Acerca de este curso: 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.
Para quién es esta clase: This course is aimed at learners who have some experience programming computers but who are not familiar with the R environment.
Enseñado por: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Enseñado por: Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University
| Información básica | Curso 1 de 5 en Mastering Software Development in R Specialization |
| Nivel | Intermediate |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
- Vídeo: Welcome to the R Programming Environment
- 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.
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
A really good introduction to R.
very good start
English is not my native language so i had troubles with tasks, because it is sometimes
hard to understand what you have to do. My advice - rewrite final quiz and make it more easy for understanding.
SS
An excellent course. Very good introduction to R and certain packages such as dplyr and grepl. Hands-on and challenging