Installing and Using R Packages

Understanding R Packages

R packages are collections of R functions, data, and compiled code that extend the capabilities of base R. They are fundamental to the R ecosystem for several reasons:

  • They allow you to access specialized functions and tools without writing them from scratch
  • They provide access to cutting-edge statistical methods and visualizations
  • They make it easier to perform complex analyses with well-tested, maintained code
  • They help maintain reproducibility in your analysis

The R Package Ecosystem

R has two main types of packages:

  1. Base Packages: Come pre-installed with R (like stats, graphics, utils)
  2. Contributed Packages: Created by the R community and available on CRAN (Comprehensive R Archive Network)

Installing Packages

There are several ways to install R packages:

Using install.packages()

The most common way to install packages is using the install.packages() function:

# Install a single package
install.packages("tidyverse")

# Install multiple packages
install.packages(c("ggplot2", "dplyr", "readr"))

Essential Packages for this Workshop

Here are the key packages we’ll be using throughout this workshop:

# Install all essential packages
install.packages(c(
  "tidyverse",  # Collection of data science packages
  "ggplot2",    # For data visualization
  "dplyr",      # For data manipulation
  "readr",      # For reading data
  "tidyr",      # For tidying data
  "rmarkdown",  # For creating documents
  "knitr"       # For knitting Rmd files
))

Loading Packages

Once installed, packages need to be loaded into your R session before you can use them:

# Load individual packages
library(tidyverse)

# You can also load specific packages from tidyverse
library(ggplot2)
library(dplyr)

Checking Package Version

You can check which version of a package you have installed:

# Check package version
packageVersion("tidyverse")

Managing Packages

Updating Packages

It’s good practice to keep your packages up to date:

# Update all packages
update.packages()

# Update specific packages
install.packages("ggplot2")  # reinstalling will update to the latest version

Viewing Installed Packages

You can see what packages are installed on your system:

# List installed packages
installed.packages()[, c("Package", "Version")]

Best Practices

  1. Document Dependencies: Always list the packages your analysis requires at the beginning of your script
  2. Version Control: Note the versions of key packages used in your analysis
  3. Load Packages Early: Load all required packages at the start of your script
  4. Use Specific Functions: When using functions from specific packages, you can use the package::function() notation

Example Usage

Here’s a simple example using some common packages:

# Load required packages
library(ggplot2)
library(dplyr)

# Create some sample data
data <- data.frame(
  x = 1:10,
  y = rnorm(10)
)

# Use dplyr to manipulate data
data <- data %>%
  mutate(z = y * 2)

# Create a plot with ggplot2
ggplot(data, aes(x = x, y = y)) +
  geom_point() +
  geom_line() +
  theme_minimal() +
  labs(title = "Sample Plot",
       x = "X axis",
       y = "Y axis")

Troubleshooting

Common issues when working with packages:

  1. Package not found: Make sure the package is installed
  2. Loading conflicts: Some functions might have the same name in different packages
  3. Dependencies: Some packages require other packages to be installed first
# Check if a package is installed
if (!require("ggplot2")) {
  install.packages("ggplot2")
}

# See which packages are currently loaded
search()

# Get help with a package
help(package = "ggplot2")

Finding Help and Documentation

R packages come with extensive documentation that can help you understand how to use them effectively:

Package Documentation 

# Get overview of a package
help(package = "dplyr")

# Get help for a specific function
?dplyr::filter
help("filter", package = "dplyr")

# See examples for a function
example("filter", package = "dplyr")

Package Vignettes

Vignettes are detailed guides that show you how to use the package:

# List all vignettes for installed packages
vignette()

# List vignettes for a specific package
vignette(package = "dplyr")

# Open a specific vignette
vignette("dplyr")

# If you know the specific vignette name
vignette("programming", package = "dplyr")

Online Resources

Many packages also have additional resources:

  1. Package websites: Most tidyverse packages have dedicated websites (e.g., dplyr.tidyverse.org)
  2. GitHub repositories: Source code and issue tracking
  3. Cheatsheets: Quick reference guides (Help -> Cheatsheets in RStudio)
# Open package documentation in browser (if available)
browseVignettes("dplyr")

# Get package news/changelog
news(package = "dplyr")

Next Steps

After understanding how to install and manage packages, you can:

  • Explore package documentation using ? or help()
  • Look for packages that suit your needs on CRAN
  • Learn about package development if you want to create your own packages