R programming

1. Demo Class on R Programming
for Statistical Analysis
Introduction to R, RStudio, and Key
Statistics Concepts (STAT 311)

2. Introduction to R & RStudio
• What is R and RStudio?
• Variables and data types: numeric, character,
logical
• Assigning variables using <- or =
• Basic operations and printing values in R

3. Programming Basics in R
• Defining functions using function()
• Control structures: if, for, while
• Installing packages with install.packages()
• Loading libraries with library()

4. Data Handling & knitr
• Working with directories: getwd(), setwd()
• Reading CSV files: read.csv()
• Creating and compiling R Markdown (.Rmd)
with knitr
• Exporting reports in HTML/PDF format

5. Chapter 1: Variables, Sampling, and
Experiments
• Types of variables: qualitative vs quantitative
• Sampling techniques and biases
• Using sample() function in R

6. Chapter 2: Summarizing Data
• Measures: Mean, Median, Mode, Range, IQR
• Summary statistics with summary()
• Visualizing data with hist(), boxplot()

7. Chapter 3: Probability
• Understanding basic probability
• Simulations using sample()
• Relative frequency with table()

8. Chapter 4: Probability Distributions
• Normal distribution with rnorm()
• Visualizing with hist()
• Binomial distribution with rbinom()

9. Chapter 5: Statistical Inference
• Concept of confidence intervals
• t-tests using t.test() function
• Interpreting p-values and test results

10. Chapter 6: Inference for
Categorical Data
• Contingency tables with table()
• Chi-square test using chisq.test()

11. Chapter 7: Inference for Numeric
Data
• T-tests: One-sample and two-sample
• ANOVA basics using aov()

12. Chapter 8 & 9: Regression
• Simple Linear Regression with lm()
• Plotting and interpreting results
• Logistic Regression using glm() with binomial
family

13. Wrap-Up & Practice Task
• Practice: Load dataset, summarize, visualize
• Homework: Try writing a function and a loop
• Generate a report using R Markdown