R Programming & RStudio for Epidemiologic Data Analysis

RStudio for public health & medical research — apply R programming, biostatistics, and epidemiologic analysis skills

What you'll learn

✓ Understand the basics of RStudio and set up a research-ready environment.
✓ Import, clean, and prepare public health datasets for analysis.
✓ Students will learn how to import, clean, and organize health research data in RStudio.
✓ They will gain hands-on experience in transforming variables, labeling data, and managing datasets from multiple sources.

Requirements

● No prior experience with R or RStudio is required — beginners are welcome.
● Basic understanding of statistics (mean, correlation, p-value) will be helpful.
● A computer with internet access to install R and RStudio.

Description

Are you ready to manage health research data with confidence using one of the most powerful tools in the world of analytics—RStudio?
This comprehensive course, RStudio for Health Research: Data Management Made Simple, is designed to guide you step by step through the essential skills needed to handle, clean, and organize health research data. Whether you are a student, researcher, or professional, this course will give you the confidence to work with real-world datasets and prepare them for meaningful analysis.
Many learners struggle with messy datasets, missing values, or difficulty in keeping data structured for research projects. This course is built to solve those challenges. Through hands-on coding sessions, practical demonstrations, and real health data examples, you will not only learn how to manage data but also why each step is important in the research workflow.
What You'll Learn
By the end of this course, you will gain practical, job-ready skills in
Data Importing and Management
• Read data from Excel, CSV, Stata, and SPSS into R
• Organize, filter, and clean datasets for efficient analysis
• Keep or drop variables as needed for your project
Data Transformation and Labeling
• Use the mutate() function to create new variables and recode existing ones
• Define variable labels to improve dataset readability
• Assign value labels for categorical variables, making results easier to interpret
Essential R Programming Tools
• Master the pipe operator (%>%) for cleaner and more efficient code
• Rename variables for clarity
• Merge and append datasets from different sources into a single structured file
Basic Statistical Preparation
• Generate descriptive statistics to summarize and check data quality
• Explore distributions and identify errors or inconsistencies before analysis
Research & Publication Skills
• Create high-quality, publication-ready tables for manuscripts
• Present well-organized datasets and summaries suitable for academic or industry reports
This course focuses on data management as the foundation for health research. By mastering these skills, you will be prepared to move on to deeper statistical analysis and modeling with confidence.
Main Topics Covered
RStudio, R programming for health research, Data management in R, Data cleaning in R, Data transformation in R, Health data analysis in R, Public health data preparation, Biostatistics data handling in R, Read Excel in R, Read CSV in R, Import Stata data in R, Import SPSS data in R, Merge datasets in R, Append datasets in R, Mutate function in R, Pipe function in R (%>%), Rename variables in R, Label variables in R, Label categorical variables in R, Data preparation for manuscripts, Publication-ready tables in R, RStudio for evidence-based research, Health data science with R, Data analysis workflow in R, RStudio tutorial for beginners, Step by step R programming, Practical data handling in R.

Who this course is for

■ Public health students and researchers who want to apply regression analysis in their studies.
■ Data science beginners interested in real-world health research applications.
■ Anyone curious about using RStudio and statistics for research projects.

Homepage

https://www.udemy.com/course/rstudio-for-health-research-data-management-made-simple

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