Course Overview:
This introductory course is designed to help learners develop a foundational understanding of data analysis using the R programming language. Participants will learn how to handle, analyze, and visualize data efficiently in R. The training will guide you through the essential tools and functions for performing statistical analysis and creating reproducible research outputs — all through practical, hands-on exercises.
Course Objectives:
- - To introduce the R environment and its core functionalities for data analysis.
- - To teach participants how to import, manage, and summarize data.
- - To develop practical skills in descriptive statistics, hypothesis testing, and regression analysis.
- - To enhance understanding of data visualization and presentation using R.
- - To build confidence in applying R for academic and professional data projects.
Why is this Course Essential?
In today’s data-driven world, R has become one of the most potent and in-demand tools for statistical computing and data visualization. Whether you are a student, researcher, or professional, the ability to analyze data using R is a vital skill that enhances analytical thinking and career competitiveness. This course provides a practical starting point for anyone aiming to explore data science, research, or evidence-based policy analysis.
Benefits of this Course:
- - Learn data analysis skills directly applicable to real-world research and business problems.
- - Gain confidence in working with R for statistical analysis and visualization.
- - Receive guided instruction from experienced trainers at CPER.
- - Obtain a Certificate of Completion from the Center for Policy and Economic Research (CPER).
- - Network with peers interested in data analytics and research.
Course Includes:
- - 1.5 hours of instructor-led online training
- - Live interactive session via online platform
- - Practical demonstrations and exercises using sample datasets
- - Learning materials and R code scripts
Course Outline:
- 1, Introduction to R
- 2. Setting the working directory
- 3. Reading data (CSV and other formats)
- 4. Listing and exploring variables
- 5. Viewing initial rows of data
- 6. Descriptive statistics
- 7. Sorting data
- 8. Creating frequency tables
- 9. Calculating correlations among variables
- 10. Conducting t-tests for one group mean
- 11. Performing ANOVA for two-group mean comparison
- 12. Running OLS regression and interpreting output
- 13. Generating and customizing plots
- 14. Redefining and transforming variables
- 15. Installing and using packages
- 16. Loading libraries and managing the R environment