关于此课程： 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.
此课程适用人群： This course is aimed at learners who have some experience programming computers but who are not familiar with the R environment.
教学方： Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health教学方： Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University
| 基本信息 | 课程 1（共 5 门，Mastering Software Development in R Specialization ） |
| 级别 | 中级 |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- 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.
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| 访问课程材料 | 可用 |
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Great class!!
Recorded whiteboard classes or videos from the instructors would significantly increase the value of the course. There should be more interaction with the instructors. Some weeks tasks/assignments were extremely more difficult compared to others. Otherwise, I liked it and learned a lot.
Great course in learning the basics of writing idiomatic R. dplyr and tidyr are so cool!
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Great overview of R with a focus on Hadley Wickham's tidyverse (very useful set of packages for data scientists).