R Programming

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About this 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.

Who is this class for: Some programming experience (in any languag…

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Didn't find what you were looking for? See also: R Programming, Programming (general), Database Management, Data Storage, and Python.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this 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.

Who is this class for: Some programming experience (in any language) is recommended.

Created by:  Johns Hopkins University
  • Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health
  • Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health
  • Taught by:  Brian Caffo, PhD, Professor, Biostatistics

    Bloomberg School of Public Health
Basic Info Course 2 of 10 in the Data Science Specialization Level Intermediate Language English, Subtitles: French, Japanese, Chinese (Simplified) How To Pass Pass all graded assignments to complete the course. User Ratings 4.5 stars Average User Rating 4.5See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Johns Hopkins University The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Syllabus


WEEK 1


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 expand


  1. Reading: Welcome to R Programming
  2. Reading: About the Instructor
  3. Reading: Pre-Course Survey
  4. Reading: Syllabus
  5. Reading: Course Textbook
  6. Reading: Course Supplement: The Art of Data Science
  7. Reading: Data Science Podcast: Not So Standard Deviations
  8. Video: Installing R on a Mac
  9. Video: Installing R on Windows
  10. Video: Installing R Studio (Mac)
  11. Video: Writing Code / Setting Your Working Directory (Windows)
  12. Video: Writing Code / Setting Your Working Directory (Mac)
  13. Reading: Getting Started and R Nuts and Bolts
  14. Video: Introduction
  15. Video: Overview and History of R
  16. Video: Getting Help
  17. Video: R Console Input and Evaluation
  18. Video: Data Types - R Objects and Attributes
  19. Video: Data Types - Vectors and Lists
  20. Video: Data Types - Matrices
  21. Video: Data Types - Factors
  22. Video: Data Types - Missing Values
  23. Video: Data Types - Data Frames
  24. Video: Data Types - Names Attribute
  25. Video: Data Types - Summary
  26. Video: Reading Tabular Data
  27. Video: Reading Large Tables
  28. Video: Textual Data Formats
  29. Video: Connections: Interfaces to the Outside World
  30. Video: Subsetting - Basics
  31. Video: Subsetting - Lists
  32. Video: Subsetting - Matrices
  33. Video: Subsetting - Partial Matching
  34. Video: Subsetting - Removing Missing Values
  35. Video: Vectorized Operations
  36. Video: Introduction to swirl
  37. Reading: Practical R Exercises in swirl Part 1
  38. Ungraded Programming: swirl Lesson 1: Basic Building Blocks
  39. Ungraded Programming: swirl Lesson 2: Workspace and Files
  40. Ungraded Programming: swirl Lesson 3: Sequences of Numbers
  41. Ungraded Programming: swirl Lesson 4: Vectors
  42. Ungraded Programming: swirl Lesson 5: Missing Values
  43. Ungraded Programming: swirl Lesson 6: Subsetting Vectors
  44. Ungraded Programming: swirl Lesson 7: Matrices and Data Frames

Graded: Week 1 Quiz

WEEK 2


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 expand


  1. Reading: Week 2: Programming with R
  2. Video: Control Structures - Introduction
  3. Video: Control Structures - If-else
  4. Video: Control Structures - For loops
  5. Video: Control Structures - While loops
  6. Video: Control Structures - Repeat, Next, Break
  7. Video: Your First R Function
  8. Video: Functions (part 1)
  9. Video: Functions (part 2)
  10. Video: Scoping Rules - Symbol Binding
  11. Video: Scoping Rules - R Scoping Rules
  12. Video: Scoping Rules - Optimization Example (OPTIONAL)
  13. Video: Coding Standards
  14. Video: Dates and Times
  15. Reading: Practical R Exercises in swirl Part 2
  16. Ungraded Programming: swirl Lesson 1: Logic
  17. Ungraded Programming: swirl Lesson 2: Functions
  18. Ungraded Programming: swirl Lesson 3: Dates and Times
  19. Reading: Programming Assignment 1 INSTRUCTIONS: Air Pollution

Graded: Week 2 Quiz
Graded: Programming Assignment 1: Quiz

WEEK 3


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 expand


  1. Reading: Week 3: Loop Functions and Debugging
  2. Video: Loop Functions - lapply
  3. Video: Loop Functions - apply
  4. Video: Loop Functions - mapply
  5. Video: Loop Functions - tapply
  6. Video: Loop Functions - split
  7. Video: Debugging Tools - Diagnosing the Problem
  8. Video: Debugging Tools - Basic Tools
  9. Video: Debugging Tools - Using the Tools
  10. Reading: Practical R Exercises in swirl Part 3
  11. Ungraded Programming: swirl Lesson 1: lapply and sapply
  12. Ungraded Programming: swirl Lesson 2: vapply and tapply

Graded: Week 3 Quiz
Graded: Programming Assignment 2: Lexical Scoping

WEEK 4


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 expand


  1. Reading: Week 4: Simulation & Profiling
  2. Video: The str Function
  3. Video: Simulation - Generating Random Numbers
  4. Video: Simulation - Simulating a Linear Model
  5. Video: Simulation - Random Sampling
  6. Video: R Profiler (part 1)
  7. Video: R Profiler (part 2)
  8. Reading: Practical R Exercises in swirl Part 4
  9. Ungraded Programming: swirl Lesson 1: Looking at Data
  10. Ungraded Programming: swrl Lesson 2: Simulation
  11. Ungraded Programming: swirl Lesson 3: Base Graphics
  12. Reading: Programming Assignment 3 INSTRUCTIONS: Hospital Quality
  13. Reading: Post-Course Survey

Graded: Week 4 Quiz
Graded: Programming Assignment 3: Quiz
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