Course Outline

Module I: Introduction to R and RStudio

• Why use R for data analysis and visualization
• Installing R and RStudio (or using Posit Cloud)
• Overview of RStudio interface and workflows
• Writing and running R scripts
• Introduction to key packages: tidyverse, readr, dplyr, ggplot2

Module II: Data Import and Wrangling

• Reading CSV, Excel, and other file types
• Data structures in R: vectors, data frames, tibbles
• Cleaning and transforming data using dplyr: filter(), select(), mutate(), arrange(), rename(), etc.
• Handling missing values and outliers
• Merging and joining datasets (left_join(), bind_rows())
• Reshaping data: pivot_longer(), pivot_wider()

Module III: Data Analysis and Exploration

• Summarizing data: summary(), group_by(), summarise()
• Creating custom metrics
• Frequency tables and cross-tabulations
• Exploring trends, patterns, and distributions
• Identifying outliers and anomalies
• Simple inferential stats: mean comparisons, correlations

Module IV: Data Visualization Using ggplot2

• The grammar of graphics and ggplot2 philosophy
• Plot types: Bar, line, scatter, box plots, histograms, density plots
• Customizing plots: colors, themes, labels, scales
• Faceting for subgroup analysis
• Interactive plots with plotly (optional)
• Exporting plots to PNG, PDF, and embedding in reports

Module V: Automating Reports and Final Project

• Creating a clean, reproducible report using R Markdown
• Combining text, code, and visualizations in one document
• Exporting reports to Word, PDF, and HTML
• Final capstone project: Import data, wrangle, analyze, visualize, report, presentation, and peer feedback