Packages

Used to share R code
Composed of code, data, documentation, and tests
Allow for easy, transparent and cross-platform extension of the R base system
Over 12,000 packages available on the Comprehensive R Archive Network (CRAN)
- Full list available packages
For more info on how R packages are developed, please read Creating R Packages: A Tutorial by Friedrich Leisch.

Terminology

Package: an extension of the R base system with code, data and documentation in a standardized format
Base packages: part of the R source tree, maintained by R Core (what you downloaded before coming today)
Repository: a website that hosts a package for installation
Source: the original version of a package with human-readable text and code
Library: a directory containing installed packages (this is on your machine)

There are three main ways to install a package in R:

Installing from CRAN: install a package directly from the repository
- Using R studio: Tools > Install Packages...
- From R console: install.packages("package name")
Installing from source: first download the add-on R package and then type the following in your console:
- install.packages("path_to_file", repos = NULL, type = "source")
Installing a variety of sources using the devtools package (must have it installed first!)
- Github: devtools::install_github("repo name")
- URL: devtools::install_url("url")
- Other methods available, too

There are two ways to access code and data from an installed package:

Loading the package to the current R workspace with the library() function
- library(package) loads all contents of package and makes them accessible until R restarts
- This is the recommended way to use packages
Directly referencing the code/data with the :: operator
- Instead of loading an entire package, we can instead temporarily access its contents
- package::name accesses name from package for a one-time use
- Useful when you only need to use a single object once or twice

Let’s install devtools, here, and car from CRAN using the console:

install.packages("devtools", dependencies = TRUE)
install.packages("here", dependencies = TRUE)
install.packages("car", dependencies = TRUE)

Now, let’s use devtools to install BRRR from Github (more info here).

Method 1:

library(devtools)  # load the devtools package
install_github("brooke-watson/BRRR")  # use the function install_github() to install BRRR

Method 2:

devtools::install_github("brooke-watson/BRRR")  # use the function install_github() from devtools to install BRRR

Play around with BRRR!

To visualize data:
- ggplot2: to create beautiful graphics
- googleVis: to use Google Chart tools
To report results:
- shiny: to create interactive web-based apps
- knitr: to combine R codes and Latex/Markdown codes
- slidify: to build HTML 5 slide shows
To write high-performance R code:
- rcpp: to write R functions that call C++ code
- data.table: to organize datasets for fast operations
- parallel: to use parallel processing in R

Khan Academy: free courses on programming, math, etc.
CRAN: R manuals, FAQs, the R Journal
Rbloggers: blog posts written by other R users
RStudio cheat sheets
Stackoverflow: online community for asking and answering questions on a variety of topics (useful for searching error messages or for help with problem strategies)
R for Data Science: free online book by creators of RStudio

Exploratory Data Analysis: all the necessary tools to investigate your data before performing any formal modeling–from summary statistics to visualization including plotting histograms, boxplots, and scatterplots
Linear Regression: everything you need to know to begin fitting linear models–from simple t-tests to estimation of regression coefficients, variable selection, model diagnostics, and prediction
Logistic Regression: the basics of generalized linear models (GLMs) with an emphasis on binary response data–we extend the theory and modeling strategies of linear regression