R for Data Analysis

Course Overview

R is the language of choice for statisticians, data scientists, epidemiologists, and researchers worldwide. Free, open-source, and extraordinarily powerful, R's rich ecosystem of packages — particularly the tidyverse — makes it an indispensable tool for modern data analysis.

This 5-day course takes you from R basics through to advanced data manipulation, statistical modeling, and stunning publication-quality visualization. You'll work hands-on with real datasets, building a portfolio of R scripts.

You'll learn to write clean, readable, reproducible code using the tidyverse philosophy, produce dynamic reports with R Markdown, and communicate your findings through professional ggplot2 visualizations.

What You Will Learn

• Install R and RStudio; understand the R environment and project structure
• Master R's fundamental data types: vectors, data frames, lists, and factors
• Import data from CSV, Excel, SPSS, and Stata using readr and haven packages
• Wrangle data efficiently with dplyr: filter, select, mutate, group_by, summarise
• Reshape data between wide and long formats using tidyr
• Handle missing data, outliers, and data cleaning challenges
• Create stunning, customizable visualizations with ggplot2
• Perform statistical tests: t-tests, ANOVA, chi-square, correlation
• Build linear and logistic regression models and interpret outputs
• Produce reproducible reports and documents with R Markdown
• Use functions and loops to automate repetitive analysis tasks

Who Should Enroll

• Data analysts and researchers looking to transition to open-source tools
• Epidemiologists and public health professionals analyzing health datasets
• Academics and graduate students conducting quantitative research
• M&E professionals who want to produce sophisticated analytical outputs
• Professionals currently using Excel who want to upgrade to programming-based analysis
• Anyone interested in data science looking for a strong analytical foundation

Requirements

• •No prior programming or R experience required — we start from zero
• •A basic understanding of statistics (mean, standard deviation, p-values) is helpful
• •Laptop with internet access (R and RStudio are free to download)
• •Willingness to think logically and problem-solve