Getting Started with R for Epidemiology

beginners

tutorial

setup

A beginner’s guide to setting up R and RStudio for epidemiological analysis

Author

Unijos R Users Team

Published

January 29, 2025

Introduction

Welcome to R programming! This post will guide you through setting up your R environment and running your first analysis.

Why R for Epidemiology?

R has become the standard tool for modern epidemiological analysis because it offers:

Reproducibility: Your entire analysis can be documented and shared
Powerful packages: Specialized tools for outbreak analytics, survival analysis, and more
Free and open-source: No licensing costs
Active community: Extensive support and resources
Modern tools: Packages like EpiNow2 and epiparameter for cutting-edge analysis

Setting Up Your Environment

Step 1: Install R

Download R from CRAN:

For Windows: Choose the .exe installer
For Mac: Choose the .pkg installer
For Linux: Use your package manager

Step 2: Install RStudio

Download RStudio Desktop (free) from Posit.

Step 3: Install Essential Packages

Open RStudio and run:

# Data manipulation and visualization
install.packages("tidyverse")

# Publication-ready tables
install.packages("gtsummary")

# Epidemiological analysis
install.packages("survival")
install.packages("epiR")

# Outbreak analytics
install.packages("incidence")
install.packages("epicurve")

Your First Analysis

Let’s analyze a simple outbreak dataset:

# Load packages
library(tidyverse)

# Create sample outbreak data
outbreak_data <- data.frame(
  age_group = c("0-5", "6-10", "11-15", "16-20", "21+"),
  cases = c(12, 18, 25, 15, 8)
)

# Create a simple epi curve
ggplot(outbreak_data, aes(x = age_group, y = cases)) +
  geom_col(fill = "steelblue") +
  theme_minimal() +
  labs(
    title = "Cases by Age Group",
    x = "Age Group (years)", 
    y = "Number of Cases"
  )

Next Steps

Join our bi-weekly training sessions via Meetup
Explore the R for Data Science book (free online)
Practice with real datasets from your field

Resources

Questions? Email us at info@unijosrusers.org or join our next training session!

--- title: "Getting Started with R for Epidemiology" author: "Unijos R Users Team" date: "2025-01-29" categories: [beginners, tutorial, setup] description: "A beginner's guide to setting up R and RStudio for epidemiological analysis" --- ## Introduction Welcome to R programming! This post will guide you through setting up your R environment and running your first analysis. ## Why R for Epidemiology? R has become the standard tool for modern epidemiological analysis because it offers: - **Reproducibility**: Your entire analysis can be documented and shared - **Powerful packages**: Specialized tools for outbreak analytics, survival analysis, and more - **Free and open-source**: No licensing costs - **Active community**: Extensive support and resources - **Modern tools**: Packages like EpiNow2 and epiparameter for cutting-edge analysis ## Setting Up Your Environment ### Step 1: Install R Download R from [CRAN](https://cran.r-project.org/): - For Windows: Choose the `.exe` installer - For Mac: Choose the `.pkg` installer - For Linux: Use your package manager ### Step 2: Install RStudio Download RStudio Desktop (free) from [Posit](https://posit.co/download/rstudio-desktop/). ### Step 3: Install Essential Packages Open RStudio and run: ```{r} #| eval: false # Data manipulation and visualization install.packages("tidyverse") # Publication-ready tables install.packages("gtsummary") # Epidemiological analysis install.packages("survival") install.packages("epiR") # Outbreak analytics install.packages("incidence") install.packages("epicurve") ``` ## Your First Analysis Let's analyze a simple outbreak dataset: ```{r} #| eval: false # Load packages library(tidyverse) # Create sample outbreak data outbreak_data <- data.frame( age_group = c("0-5", "6-10", "11-15", "16-20", "21+"), cases = c(12, 18, 25, 15, 8) ) # Create a simple epi curve ggplot(outbreak_data, aes(x = age_group, y = cases)) + geom_col(fill = "steelblue") + theme_minimal() + labs( title = "Cases by Age Group", x = "Age Group (years)", y = "Number of Cases" ) ``` ## Next Steps - Join our bi-weekly training sessions via [Meetup](https://www.meetup.com/unijos-r-users/) - Explore the [R for Data Science](https://r4ds.hadley.nz/) book (free online) - Practice with real datasets from your field ## Resources - [CRAN Task View: Epidemiology](https://cran.r-project.org/web/views/Epidemiology.html) - [RStudio Cheatsheets](https://posit.co/resources/cheatsheets/) - [Epiverse Training Materials](https://epiverse-trace.github.io/) --- **Questions?** Email us at [info@unijosrusers.org](mailto:info@unijosrusers.org) or join our next training session!