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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Datenverarbeitung
R-Programmierung
von
Über diesen Kurs
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Was Sie lernen werden
Collect detailed information using R profiler
Configure statistical programming software
Make use of R loop functions and debugging tools
Understand critical programming language concepts
Kompetenzen, die Sie erwerben
Data AnalysisDebuggingR ProgrammingRstudio
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
125 Stunden zum Abschließen
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 (Gesamt 129 min), 9 Lektüren, 8 Quiz
28 Videos
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 - Summary43
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 Lektüren
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 praktische Übung
Week 1 Quiz40m
Woche
212 Stunden zum Abschließen
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 (Gesamt 91 min), 3 Lektüren, 5 Quiz
13 Videos
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 Lektüren
Week 2: Programming with R10m
Practical R Exercises in swirl Part 210m
Programming Assignment 1 INSTRUCTIONS: Air Pollution10m
2 praktische Übungen
Week 2 Quiz20m
Programming Assignment 1: Quiz20m
Woche
310 Stunden zum Abschließen
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 (Gesamt 61 min), 2 Lektüren, 4 Quiz
8 Videos
Loop Functions - tapply3m
Loop Functions - split9m
Debugging Tools - Diagnosing the Problem12m
Debugging Tools - Basic Tools6m
Debugging Tools - Using the Tools8m
2 Lektüren
Week 3: Loop Functions and Debugging10m
Practical R Exercises in swirl Part 310m
1 praktische Übung
Week 3 Quiz10m
Woche
411 Stunden zum Abschließen
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 (Gesamt 42 min), 4 Lektüren, 5 Quiz
6 Videos
Simulation - Random Sampling2m
R Profiler (part 1)10m
R Profiler (part 2)10m
4 Lektüren
Week 4: Simulation & Profiling10m
Practical R Exercises in swirl Part 410m
Programming Assignment 3 INSTRUCTIONS: Hospital Quality10m
Post-Course Survey10m
2 praktische Übungen
Week 4 Quiz20m
Programming Assignment 3: Quiz20m
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Top reviews from R-Programmierung
"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
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!
Dozenten
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
Über 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.
Über die Spezialisierung Datenverarbeitung
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.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Sobald Sie sich für ein Zertifikat angemeldet haben, haben Sie Zugriff auf alle Videos, Quizspiele und Programmieraufgaben (falls zutreffend). Aufgaben, die von anderen Kursteilnehmern bewertet werden, können erst dann eingereicht und überprüft werden, wenn Ihr Unterricht begonnen hat. Wenn Sie sich den Kurs anschauen möchten, ohne ihn zu kaufen, können Sie womöglich auf bestimmte Aufgaben nicht zugreifen.
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse der Spezialisierung und Sie erhalten nach Abschluss aller Arbeiten ein Zertifikat. Ihr elektronisches Zertifikat wird zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
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