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.
À propos de ce cours
4.6
12,725 notes
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
Data AnalysisDebuggingR ProgrammingRstudio
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Week 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 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 - Summary min
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 Quiz40 min
Semaine
2Week 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 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 Quiz20 min
Programming Assignment 1: Quiz20 min
Semaine
3Week 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 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 Quiz10 min
Semaine
4Week 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 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 Quiz20 min
Programming Assignment 3: Quiz20 min
4.6
2,660 avis28%
a commencé une nouvelle carrière après avoir terminé ces cours
83%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
par EJ•Jul 12th 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 AK•May 27th 2017
This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox.\n\nThis is perhaps the best course on R Programming designed for a small duration.
Enseignants
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
À propos de 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.
À propos de la Spécialisation Data Science
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
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