboard
- Super Solutions for Shiny Architecture 1 of 5: Using Session Data
- Why You Should Use R Shiny for Enterprise Application Development
- Best Practice: Development of Robust Shiny Dashboards as R Packages
- FDA’s Approach to R Shiny Standardized, Interactive Tools
- Business Science Shiny App Tutorial
- Gnatt Charts with R
- Packaging Shiny Applications: A Deep Dive
- Delivering Data Science for the Enterprise with Shiny (R) in Kubernetes
- Speed Up Shiny Coding with Data
- SQL Server Schemas & R Tip
- Shiny GEM
Awesome Lists {#awesome}
R Specific
General Programming
- Awesome Git
- Awesome CSS
- Awesome DataViz
- Awesome GitHub
- Awesome HTML5
- Awesome Javascript
- Awesome JSON
- Awesome Markdown
- Awesome Product Design
- Awesome PowerShell
- Awesome PostGres
- Awesome Readme
- Awesome Regex
- Awesome Analytics
- Awesome Software Architecture
- Awesome Web Design
- Awesome Design Principles
- Awesome Design Systems
jimbrig2011/knowledge-repo documentation built on Feb. 11, 2020, 2:37 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(fontawesome) library(htmltools) library(rmarkdown) library(knitr) htmltools::tagList(rmarkdown::html_dependency_font_awesome())
R Resources Knowledge Repository
The goal of knowledge-repo is to provide R users and developers a central hub for storing useful resources related to R.
NOTE: Favorite resources are identified with a r fa("star", fill = "steelblue") icon.
Table of Contents
- Learning R
- Getting Started - Basic Resources
- Progression - Intermediate and Advanced Resources
- General R
- Books
- Repositories
- Addins
- Style Guides
- Blogs
- Articles
- Awesome Lists
Learning R - A Curated Path {#learn-r}
The R language has been said to have a steep learning curve and although there are many introductory courses, books, and online resources, it’s hard to decide where to start.
Fortunitely, I have compiled some of the more helpful resources to help guide newcomers to more efficiently learn R and advance their skills.
If you already feel comfortable with the basics of R - you can start with the Progression - Intermediate and Advanced R section.
Getting Started - Introductory Resources {#getting-started}
To get started with learning how to program in R, I recommend starting with some beginner level books and manuals which cover the fundamentals of R:
- A (Very) Short Introduction to R (Torfs & Bauer, 2014)
r fa("star", fill = "steelblue") - Intro to R Manual (R Core Team, 1999)
- R Language Definition (R Core Team, 2000)
- Data Import and Export (R Core Team, 2000)
- SimpleR (Verzani, 2002)
- Introduction to R (Spector, 2004)
- R In a Nutshell (Adler, 2010)
- ComputerWorld Beginner Guide
- R In Action Book
When first starting R, some of the fundamental basics to focus on include, but are not limited to:
- Installing necessary software and setting up your machine
- How to install and library R Packages
- Getting and setting your working directory
- Setting up an R project
- Loading, Saving, and Managing Data
- Basic data manipulation with base R
- Reading and Writing Code
- Objects and functions
- Scalars and vectors
- Debugging
Progression to Intermediate and Advanced R Resources {#progression}
After honing in the beginner level skills, further advance your abilities with these intermediate and advanced R resources:
Further Your Understanding of R
- Pirate's Guide to R
- R For Data Science
r fa("star", fill = "steelblue") - Hands On Programming with R
- Efficient R Programming
r fa("star", fill = "steelblue") - Advanced R
r fa("star", fill = "steelblue") - R Tips
- Mastering Software Development in R
r fa("star", fill = "steelblue")
Once you feel comfortable with the fundamentals of R coding, expand your horizons by learning other various areas using the resources below:
Functional Programming, Tidy Evaluation, and Creating R Packages
- Functional Programming and Unit Testing for Data Munging with R
- Tidy Evaluation
- R Packages
r fa("star", fill = "steelblue") - rOpenSci Package Development Guide
- Writing R Extensions
r fa("star", fill = "steelblue") - R Notes for Professionals
r fa("star", fill = "steelblue")
Dealing with Strings and Text Mining
Version Control with Git
- Happy Git and GitHub for the useR
r fa("star", fill = "steelblue")
Reproducible, Literate Programming with R Markdown
- R Markdown - Definitive Guide
r fa("star", fill = "steelblue")
Developing Website Applications with Shiny
- Shiny Home Page
- Shiny Gallery
- Getting Started with ShinyDashboard
- R Shiny Function Reference
- How to Build a Shiny Application from Scratch
- Building Shiny Apps - A Workflow (WIP)
r fa("star", fill = "steelblue") - Mastering Shiny
r fa("star", fill = "steelblue")
Continued Learning and Staying Up-To-Date
The R programming language is always contrinuously evolving and progressing making it important to be able to quickly adapt and stay up to date with your abilites.
Blogs {#blogs}
- RStudio Community
- CRAN Task Views
- R-Bloggers
- RStudio Blog
- RTask
- TensorFlow
- Tidyverse
- Business Science
- Paul Vanderlaken
- URSA Labs
- Colin Fay
- WinVector
- Google R Group
- Applison
- Mango Solutions
- R Weekly
Asking Questions
- Stackoverflow
- Reprex
Other Helpful Resources
Style Guides {#styleguides}
- Google's R Style Guide
- Tidyverse Style Guide
- Advanced R Style Guide
- R Style Guide
- R Code - Best Practices
Miscellaneous
Books {#books}
- R For Data Science
- Advanced R
- Efficient R Programming
- R Markdown - Definitive Guide
- Pirate's Guide to R
- R Packages
- R Tips
- Happy Git and GitHub for the useR
- Mastering Software Development in R
- Functional Programming and Unit Testing for Data Munging with R
- Mastering Shiny
- How to Build a Shiny Application from Scratch
- rOpenSci Package Development Guide
- Introduction to Open Data Science
- Data Science with R: A Resource Compendium
- Tidy Evaluation
- Building Shiny Apps - A Workflow (WIP)
- Writing R Extensions
- Hands On Programming with R
- R Graphics Cookbook
- R Notes for Professionals
- Tidy Text Mining
- Handling Strings in R
Repositories {#repos}
R-studio Addins {#addins}
- Dean Attali's List
- Copydat
- Rstudio-Snippets
- view Enhance
- Extra Inserts
- Get Function Args
- Git Gadget
- GREA
- Image Clipper
- Inert Image
- Keyboard Shortcuts
- Sinew
- Common Utils
- Object Remover
- Path to Clip
Articles {#articles}
- Pimp My RMD
- How to Build a Shiny Truck
- A Journey From Basic Prototype To Production-Ready Shiny Dashboard
- Super Solutions for Shiny Architecture 1 of 5: Using Session Data
- Why You Should Use R Shiny for Enterprise Application Development
- Best Practice: Development of Robust Shiny Dashboards as R Packages
- FDA’s Approach to R Shiny Standardized, Interactive Tools
- Business Science Shiny App Tutorial
- Gnatt Charts with R
- Packaging Shiny Applications: A Deep Dive
- Delivering Data Science for the Enterprise with Shiny (R) in Kubernetes
- Speed Up Shiny Coding with Data
- SQL Server Schemas & R Tip
- Shiny GEM
Awesome Lists {#awesome}
R Specific
General Programming
- Awesome Git
- Awesome CSS
- Awesome DataViz
- Awesome GitHub
- Awesome HTML5
- Awesome Javascript
- Awesome JSON
- Awesome Markdown
- Awesome Product Design
- Awesome PowerShell
- Awesome PostGres
- Awesome Readme
- Awesome Regex
- Awesome Analytics
- Awesome Software Architecture
- Awesome Web Design
- Awesome Design Principles
- Awesome Design Systems
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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