The R Programming Environment

About this course: 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.

Who is this class for: This course is aimed at learners who have some experience programming computers but who are not familiar with the R environment.

Created by:  Johns Hopkins University

• Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences

  Colorado State University

Basic Info Course 1 of 5 in the Mastering Software Development in R Specialization Level Intermediate Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.4 stars Average User Rating 4.4See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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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.

Syllabus


WEEK 1


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 video, 27 readings expand

1. Video: 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

Graded: Swirl Lessons

WEEK 2


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 readings expand

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

Graded: Swirl Lessons

WEEK 3


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 readings expand

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

Graded: Swirl Lessons

WEEK 4


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 readings expand

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

Graded: Reading and Summarizing Data