This repository contains files and data used to complete the R Programming course.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Week 1: Background, Getting Started, and Nuts & Bolts This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story. 28 videos, 9 readings
Reading: Welcome to R Programming
Reading: About the Instructor
Reading: Pre-Course Survey
Reading: Syllabus
Reading: Course Textbook
Reading: Course Supplement: The Art of Data Science
Reading: Data Science Podcast: Not So Standard Deviations
Video: Installing R on a Mac
Video: Installing R on Windows
Video: Installing R Studio (Mac)
Video: Writing Code / Setting Your Working Directory (Windows)
Video: Writing Code / Setting Your Working Directory (Mac)
Reading: Getting Started and R Nuts and Bolts
Video: Introduction
Video: Overview and History of R
Video: Getting Help
Video: R Console Input and Evaluation
Video: Data Types - R Objects and Attributes
Video: Data Types - Vectors and Lists
Video: Data Types - Matrices
Video: Data Types - Factors
Video: Data Types - Missing Values
Video: Data Types - Data Frames
Video: Data Types - Names Attribute
Video: Data Types - Summary
Video: Reading Tabular Data
Video: Reading Large Tables
Video: Textual Data Formats
Video: Connections: Interfaces to the Outside World
Video: Subsetting - Basics
Video: Subsetting - Lists
Video: Subsetting - Matrices
Video: Subsetting - Partial Matching
Video: Subsetting - Removing Missing Values
Video: Vectorized Operations
Video: Introduction to swirl
Reading: Practical R Exercises in swirl Part 1
Ungraded Programming: swirl Lesson 1: Basic Building Blocks
Ungraded Programming: swirl Lesson 2: Workspace and Files
Ungraded Programming: swirl Lesson 3: Sequences of Numbers
Ungraded Programming: swirl Lesson 4: Vectors
Ungraded Programming: swirl Lesson 5: Missing Values
Ungraded Programming: swirl Lesson 6: Subsetting Vectors
Ungraded Programming: swirl Lesson 7: Matrices and Data Frames
Graded: Week 1 Quiz
Week 2: Programming with R Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week. 13 videos, 3 readings
Reading: Week 2: Programming with R
Video: Control Structures - Introduction
Video: Control Structures - If-else
Video: Control Structures - For loops
Video: Control Structures - While loops
Video: Control Structures - Repeat, Next, Break
Video: Your First R Function
Video: Functions (part 1)
Video: Functions (part 2)
Video: Scoping Rules - Symbol Binding
Video: Scoping Rules - R Scoping Rules
Video: Scoping Rules - Optimization Example (OPTIONAL)
Video: Coding Standards
Video: Dates and Times
Reading: Practical R Exercises in swirl Part 2
Ungraded Programming: swirl Lesson 1: Logic
Ungraded Programming: swirl Lesson 2: Functions
Ungraded Programming: swirl Lesson 3: Dates and Times
Reading: Programming Assignment 1 INSTRUCTIONS: Air Pollution
Graded: Week 2 Quiz Graded: Programming Assignment 1: Quiz
Week 3: Loop Functions and Debugging We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice. 8 videos, 2 readings
Reading: Week 3: Loop Functions and Debugging
Video: Loop Functions - lapply
Video: Loop Functions - apply
Video: Loop Functions - mapply
Video: Loop Functions - tapply
Video: Loop Functions - split
Video: Debugging Tools - Diagnosing the Problem
Video: Debugging Tools - Basic Tools
Video: Debugging Tools - Using the Tools
Reading: Practical R Exercises in swirl Part 3
Ungraded Programming: swirl Lesson 1: lapply and sapply
Ungraded Programming: swirl Lesson 2: vapply and tapply
Graded: Week 3 Quiz Graded: Programming Assignment 2: Lexical Scoping
Week 4: Simulation & Profiling This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R. 6 videos, 4 readings
Reading: Week 4: Simulation & Profiling
Video: The str Function
Video: Simulation - Generating Random Numbers
Video: Simulation - Simulating a Linear Model
Video: Simulation - Random Sampling
Video: R Profiler (part 1)
Video: R Profiler (part 2)
Reading: Practical R Exercises in swirl Part 4
Ungraded Programming: swirl Lesson 1: Looking at Data
Ungraded Programming: swrl Lesson 2: Simulation
Ungraded Programming: swirl Lesson 3: Base Graphics
Reading: Programming Assignment 3 INSTRUCTIONS: Hospital Quality
Reading: Post-Course Survey
Graded: Week 4 Quiz Graded: Programming Assignment 3: Quiz.