Data Analysis Using R

Course Description
This course provides a comprehensive introduction to R programming for data analysis and
statistical computing. Learners will gain hands-on experience using R and RStudio to import,
manipulate, analyze, and visualize data. The course emphasizes practical data analysis
workflows, exploratory data analysis, basic statistical methods, and reproducible reporting using
R Markdown. By the end of the course, learners will be able to independently conduct data
analysis projects using R and effectively communicate their results.

Course Objectives

By the end of this course, learners will be able to:
● Understand and apply core R programming concepts
● Import, clean, manipulate, and manage datasets in R
● Perform exploratory data analysis and basic statistical tests
● Create effective data visualizations using ggplot2
● Conduct regression analysis and interpret results
● Produce reproducible and professional reports using R Markdown

Course Outline:

Module 1: Introduction to R and R Studio 

• Explain the purpose and applications of R
•  Install and configure R and RStudio
• Navigate the RStudio interface
• Execute basic R commands and scripts
• Install and load R packages
• Use help files and documentation effectively

Module 2:  Data Structures in R

● Identify basic R data types
● Create and manipulate vectors, matrices, lists, and data frames
● Subset and index data structures
● Convert between data types
● Inspect and understand data structures

Module 3: Importing and Exporting Data

● Import data from common file formats (CSV, Excel, text)
● Use base R and tidyverse functions for data import
● Export data and results to external files
● Save and load R objects

● Apply best practices for data management

Module 4 : Data Manipulation in R

● Apply dplyr functions to manipulate datasets
● Filter, sort, and summarize data
● Create new variables
● Handle missing values
● Merge and join multiple datasets
● Work with dates and text data

Module 5: Exploratory Data Analysis (EDA) in R

● Summarize data using descriptive statistics
● Explore data distributions and relationships
● Identify outliers and missing data patterns
● Conduct correlation analysis
● Use EDA to inform further analysis

Module 6: Data Visualization with ggplot2

● Understand the grammar of graphics
● Create common plot types using ggplot2
● Customize plot aesthetics and themes
● Visualize grouped and faceted data
● Apply best practices in data visualization
● Export visual outputs for reporting

Module 7: Basic Statistical Analysis in R

● Compute descriptive and inferential statistics
● Perform common hypothesis tests (t-test, chi-square, ANOVA)
● Interpret p-values and confidence intervals
● Understand assumptions underlying statistical tests
● Communicate statistical findings clearly

Module 8: Regression Analysis

● Explain regression concepts and assumptions
● Fit simple and multiple linear regression models
● Interpret regression coefficients and outputs
● Evaluate model performance and diagnostics
● Make predictions using regression models

Module 9: Introduction to R Markdown

● Understand the principles of reproducible analysis
● Create R Markdown documents
● Embed R code, outputs, and visualizations
● Generate reports in multiple formats
● Apply best practices for documentation and reporting