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Working with Packages

Packages extend R's functionality by providing additional functions, data, and tools. Understanding how to work with packages is essential for data analysis.

What are Packages?

Packages are collections of R functions, data, and compiled code organized in a well-defined structure. They can be: - Base packages: Included with R installation - Recommended packages: Installed by default but not loaded - Contributed packages: Available from CRAN, Bioconductor, GitHub, etc.

Installing Packages

From CRAN

# Install a single package
install.packages("ggplot2")

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

# Install with dependencies
install.packages("ggplot2", dependencies = TRUE)

From Bioconductor

# Install BiocManager first
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Install Bioconductor packages
BiocManager::install("Biobase")

From GitHub

# Install devtools first
install.packages("devtools")

# Install from GitHub
devtools::install_github("username/repository")

Loading Packages

# Load a package
library(ggplot2)

# Load with namespace (recommended)
library(dplyr)

# Check if package is loaded
"ggplot2" %in% loadedNamespaces()

Using Package Functions

Direct Access

library(dplyr)
filter(mtcars, mpg > 20)

Namespace Access

# Use without loading
dplyr::filter(mtcars, mpg > 20)

# Useful when multiple packages have same function name
stats::filter()  # Different from dplyr::filter()

Essential Packages for Data Analysis

Data Manipulation

# dplyr - Data manipulation
library(dplyr)
mtcars %>%
  filter(mpg > 20) %>%
  select(mpg, hp, wt) %>%
  arrange(desc(mpg))

# tidyr - Data tidying
library(tidyr)
# pivot_longer(), pivot_wider(), separate(), unite()

# data.table - Fast data manipulation
library(data.table)

Data Import/Export

# readr - Read rectangular data
library(readr)
read_csv("data.csv")
read_tsv("data.tsv")

# readxl - Read Excel files
library(readxl)
read_excel("data.xlsx")

# haven - Read SAS, SPSS, Stata files
library(haven)

Visualization

# ggplot2 - Grammar of graphics
library(ggplot2)
ggplot(mtcars, aes(x = mpg, y = hp)) +
  geom_point()

# plotly - Interactive plots
library(plotly)

# lattice - Trellis graphics
library(lattice)

Statistical Analysis

# broom - Tidy statistical models
library(broom)

# car - Companion to Applied Regression
library(car)

Managing Packages

List Installed Packages

# All installed packages
installed.packages()

# Check if specific package is installed
"ggplot2" %in% rownames(installed.packages())

Update Packages

# Update all packages
update.packages()

# Update specific package
update.packages("ggplot2")

Remove Packages

# Remove a package
remove.packages("package_name")

Package Information

# Package help
help(package = "ggplot2")
packageDescription("ggplot2")

# List functions in package
ls("package:ggplot2")

Package Conflicts

When multiple packages have functions with the same name:

# Check conflicts
conflicts(detail = TRUE)

# Use namespace to specify
dplyr::filter()  # dplyr's filter
stats::filter()  # stats' filter

Creating Your Own Package

While advanced, you can create your own packages:

# Using devtools
library(devtools)
create_package("my_package")

Best Practices

  1. Load packages at the start: Put library() calls at the beginning of your script
  2. Use namespace notation: package::function() when there might be conflicts
  3. Document dependencies: List required packages in your scripts
  4. Keep packages updated: Regularly update packages for bug fixes
  5. Use renv for projects: Manage package versions per project
  6. Check package versions: Some functions change between versions

Example: Complete Workflow

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

# Read data
data <- read_csv("data.csv")

# Process data
processed <- data %>%
  filter(!is.na(value)) %>%
  group_by(category) %>%
  summarize(mean_value = mean(value))

# Visualize
ggplot(processed, aes(x = category, y = mean_value)) +
  geom_bar(stat = "identity") +
  labs(title = "Mean Values by Category")

Package Repositories

  • CRAN: Comprehensive R Archive Network (main repository)
  • Bioconductor: Bioinformatics packages
  • GitHub: Development versions and custom packages
  • R-Forge: Development repository

Next Steps

Now that you understand R fundamentals, proceed to Data Analysis with R to apply these concepts to real data analysis tasks.