R Programming

R Programming

This course is part of multiple programs.

Instructors: Roger D. Peng, PhD

Access provided by Emirates Development Bank

748,725 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

22,364 reviews

Intermediate level

Recommended experience

Flexible schedule

6 weeks at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

22,364 reviews

Intermediate level

Recommended experience

Flexible schedule

6 weeks at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

What you'll learn

Understand critical programming language concepts
Configure statistical programming software
Make use of R loop functions and debugging tools
Collect detailed information using R profiler

Skills you'll gain

Tools you'll learn

R Programming

Details to know

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Assessments

6 assignments

Taught in English

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There are 4 modules in 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.

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.

What's included

28 videos9 readings1 assignment7 programming assignments

28 videosTotal 129 minutes

Installing R on a Mac2 minutes
Installing R on Windows3 minutes
Installing R Studio (Mac)2 minutes
Writing Code / Setting Your Working Directory (Windows)7 minutes
Writing Code / Setting Your Working Directory (Mac)8 minutes
Introduction1 minute
Overview and History of R16 minutes
Getting Help14 minutes
R Console Input and Evaluation5 minutes
Data Types - R Objects and Attributes5 minutes
Data Types - Vectors and Lists6 minutes
Data Types - Matrices3 minutes
Data Types - Factors5 minutes
Data Types - Missing Values2 minutes
Data Types - Data Frames3 minutes
Data Types - Names Attribute2 minutes
Data Types - Summary1 minute
Reading Tabular Data6 minutes
Reading Large Tables7 minutes
Textual Data Formats5 minutes
Connections: Interfaces to the Outside World5 minutes
Subsetting - Basics4 minutes
Subsetting - Lists5 minutes
Subsetting - Matrices3 minutes
Subsetting - Partial Matching2 minutes
Subsetting - Removing Missing Values4 minutes
Vectorized Operations4 minutes
Introduction to swirl1 minute

9 readingsTotal 90 minutes

Welcome to R Programming10 minutes
About the Instructor10 minutes
Pre-Course Survey10 minutes
Syllabus10 minutes
Course Textbook10 minutes
Course Supplement: The Art of Data Science10 minutes
Data Science Podcast: Not So Standard Deviations10 minutes
Getting Started and R Nuts and Bolts10 minutes
Practical R Exercises in swirl Part 110 minutes

1 assignmentTotal 40 minutes

Week 1 Quiz40 minutes

7 programming assignmentsTotal 1,260 minutes

swirl Lesson 1: Basic Building Blocks180 minutes
swirl Lesson 2: Workspace and Files180 minutes
swirl Lesson 3: Sequences of Numbers180 minutes
swirl Lesson 4: Vectors180 minutes
swirl Lesson 5: Missing Values180 minutes
swirl Lesson 6: Subsetting Vectors180 minutes
swirl Lesson 7: Matrices and Data Frames180 minutes

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.

What's included

13 videos3 readings2 assignments3 programming assignments

13 videosTotal 91 minutes

Control Structures - Introduction1 minute
Control Structures - If-else2 minutes
Control Structures - For loops4 minutes
Control Structures - While loops3 minutes
Control Structures - Repeat, Next, Break5 minutes
Your First R Function10 minutes
Functions (part 1)9 minutes
Functions (part 2)7 minutes
Scoping Rules - Symbol Binding11 minutes
Scoping Rules - R Scoping Rules9 minutes
Scoping Rules - Optimization Example (OPTIONAL)9 minutes
Coding Standards9 minutes
Dates and Times10 minutes

3 readingsTotal 30 minutes

Week 2: Programming with R10 minutes
Practical R Exercises in swirl Part 210 minutes
Programming Assignment 1 INSTRUCTIONS: Air Pollution10 minutes

2 assignmentsTotal 60 minutes

Week 2 Quiz30 minutes
Programming Assignment 1: Quiz30 minutes

3 programming assignmentsTotal 540 minutes

swirl Lesson 1: Logic180 minutes
swirl Lesson 2: Functions180 minutes
swirl Lesson 3: Dates and Times180 minutes

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.

What's included

8 videos2 readings1 assignment2 programming assignments1 peer review

8 videosTotal 61 minutes

Loop Functions - lapply9 minutes
Loop Functions - apply7 minutes
Loop Functions - mapply5 minutes
Loop Functions - tapply3 minutes
Loop Functions - split9 minutes
Debugging Tools - Diagnosing the Problem13 minutes
Debugging Tools - Basic Tools6 minutes
Debugging Tools - Using the Tools8 minutes

2 readingsTotal 20 minutes

Week 3: Loop Functions and Debugging10 minutes
Practical R Exercises in swirl Part 310 minutes

1 assignmentTotal 30 minutes

Week 3 Quiz30 minutes

2 programming assignmentsTotal 360 minutes

swirl Lesson 1: lapply and sapply180 minutes
swirl Lesson 2: vapply and tapply180 minutes

1 peer reviewTotal 60 minutes

Programming Assignment 2: Lexical Scoping 60 minutes

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.

What's included

6 videos4 readings2 assignments3 programming assignments

6 videosTotal 42 minutes

The str Function6 minutes
Simulation - Generating Random Numbers8 minutes
Simulation - Simulating a Linear Model5 minutes
Simulation - Random Sampling3 minutes
R Profiler (part 1)11 minutes
R Profiler (part 2)10 minutes

4 readingsTotal 40 minutes

Week 4: Simulation & Profiling10 minutes
Practical R Exercises in swirl Part 410 minutes
Programming Assignment 3 INSTRUCTIONS: Hospital Quality10 minutes
Post-Course Survey10 minutes

2 assignmentsTotal 60 minutes

Week 4 Quiz30 minutes
Programming Assignment 3: Quiz30 minutes

3 programming assignmentsTotal 540 minutes

swirl Lesson 1: Looking at Data180 minutes
swrl Lesson 2: Simulation180 minutes
swirl Lesson 3: Base Graphics180 minutes

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Instructors

Instructor ratings

(1,979 ratings)

Roger D. Peng, PhD

Johns Hopkins University

37 Courses1,683,165 learners

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Johns Hopkins University

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Reviewed on Mar 29, 2021

Nice course but it would be better if more links to this course and exercises were added for students to be able to have more practices not only with swirl ( ) but also with trustable third parties.

Reviewed on May 26, 2017

This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox.This is perhaps the best course on R Programming designed for a small duration.

Reviewed on Jul 15, 2018

For anyone planning to set their foot in the vast field of data analytics and R, this course is what will set the momentum and actually make you fond of programming in R by the end of the course.

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