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The R Programming Environment

总览授课大纲常见问题解答制作方价格评分和审阅

主页数据科学数据分析

The R Programming Environment

约翰霍普金斯大学

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.


制作方:  约翰霍普金斯大学
约翰霍普金斯大学

  • Roger D. Peng, PhD

    教学方:  Roger D. Peng, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health

  • Brooke Anderson

    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Intermediate
语言
English
如何通过通过所有计分作业以完成课程。
用户评分
4.4 星
平均用户评分 4.4查看学生的留言
授课大纲
第 1 周
Basic R Language
In this module, you'll learn the basics of R, including syntax, some tidy data principles and processes, and how to read data into R.
1 视频, 27 阅读材料
  1. 视频: Welcome to the R Programming Environment
  2. Reading: Course Textbook: Mastering Software Development in R
  3. Reading: Syllabus
  4. Reading: Swirl Assignments
  5. Reading: Datasets
  6. Reading: Lesson Introduction
  7. Reading: Evaluation
  8. Reading: Objects
  9. Reading: Numbers
  10. Reading: Creating Vectors
  11. Reading: Mixing Objects
  12. Reading: Explicit Coercion
  13. Reading: Matrices
  14. Reading: Lists
  15. Reading: Factors
  16. Reading: Missing Values
  17. Reading: Data Frames
  18. Reading: Names
  19. Reading: Attributes
  20. Reading: Summary
  21. Reading: The Importance of Tidy Data
  22. Reading: The “Tidyverse”
  23. Reading: Reading Tabular Data with the readr Package
  24. Reading: Reading Web-Based Data
  25. Reading: Flat files online
  26. Reading: Requesting data through a web API
  27. Reading: Scraping web data
  28. Reading: Parsing JSON, XML, or HTML data
已评分: Swirl Lessons
第 2 周
Data Manipulation
During this module, you'll learn to summarize, filter, merge, and otherwise manipulate data in R, including working through the challenges of dates and times.
11 阅读材料
  1. Reading: Basic Data Manipulation
  2. Reading: Piping
  3. Reading: Summarizing data
  4. Reading: Selecting and filtering data
  5. Reading: Adding, changing, or renaming columns
  6. Reading: Spreading and gathering data
  7. Reading: Merging datasets
  8. Reading: Working with Dates, Times, Time Zones
  9. Reading: Converting to a date or date-time class
  10. Reading: Pulling out date and time elements
  11. Reading: Working with time zones
已评分: Swirl Lessons
第 3 周
Text Processing, Regular Expression, & Physical Memory
During this module, you'll learn to use R tools and packages to deal with text and regular expressions. You'll also learn how to manage and get the most from your computer's physical memory when working in R.
9 阅读材料
  1. Reading: Text Processing and Regular Expressions
  2. Reading: Text Manipulation Functions in R
  3. Reading: Regular Expressions
  4. Reading: RegEx Functions in R
  5. Reading: The stringr Package
  6. Reading: Summary
  7. Reading: The Role of Physical Memory
  8. Reading: Back of the Envelope Calculations
  9. Reading: Internal Memory Management in R
已评分: Swirl Lessons
第 4 周
Large Datasets
In this final module, you'll learn how to overcome the challenges of working with large datasets both in memory and out as well as how to diagnose problems and find help.
7 阅读材料
  1. Reading: Working with Large Datasets
  2. Reading: In-memory strategies
  3. Reading: Out-of-memory strategies
  4. Reading: Diagnosing Problems
  5. Reading: How to Google Your Way Out of a Jam
  6. Reading: Asking for Help
  7. Reading: Quiz Instructions
已评分: Reading and Summarizing Data

常见问题解答
运作方式
Coursework
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制作方
约翰霍普金斯大学
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.
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评分和审阅
已评分 4.4,总共 5 个 584 评分

SS

An excellent course. Very good introduction to R and certain packages such as dplyr and grepl. Hands-on and challenging

JZ

A really good introduction to R.

Mayer Antoine

very good start

AY

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.



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