Course Outline

Module 1: Introduction to R and RStudio

• Intro to R and RStudio
• Downloading and installing R & RStudio
• Quick guide to the RStudio user interface
• Changing the appearance in RStudio
• Installing packages and using the library
• Case Study: Set up RStudio and install essential packages to analyze a dataset of your choice.

Module 2: Basic Building Blocks of R

• Creating an object in R
• Data types in R: Integers and doubles
• Data types in R: Characters and logicals
• Coercion rules in R
• Functions in R
• Functions and arguments
• Building a function in R
• Case Study: Develop a custom function to clean and process a sample dataset, demonstrating different data types and coercion rules.

Module 3: Working with Vectors

• Introduction to vectors
• Vector recycling
• Naming a vector
• Slicing and indexing a vector
• Vector operations
• Case Study: Perform data analysis on a vector of sales data, including operations and indexing to calculate summary statistics.

Module 4: Matrices and Matrix Operations

• Creating a matrix
• Faster code: creating a matrix in a single line of code
• Matrix recycling
• Indexing and slicing a matrix
• Matrix arithmetic and operations
• Case Study: Analyze a matrix representing monthly sales figures for different products, including performing arithmetic operations and slicing to extract specific information.

Module 5: Fundamentals of Programming with R

• Relational operators in R
• Logical operators in R
• Logical operators and vectors
• If, else, and else-if statements
• For loops, while loops, and repeat loops
• Case Study: Create a script to classify and summarize survey responses based on logical conditions and loops.

Module 6: Data Frames and Tidyverse

• Creating a data frame
• Introduction to the Tidyverse package
• Data import and export in R
• Indexing and slicing a data frame
• Dealing with missing data
• Case Study: Import a dataset into R, clean it using Tidyverse functions, and handle missing values to prepare it for analysis.

Module 7: Data Manipulation with Dplyr

• Data transformation with the Dplyr package
• Sampling data with Dplyr
• Using the pipe operator
• Tidying your data: gather(), separate(), unite(), and spread()
• Case Study: Transform and analyze a dataset using Dplyr functions, including data sampling and reshaping.

Module 8: Data Visualization with ggplot2

• Introduction to data visualization
• Introduction to ggplot2
• Building various plots: histogram, bar chart, box and whiskers plot, scatterplot
• Case Study: Create a series of visualizations to explore and present the insights from a dataset using ggplot2.

Module 9: Exploratory Data Analysis

• Population vs. sample
• Mean, median, mode
• Skewness
• Variance, standard deviation, and coefficient of variability
• Covariance and correlation
• Case Study: Perform exploratory data analysis on a sample dataset, calculating and interpreting statistical measures and visualizing relationships.

Module 10: Hypothesis Testing and Linear Regression

• Distributions and standard error
• Hypothesis testing concepts
• Test for the mean: population variance known and unknown
• Comparing two means: dependent and independent samples
• Linear regression analysis: model, interpretation, and R-squared
• Case Study: Conduct a hypothesis test and linear regression analysis on a dataset to determine relationships between variables and interpret the results.