Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
7 Hour(s) 0 Minute(s)
Language
English
Taught by
Md Ahshanul Haque
Rating
Course Overview
RStudio for public health & medical research — apply R programming, biostatistics, and epidemiologic analysis skills
Are you ready to analyze health research data with confidence using R and RStudio?
This comprehensive course is designed to take you from data management to advanced epidemiologic analysis, using real-world health datasets. Whether you are a student, researcher, or professional in public health, epidemiology, or biostatistics, this course will give you the practical skills needed to handle, analyze, and interpret data for research and publication.
You will begin by learning how to import data from Excel, CSV, Stata, and SPSS, and organize it for analysis. The course then guides you through essential data management tasks, including cleaning messy datasets, handling missing values, transforming variables, labeling data, and preparing analysis-ready datasets.
Once the foundation is built, you will move into data visualization using ggplot2, creating clear and publication-quality charts such as histograms, boxplots, and bar graphs. You will also learn how to export figures and tables for reports and manuscripts.
A major focus of the course is statistical analysis in R, including descriptive and inferential analysis, p-values, and summary tables using the gtsummary package. You will then advance to key epidemiologic methods, including:
Linear and multiple regression
Logistic regression and interpretation of odds ratios
Cohort study analysis (risk ratio, Poisson regression, IRR)
Matched case–control analysis (conditional logistic regression)
Survival analysis (Kaplan–Meier curves and Cox regression)
Throughout the course, you will create publication-ready tables and outputs, making your results suitable for theses, dissertations, and journal articles.
This is a hands-on, step-by-step course with real data examples, designed for beginners and intermediate learners. By the end, you will be able to conduct complete epidemiologic data analysis workflows in R, from raw data to final results.
If you want to build strong, practical skills in R programming for health research, this course will give you the confidence to move forward.
Course Content
- 14 section(s)
- 85 lecture(s)
- Section 1 Introduction to RStudio
- Section 2 Organizing Data — Keep, Drop, and Manage Variables in RStudio
- Section 3 Value and Variable Labels in RStudio | Data management
- Section 4 Variable Transformation in RStudio | Data Management part -3
- Section 5 Data Visualization in RStudio | Create Insightful Charts and Graphs | ggplot
- Section 6 Bar Diagrams for Categorical Variables in RStudio
- Section 7 Bar Diagram for Binary Variables in RStudio
- Section 8 Descriptive and Inferential Analysis in RStudio | Summarize and Interpret Data
- Section 9 Linear Regression Analysis in RStudio using gtsummary | R and R Programming
- Section 10 Logistic Regression in RStudio | Epidemiological Analysis | gtsummary
- Section 11 Cohort Study Data Analysis in R | Binary Outcomes | Risk Ratio Estimation
- Section 12 Cohort Study Analysis for Count Outcomes using Poisson Regression in R | IRR
- Section 13 Matched Case–Control Study Data Analysis in RStudio | Conditional Logistic reg
- Section 14 Survival Analysis for Assessing Treatment Effects in Hospital Patients using R
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.
Skills covered in this course
Reviews
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MMd. Zafar Ilias Bhuiyan
I appreciate and please try to create manuscript writing content.
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AAshraful Alam Siddique
Amazing video and helpful for our job/research profession.
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SSwapan Cruze
Thank you for a nice contant Is it possible to include DHS data analysis
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AAnicet Tchibozo
Combining data management and stats analysis with specific focus on some epidemiology aspects using R programming and RStudio was a great design. I Apprecuated it. As a contribution, I would suggest a final exam to strengthen the learning path.
