À propos de ce cours : 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.

Les destinataires de ce cours : Some programming experience (in any language) is recommended.

Créé par : Université Johns-Hopkins

Enseigné par : Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Enseigné par : Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Enseigné par : Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health

Informations de base	Cours 2 de 10 dans Data Science Specialization
Niveau	Intermédiaire
Langue	English, Sous-titres : French, Chinese (Simplified), Vietnamese, Japanese
Comment réussir	Réussissez tous les devoirs notés pour terminer le cours.
Notes des utilisateurs	4.6 étoiles Note moyenne des utilisateurs 4.6Voir ce que disent les étudiants

Programme de cours

SEMAINE 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 vidéos, 9 lectures

Reading: Welcome to R Programming
Reading: About the Instructor
Reading: Pre-Course Survey
Reading: Syllabus
Reading: Course Textbook
Reading: Course Supplement: The Art of Data Science
Reading: Data Science Podcast: Not So Standard Deviations
Vidéo: Installing R on Windows
Vidéo: Installing R Studio (Mac)
Vidéo: Writing Code / Setting Your Working Directory (Windows)
Vidéo: Writing Code / Setting Your Working Directory (Mac)
Reading: Getting Started and R Nuts and Bolts
Vidéo: Introduction
Vidéo: Overview and History of R
Vidéo: Getting Help
Vidéo: R Console Input and Evaluation
Vidéo: Data Types - R Objects and Attributes
Vidéo: Data Types - Vectors and Lists
Vidéo: Data Types - Matrices
Vidéo: Data Types - Factors
Vidéo: Data Types - Missing Values
Vidéo: Data Types - Data Frames
Vidéo: Data Types - Names Attribute
Vidéo: Data Types - Summary
Vidéo: Reading Tabular Data
Vidéo: Reading Large Tables
Vidéo: Textual Data Formats
Vidéo: Connections: Interfaces to the Outside World
Vidéo: Subsetting - Basics
Vidéo: Subsetting - Lists
Vidéo: Subsetting - Matrices
Vidéo: Subsetting - Partial Matching
Vidéo: Subsetting - Removing Missing Values
Vidéo: Vectorized Operations
Vidéo: Introduction to swirl
Reading: Practical R Exercises in swirl Part 1
Ungraded Programming: swirl Lesson 1: Basic Building Blocks
Ungraded Programming: swirl Lesson 2: Workspace and Files
Ungraded Programming: swirl Lesson 3: Sequences of Numbers
Ungraded Programming: swirl Lesson 4: Vectors
Ungraded Programming: swirl Lesson 5: Missing Values
Ungraded Programming: swirl Lesson 6: Subsetting Vectors
Ungraded Programming: swirl Lesson 7: Matrices and Data Frames

Noté: Week 1 Quiz

SEMAINE 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 vidéos, 3 lectures

Reading: Week 2: Programming with R
Vidéo: Control Structures - If-else
Vidéo: Control Structures - For loops
Vidéo: Control Structures - While loops
Vidéo: Control Structures - Repeat, Next, Break
Vidéo: Your First R Function
Vidéo: Functions (part 1)
Vidéo: Functions (part 2)
Vidéo: Scoping Rules - Symbol Binding
Vidéo: Scoping Rules - R Scoping Rules
Vidéo: Scoping Rules - Optimization Example (OPTIONAL)
Vidéo: Coding Standards
Vidéo: Dates and Times
Reading: Practical R Exercises in swirl Part 2
Ungraded Programming: swirl Lesson 1: Logic
Ungraded Programming: swirl Lesson 2: Functions
Ungraded Programming: swirl Lesson 3: Dates and Times
Reading: Programming Assignment 1 INSTRUCTIONS: Air Pollution

Noté: Week 2 Quiz

Noté: Programming Assignment 1: Quiz

SEMAINE 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 vidéos, 2 lectures

Reading: Week 3: Loop Functions and Debugging
Vidéo: Loop Functions - apply
Vidéo: Loop Functions - mapply
Vidéo: Loop Functions - tapply
Vidéo: Loop Functions - split
Vidéo: Debugging Tools - Diagnosing the Problem
Vidéo: Debugging Tools - Basic Tools
Vidéo: Debugging Tools - Using the Tools
Reading: Practical R Exercises in swirl Part 3
Ungraded Programming: swirl Lesson 1: lapply and sapply
Ungraded Programming: swirl Lesson 2: vapply and tapply

Noté: Week 3 Quiz

Noté: Programming Assignment 2: Lexical Scoping

SEMAINE 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 vidéos, 4 lectures

Reading: Week 4: Simulation & Profiling
Vidéo: Simulation - Generating Random Numbers
Vidéo: Simulation - Simulating a Linear Model
Vidéo: Simulation - Random Sampling
Vidéo: R Profiler (part 1)
Vidéo: R Profiler (part 2)
Reading: Practical R Exercises in swirl Part 4
Ungraded Programming: swirl Lesson 1: Looking at Data
Ungraded Programming: swrl Lesson 2: Simulation
Ungraded Programming: swirl Lesson 3: Base Graphics
Reading: Programming Assignment 3 INSTRUCTIONS: Hospital Quality
Reading: Post-Course Survey

Noté: Week 4 Quiz

Noté: Programming Assignment 3: Quiz

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Coursework

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Créateurs

Université Johns-Hopkins

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.

Tarification

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Notation et examens

Note moyenne 4.6 sur 5 sur 11,707 notes

This is a great course provided you read the book, do all the assignments and really consider quality time for it.

Week 3 can be a bit more elaborate.

Nice course! I learned much from it. However, the knowledge sometimes didn't meet the need of student, especially in Assignment 1&3, which took me much effort and time to figure out the solution. Perhaps, professor can upload some video about the Assignment because many students, as me, would be thrilled and eager to see a explanations and deduction from the professor.