Common Methods for Loading Data in R

1. Loading CSV Files (The Everyday Classic)

CSV files are everywhere in public health and biostatistics. R’s base functions and tidyverse tools make them easy to work with.

• read.csv() (base R)
• readr::read_csv() (tidyverse)

Why it matters:

• Faster import for large datasets
• Better handling of variable types
• Cleaner workflows for reproducible research

Graduate students especially benefit from learning tidyverse methods early—it makes collaboration and code sharing much easier.

2. Importing Excel Files (Because Excel Is Not Going Away)

Let’s be honest: Excel is still deeply embedded in health systems.

R allows you to import Excel files directly using packages like:

• readxl

This is crucial when working with:

• Hospital reports
• Program monitoring data
• NGO or ministry spreadsheets

Knowing how to read Excel files directly into R means you can move from messy spreadsheets to clean, analyzable data without manual copying.

3. Reading SAS, SPSS, and Stata Files (A Biostatistics Must-Have)

If you work in biostatistics, epidemiology, or clinical research, you will eventually encounter .sas7bdat, .sav, or .dta files.

Packages like:

• haven

allow you to load these files while preserving:

• Variable labels
• Value labels
• Metadata

This is especially important for:

• Thesis and dissertation work
• Multi-institutional research projects
• Secondary data analysis

At oores Analytics, this is one of the first “aha” moments students have – realizing they don’t need to convert files outside R.

4. Loading Data from URLs and Online Sources

Modern public health research often relies on open data portals, dashboards, and repositories.

R allows you to:

• Load data directly from URLs
• Connect to GitHub-hosted datasets
• Pull real-time data for analysis

This is a game-changer for:

• Surveillance projects
• Rapid assessments
• Teaching reproducible research methods

Learning this skill positions graduate students and professionals as modern, adaptable analysts.

5. Database Connections (For Advanced Users)

As you progress, you may work with:

• SQL databases
• Electronic health records
• Large-scale administrative datasets

R can connect directly to databases, allowing you to:

• Query only the data you need
• Avoid loading massive files into memory
• Build scalable analytics pipelines

This is where data science meets real-world health systems – and why oores Analytics emphasizes practical, career-ready skills.

How This Applies to Graduate Students

For graduate students in public health and biostatistics, mastering data-loading methods means:

• Faster progress on assignments and theses
• Cleaner, more reproducible analyses
• Less stress when working with unfamiliar datasets

Instead of panicking when a professor sends a SAS file or Excel workbook, you’ll know exactly what to do.

How This Applies to Health Professionals

For health professionals – analysts, researchers, clinicians, and program managers – these skills translate to:

• Better data-driven decisions
• Stronger reports and visualizations
• Increased confidence when working across teams

In today’s health landscape, being able to access and prepare data efficiently is just as important as analyzing it.

Final Thoughts from oores Analytics

At oores Analytics, we believe data science should feel approachable, practical, and empowering – not intimidating.

Learning multiple methods for loading data in R is one of the smartest investments you can make in your data journey. It’s the foundation that everything else is built on.

If you can load it, you can analyze it.
And if you can analyze it, you can change outcomes.