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.
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Université Johns-HopkinsÀ propos de ce cours
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Résultats de carrière des étudiants
32%
ont commencé une nouvelle carrière après avoir terminé ce cours
32%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
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100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
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Niveau intermédiaire
Familiarity with regression is recommended.
Approx. 57 heures pour terminer
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Sous-titres : Arabe, Français, Portugais (brésilien), Chinois (simplifié), Italien, Portugais (brésilien), Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol, Japonais, Chinois
Ce que vous allez apprendre
Understand critical programming language concepts
Configure statistical programming software
Make use of R loop functions and debugging tools
Collect detailed information using R profiler
Compétences que vous acquerrez
Data AnalysisDebuggingR ProgrammingRstudio
Résultats de carrière des étudiants
32%
ont commencé une nouvelle carrière après avoir terminé ce cours
32%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Familiarity with regression is recommended.
Approx. 57 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (brésilien), Chinois (simplifié), Italien, Portugais (brésilien), Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol, Japonais, Chinois
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
25 heures pour terminer
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 heures pour terminer
28 vidéos (Total 129 min), 9 lectures, 8 quiz
28 vidéos
Installing R on Windows3 min
Installing R Studio (Mac)1 min
Writing Code / Setting Your Working Directory (Windows)7 min
Writing Code / Setting Your Working Directory (Mac)7 min
Introduction1 min
Overview and History of R16 min
Getting Help13 min
R Console Input and Evaluation4 min
Data Types - R Objects and Attributes4 min
Data Types - Vectors and Lists6 min
Data Types - Matrices3 min
Data Types - Factors4 min
Data Types - Missing Values2 min
Data Types - Data Frames2 min
Data Types - Names Attribute1 min
Data Types - Summary43s
Reading Tabular Data5 min
Reading Large Tables7 min
Textual Data Formats4 min
Connections: Interfaces to the Outside World4 min
Subsetting - Basics4 min
Subsetting - Lists4 min
Subsetting - Matrices2 min
Subsetting - Partial Matching1 min
Subsetting - Removing Missing Values3 min
Vectorized Operations3 min
Introduction to swirl1 min
9 lectures
Welcome to R Programming10 min
About the Instructor10 min
Pre-Course Survey10 min
Syllabus10 min
Course Textbook10 min
Course Supplement: The Art of Data Science10 min
Data Science Podcast: Not So Standard Deviations10 min
Getting Started and R Nuts and Bolts10 min
Practical R Exercises in swirl Part 110 min
1 exercice pour s'entraîner
Week 1 Quiz30 min
12 heures pour terminer
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 heures pour terminer
13 vidéos (Total 91 min), 3 lectures, 5 quiz
13 vidéos
Control Structures - If-else1 min
Control Structures - For loops4 min
Control Structures - While loops3 min
Control Structures - Repeat, Next, Break4 min
Your First R Function10 min
Functions (part 1)9 min
Functions (part 2)7 min
Scoping Rules - Symbol Binding10 min
Scoping Rules - R Scoping Rules8 min
Scoping Rules - Optimization Example (OPTIONAL)9 min
Coding Standards8 min
Dates and Times10 min
3 lectures
Week 2: Programming with R10 min
Practical R Exercises in swirl Part 210 min
Programming Assignment 1 INSTRUCTIONS: Air Pollution10 min
2 exercices pour s'entraîner
Week 2 Quiz30 min
Programming Assignment 1: Quiz30 min
9 heures pour terminer
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 heures pour terminer
8 vidéos (Total 61 min), 2 lectures, 4 quiz
8 vidéos
Loop Functions - apply7 min
Loop Functions - mapply4 min
Loop Functions - tapply3 min
Loop Functions - split9 min
Debugging Tools - Diagnosing the Problem12 min
Debugging Tools - Basic Tools6 min
Debugging Tools - Using the Tools8 min
2 lectures
Week 3: Loop Functions and Debugging10 min
Practical R Exercises in swirl Part 310 min
1 exercice pour s'entraîner
Week 3 Quiz30 min
11 heures pour terminer
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 heures pour terminer
6 vidéos (Total 42 min), 4 lectures, 5 quiz
6 vidéos
The str Function6 min
Simulation - Generating Random Numbers7 min
Simulation - Simulating a Linear Model4 min
Simulation - Random Sampling2 min
R Profiler (part 1)10 min
R Profiler (part 2)10 min
4 lectures
Week 4: Simulation & Profiling10 min
Practical R Exercises in swirl Part 410 min
Programming Assignment 3 INSTRUCTIONS: Hospital Quality10 min
Post-Course Survey10 min
2 exercices pour s'entraîner
Week 4 Quiz30 min
Programming Assignment 3: Quiz30 min
Avis
Meilleurs avis pour LA PROGRAMMATION EN R
par JM11 août 2019
Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.
par WH2 févr. 2016
"R Programming" forces you to dive in deep. These skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
par EJ11 juil. 2016
Excellent course! I already knew a lot about R - but this class helped me solidify what I already knew, taught me lots of new tricks, and now I have a certificate that says I know `something' about R!
par AK26 mai 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.
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