Introduction to Packages

Introduction

In R, packages are collections of functions, datasets, documentation, and compiled code that extend the base functionality of R. Packages allow users to perform complex tasks easily without writing everything from scratch.

R’s power comes largely from its rich package ecosystem, which supports data analysis, statistics, machine learning, visualization, web applications, and more.

What is an R Package?

An R package is a bundled unit of reusable code and resources that can be installed and loaded into an R session.

A package typically contains:

R functions
Preloaded datasets
Help documentation
Compiled C/C++/Fortran code (optional)
Tests and examples

Why Packages are Important in R

Packages allow:

Code reuse
Faster development
Access to advanced algorithms
Standardized and tested solutions
Community-driven improvements

Examples of tasks done using packages:

Data manipulation → dplyr
Data visualization → ggplot2
Machine learning → caret
Web apps → shiny
Statistical modeling → lme4

Base R Packages

Base R comes with several default packages that are automatically available.

Examples:

base
stats
graphics
utils
methods

Check loaded base packages:

search()

CRAN (Comprehensive R Archive Network)

CRAN is the official repository for R packages.

Features:

Thousands of packages
Peer-reviewed submissions
Version control
Platform support (Windows, macOS, Linux)

Website: https://cran.r-project.org

Installing Packages in R

Installing from CRAN

Use install.packages().

install.packages("ggplot2")

This installs the package on your system.

Installing Multiple Packages

install.packages(c("dplyr", "tidyr", "readr"))

Choosing a CRAN Mirror

chooseCRANmirror()

Loading Packages

Using `library()`

Loads the package into the current session.

library(ggplot2)

Using `require()`

Returns TRUE/FALSE if package is loaded.

require(dplyr)

Difference Between `install.packages()` and `library()`

Function	Purpose
`install.packages()`	Downloads and installs package
`library()`	Loads installed package into session

You install once, but load every session.

Checking Installed Packages

List All Installed Packages

installed.packages()

Check if a Package is Installed

"ggplot2" %in% rownames(installed.packages())

Using Package Functions Without Loading

You can access functions using ::.

ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg))

Useful when:

Avoiding name conflicts
Using a single function only

Viewing Package Documentation

Help for a Package

help(package = "dplyr")

Help for a Function

?filter

Browse Package Vignettes

browseVignettes("dplyr")

Updating Packages

Keep packages up to date.

update.packages()

Update specific package:

install.packages("ggplot2")

Removing Packages

Uninstall packages you no longer need.

remove.packages("ggplot2")

Popular R Packages and Their Uses

`dplyr`

Data manipulation:

filter()
select()
mutate()
summarise()

`ggplot2`

Data visualization using grammar of graphics.

ggplot(mtcars, aes(wt, mpg)) + geom_point()

`tidyr`

Data reshaping:

pivot_longer()
pivot_wider()

`shiny`

Build interactive web applications.

`readr`

Fast data import/export.

The Tidyverse

The tidyverse is a collection of related packages designed for data science.

Includes:

ggplot2
dplyr
tidyr
readr
purrr
stringr

Install tidyverse:

install.packages("tidyverse")

Load tidyverse:

library(tidyverse)

Package Conflicts

Sometimes two packages have functions with the same name.

Example:

filter() in stats
filter() in dplyr

Solution:

dplyr::filter()

Creating Your Own Package (Introduction)

Advanced users can create their own packages to:

Share reusable code
Distribute tools
Organize large projects

Tools:

devtools
roxygen2
usethis

Practical Example

install.packages("dplyr")
library(dplyr)

data <- data.frame(
  name = c("Alice", "Bob"),
  score = c(85, 90)
)

filter(data, score > 85)

Common Mistakes with Packages

Forgetting to load package after installing
Name conflicts between packages
Installing packages repeatedly
Using outdated package versions

Summary

Packages are the backbone of R’s ecosystem. They allow users to extend R’s capabilities, reuse high-quality code, and perform complex tasks easily. Understanding how to install, load, manage, and use packages is essential for effective R programming and data science work.