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.

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Niveau intermédiaire

Approx. 17 heures pour terminer

Recommandé : 6 hours/week

English

Sous-titres : English, French, Japanese, Chinese (Simplified)

Ce que vous allez apprendre

Collect detailed information using R profiler

Configure statistical programming software

Make use of R loop functions and debugging tools

Understand critical programming language concepts

Compétences que vous acquerrez

R ProgrammingData AnalysisDebuggingGithub

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Niveau intermédiaire

Approx. 17 heures pour terminer

Recommandé : 6 hours/week

English

Sous-titres : English, French, Japanese, Chinese (Simplified)

Syllabus - What you will learn from this course

1

Section

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. ...

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 - Summary0m

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 exercises

Week 1 Quiz40m

2

Section

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....

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

13 videos

Control Structures - Introduction0m

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 Quiz20m

Programming Assignment 1: Quiz20m

3

Section

10 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....

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

8 videos

Loop Functions - lapply9m

Loop Functions - apply7m

Loop Functions - mapply4m

Loop Functions - tapply3m

Loop Functions - split9m

Debugging Tools - Diagnosing the Problem12m

Debugging Tools - Basic Tools6m

Debugging Tools - Using the Tools8m

2 readings

Week 3: Loop Functions and Debugging10m

Practical R Exercises in swirl Part 310m

1 practice exercises

Week 3 Quiz10m

4

Section

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....

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 Quiz20m

Programming Assignment 3: Quiz20m

Instructors

Roger D. Peng, PhD

Associate Professor, Biostatistics

Jeff Leek, PhD

Associate Professor, Biostatistics

Brian Caffo, PhD

Professor, Biostatistics

About 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....

Frequently Asked Questions

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Spécialisation Data Science