4.5
stars
21,627 ratings
•
4,688 reviews

Roger D. Peng, PhD +2 more instructors

656,970 already enrolled

Offered By

Johns Hopkins University

About this Course

360,569 recent views

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Familiarity with regression is recommended.

Approx. 57 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, English, Spanish, Japanese

What you will 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 will gain

Data Analysis
Debugging
R Programming
Rstudio

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Familiarity with regression is recommended.

Approx. 57 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, English, Spanish, Japanese

Instructors

Instructor rating

4.4/5 (1,795 Ratings)

Offered by

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 - What you will learn from this course

Content Rating94%(102,146 ratings)

Week

Week 1

25 hours to complete

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.

25 hours to complete

28 videos (Total 129 min), 9 readings, 8 quizzes

28 videos

Installing R on a Mac1m

Installing R on Windows3m

Installing R Studio (Mac)1m

Writing Code / Setting Your Working Directory (Windows)7m

Writing Code / Setting Your Working Directory (Mac)7m

Introduction1m

Overview and History of R16m

Getting Help13m

R Console Input and Evaluation4m

Data Types - R Objects and Attributes4m

Data Types - Vectors and Lists6m

Data Types - Matrices3m

Data Types - Factors4m

Data Types - Missing Values2m

Data Types - Data Frames2m

Data Types - Names Attribute1m

Data Types - Summary43s

Reading Tabular Data5m

Reading Large Tables7m

Textual Data Formats4m

Connections: Interfaces to the Outside World4m

Subsetting - Basics4m

Subsetting - Lists4m

Subsetting - Matrices2m

Subsetting - Partial Matching1m

Subsetting - Removing Missing Values3m

Vectorized Operations3m

Introduction to swirl1m

9 readings

Welcome to R Programming10m

About the Instructor10m

Pre-Course Survey10m

Syllabus10m

Course Textbook10m

Course Supplement: The Art of Data Science10m

Data Science Podcast: Not So Standard Deviations10m

Getting Started and R Nuts and Bolts10m

Practical R Exercises in swirl Part 110m

1 practice exercise

Week 1 Quiz30m

Week

Week 2

12 hours to complete

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.

12 hours to complete

13 videos (Total 91 min), 3 readings, 5 quizzes

13 videos

Control Structures - Introduction54s

Control Structures - If-else1m

Control Structures - For loops4m

Control Structures - While loops3m

Control Structures - Repeat, Next, Break4m

Your First R Function10m

Functions (part 1)9m

Functions (part 2)7m

Scoping Rules - Symbol Binding10m

Scoping Rules - R Scoping Rules8m

Scoping Rules - Optimization Example (OPTIONAL)9m

Coding Standards8m

Dates and Times10m

3 readings

Week 2: Programming with R10m

Practical R Exercises in swirl Part 210m

Programming Assignment 1 INSTRUCTIONS: Air Pollution10m

2 practice exercises

Week 2 Quiz30m

Programming Assignment 1: Quiz30m

Week

Week 3

9 hours to complete

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.

9 hours to complete

8 videos (Total 61 min), 2 readings, 4 quizzes

Week

Week 4

11 hours to complete

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.

11 hours to complete

6 videos (Total 42 min), 4 readings, 5 quizzes

6 videos

The str Function6m

Simulation - Generating Random Numbers7m

Simulation - Simulating a Linear Model4m

Simulation - Random Sampling2m

R Profiler (part 1)10m

R Profiler (part 2)10m

4 readings

Week 4: Simulation & Profiling10m

Practical R Exercises in swirl Part 410m

Programming Assignment 3 INSTRUCTIONS: Hospital Quality10m

Post-Course Survey10m

2 practice exercises

Week 4 Quiz30m

Programming Assignment 3: Quiz30m

Reviews

4.5

4688 reviews

5 stars
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4 stars
22.20%
3 stars
5.81%
2 stars
2.06%
1 star
1.77%

TOP REVIEWS FROM R PROGRAMMING

by AKMay 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.

by DVFeb 2, 2016

The content is superbly designer for a beginner. The Swirl assignments need to make compulsory. Infact they contributed more to the learning process. More Swirl contents will make the course richer.

by BPJul 3, 2019

This course was almost excellent. The tutorials were amazing. I am just going to complain about Assignment 2; inverted matrices weren't a pre-requisite so it was hard to understand that assignment

by MLNov 2, 2018

This is a very thorough introduction to R. There are plenty of exercises to quickly get familiar with the language. Some good guided assignments really help getting familiar with coding functions.

View all reviews

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R Programming