À propos de ce cours : This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.
Les destinataires de ce cours : This course is aimed at learners who have some experience programming computers but who are not familiar with the R environment.
Enseigné par : Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Enseigné par : Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University
| Informations de base | Cours 1 de 5 dans Mastering Software Development in R Specialization |
| Niveau | Intermédiaire |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Reading: Course Textbook: Mastering Software Development in R
- Reading: Syllabus
- Reading: Swirl Assignments
- Reading: Datasets
- Reading: Lesson Introduction
- Reading: Evaluation
- Reading: Objects
- Reading: Numbers
- Reading: Creating Vectors
- Reading: Mixing Objects
- Reading: Explicit Coercion
- Reading: Matrices
- Reading: Lists
- Reading: Factors
- Reading: Missing Values
- Reading: Data Frames
- Reading: Names
- Reading: Attributes
- Reading: Summary
- Reading: The Importance of Tidy Data
- Reading: The “Tidyverse”
- Reading: Reading Tabular Data with the readr Package
- Reading: Reading Web-Based Data
- Reading: Flat files online
- Reading: Requesting data through a web API
- Reading: Scraping web data
- Reading: Parsing JSON, XML, or HTML data
- Reading: Basic Data Manipulation
- Reading: Piping
- Reading: Summarizing data
- Reading: Selecting and filtering data
- Reading: Adding, changing, or renaming columns
- Reading: Spreading and gathering data
- Reading: Merging datasets
- Reading: Working with Dates, Times, Time Zones
- Reading: Converting to a date or date-time class
- Reading: Pulling out date and time elements
- Reading: Working with time zones
- Reading: Text Processing and Regular Expressions
- Reading: Text Manipulation Functions in R
- Reading: Regular Expressions
- Reading: RegEx Functions in R
- Reading: The stringr Package
- Reading: Summary
- Reading: The Role of Physical Memory
- Reading: Back of the Envelope Calculations
- Reading: Internal Memory Management in R
- Reading: Working with Large Datasets
- Reading: In-memory strategies
- Reading: Out-of-memory strategies
- Reading: Diagnosing Problems
- Reading: How to Google Your Way Out of a Jam
- Reading: Asking for Help
- Reading: Quiz Instructions
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Super Crash Course in learning almost every detailed aspect of R!!
I wish there had been more exercises like the final Week 4 quiz along the way -- Swirl was useful as an interactive supplement to the readings, but those last 10 questions were the only time where I really felt challenged to use learnings to solve problems more independently and originally.
Good to learn the possibilities in the R environment. In the end you learn most by applying it to your own projects (with a lot of help in available documentation or via internet sea).
p
Great review of concepts that I had already learned at some point. Looking forward to 'Advanced R Programming'.