R Programming

R Programming

This course is part of multiple programs.

Instructors: Roger D. Peng, PhD

Access provided by DBS Bank

747,386 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

22,362 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.

22,362 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 videos Total 129 minutes

Installing R on a Mac 2 minutes
Installing R on Windows 3 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
Introduction 1 minute
Overview and History of R 16 minutes
Getting Help 14 minutes
R Console Input and Evaluation 5 minutes
Data Types - R Objects and Attributes 5 minutes
Data Types - Vectors and Lists 6 minutes
Data Types - Matrices 3 minutes
Data Types - Factors 5 minutes
Data Types - Missing Values 2 minutes
Data Types - Data Frames 3 minutes
Data Types - Names Attribute 2 minutes
Data Types - Summary 1 minute
Reading Tabular Data 6 minutes
Reading Large Tables 7 minutes
Textual Data Formats 5 minutes
Connections: Interfaces to the Outside World 5 minutes
Subsetting - Basics 4 minutes
Subsetting - Lists 5 minutes
Subsetting - Matrices 3 minutes
Subsetting - Partial Matching 2 minutes
Subsetting - Removing Missing Values 4 minutes
Vectorized Operations 4 minutes
Introduction to swirl 1 minute

9 readings Total 90 minutes

Welcome to R Programming 10 minutes
About the Instructor 10 minutes
Pre-Course Survey 10 minutes
Syllabus 10 minutes
Course Textbook 10 minutes
Course Supplement: The Art of Data Science 10 minutes
Data Science Podcast: Not So Standard Deviations 10 minutes
Getting Started and R Nuts and Bolts 10 minutes
Practical R Exercises in swirl Part 1 10 minutes

1 assignment Total 40 minutes

Week 1 Quiz 40 minutes

7 programming assignments Total 1,260 minutes

swirl Lesson 1: Basic Building Blocks 180 minutes
swirl Lesson 2: Workspace and Files 180 minutes
swirl Lesson 3: Sequences of Numbers 180 minutes
swirl Lesson 4: Vectors 180 minutes
swirl Lesson 5: Missing Values 180 minutes
swirl Lesson 6: Subsetting Vectors 180 minutes
swirl Lesson 7: Matrices and Data Frames 180 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 videos Total 91 minutes

Control Structures - Introduction 1 minute
Control Structures - If-else 2 minutes
Control Structures - For loops 4 minutes
Control Structures - While loops 3 minutes
Control Structures - Repeat, Next, Break 5 minutes
Your First R Function 10 minutes
Functions (part 1) 9 minutes
Functions (part 2) 7 minutes
Scoping Rules - Symbol Binding 11 minutes
Scoping Rules - R Scoping Rules 9 minutes
Scoping Rules - Optimization Example (OPTIONAL) 9 minutes
Coding Standards 9 minutes
Dates and Times 10 minutes

3 readings Total 30 minutes

Week 2: Programming with R 10 minutes
Practical R Exercises in swirl Part 2 10 minutes
Programming Assignment 1 INSTRUCTIONS: Air Pollution 10 minutes

2 assignments Total 60 minutes

Week 2 Quiz 30 minutes
Programming Assignment 1: Quiz 30 minutes

3 programming assignments Total 540 minutes

swirl Lesson 1: Logic 180 minutes
swirl Lesson 2: Functions 180 minutes
swirl Lesson 3: Dates and Times 180 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 videos Total 61 minutes

Loop Functions - lapply 9 minutes
Loop Functions - apply 7 minutes
Loop Functions - mapply 5 minutes
Loop Functions - tapply 3 minutes
Loop Functions - split 9 minutes
Debugging Tools - Diagnosing the Problem 13 minutes
Debugging Tools - Basic Tools 6 minutes
Debugging Tools - Using the Tools 8 minutes

2 readings Total 20 minutes

Week 3: Loop Functions and Debugging 10 minutes
Practical R Exercises in swirl Part 3 10 minutes

1 assignment Total 30 minutes

Week 3 Quiz 30 minutes

2 programming assignments Total 360 minutes

swirl Lesson 1: lapply and sapply 180 minutes
swirl Lesson 2: vapply and tapply 180 minutes

1 peer review Total 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 videos Total 42 minutes

The str Function 6 minutes
Simulation - Generating Random Numbers 8 minutes
Simulation - Simulating a Linear Model 5 minutes
Simulation - Random Sampling 3 minutes
R Profiler (part 1) 11 minutes
R Profiler (part 2) 10 minutes

4 readings Total 40 minutes

Week 4: Simulation & Profiling 10 minutes
Practical R Exercises in swirl Part 4 10 minutes
Programming Assignment 3 INSTRUCTIONS: Hospital Quality 10 minutes
Post-Course Survey 10 minutes

2 assignments Total 60 minutes

Week 4 Quiz 30 minutes
Programming Assignment 3: Quiz 30 minutes

3 programming assignments Total 540 minutes

swirl Lesson 1: Looking at Data 180 minutes
swrl Lesson 2: Simulation 180 minutes
swirl Lesson 3: Base Graphics 180 minutes

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Instructors

Instructor ratings

(1,979 ratings)

Roger D. Peng, PhD

Johns Hopkins University

37 Courses 1,679,740 learners

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Johns Hopkins University

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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 Feb 11, 2022

Difficult to follow at times. it will take a lot of time to get through the assigments. but if you are serious about learning R or any programming, it is necessary to get thrown into the fire.

Reviewed on Jul 16, 2020

This is course is probably my favorite out of the Data Science: Foundations using R specialization. There was plenty of opportunity to practice and further develop by burgeoning R programming skill.

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