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The R Programming Environment
  • Level Professional
  • Duration 27 hours
  • Course by Johns Hopkins University
  • Offered by

About

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.

Modules

Welcome

    • 1 Videos
    • 4 Readings
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1 Videos

  • Welcome to the R Programming Environment

4 Readings

  • Course Textbook: Mastering Software Development in R
  • Syllabus
  • Swirl Assignments
  • Datasets

Crash Course on R Syntax

    • 15 Readings
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15 Readings

  • Lesson Introduction
  • Evaluation
  • Objects
  • Numbers
  • Creating Vectors
  • Mixing Objects
  • Explicit Coercion
  • Matrices
  • Lists
  • Factors
  • Missing Values
  • Data Frames
  • Names
  • Attributes
  • Summary

The Importance of Tidy Data

    • 2 Readings
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2 Readings

  • The Importance of Tidy Data
  • The “Tidyverse”

Reading Tabular Data with the readr Package

    • 1 Readings
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1 Readings

  • Reading Tabular Data with the readr Package

Reading Web-Based Data

    • 5 Readings
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5 Readings

  • Reading Web-Based Data
  • Flat files online
  • Requesting data through a web API
  • Scraping web data
  • Parsing JSON, XML, or HTML data

Swirl: R Basics - Automatic Submission

    • 1 Programming
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1 Programming

  • Swirl Lessons

Swirl: R Basics - Manual Submission

    • 1 Assignment
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1 Assignment

  • Swirl Lessons

Basic Data Manipulation

    • 7 Readings
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7 Readings

  • Basic Data Manipulation
  • Piping
  • Summarizing data
  • Selecting and filtering data
  • Adding, changing, or renaming columns
  • Spreading and gathering data
  • Merging datasets

Working with Dates, Times, Time Zones

    • 4 Readings
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4 Readings

  • Working with Dates, Times, Time Zones
  • Converting to a date or date-time class
  • Pulling out date and time elements
  • Working with time zones

Swirl: Data Manipulation - Automatic Submission

    • 1 Programming
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1 Programming

  • Swirl Lessons

Swirl: Data Manipulation - Manual Submission

    • 1 Assignment
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1 Assignment

  • Swirl Lessons

Text Processing and Regular Expressions

    • 6 Readings
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6 Readings

  • Text Processing and Regular Expressions
  • Text Manipulation Functions in R
  • Regular Expressions
  • RegEx Functions in R
  • The stringr Package
  • Summary

The Role of Physical Memory

    • 3 Readings
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3 Readings

  • The Role of Physical Memory
  • Back of the Envelope Calculations
  • Internal Memory Management in R

Swirl: Text and Regular Expressions - Automatic Submission

    • 1 Programming
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1 Programming

  • Swirl Lessons

Swirl: Text and Regular Expressions - Manual Submission

    • 1 Assignment
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1 Assignment

  • Swirl Lessons

Working with Large Datasets

    • 3 Readings
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3 Readings

  • Working with Large Datasets
  • In-memory strategies
  • Out-of-memory strategies

Diagnosing Problems

    • 3 Readings
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3 Readings

  • Diagnosing Problems
  • How to Google Your Way Out of a Jam
  • Asking for Help

Data Manipulation and Summary

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Reading and Summarizing Data

1 Readings

  • Quiz Instructions

Auto Summary

"The R Programming Environment" by Coursera offers a comprehensive introduction to R programming, tailored for data science and software development. This professional-level course covers R fundamentals, tidy data concepts, data processing, and basic data science tasks. Ideal for data science teams and individual developers, the course ensures fluency in R and the ability to create tidy datasets. With a duration of 1620 minutes, subscription options include Starter, Professional, and Paid.

Roger D. Peng, PhD
Instructors

Roger D. Peng, PhD

Brooke Anderson
Instructors

Brooke Anderson