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The R Programming Environment

The R Programming Environment

This course is part of Mastering Software Development in R Specialization

Instructors: Roger D. Peng, PhD

53,455 already enrolled

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7 modules

Gain insight into a topic and learn the fundamentals.

1,167 reviews

Intermediate level

Some related experience required

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

92%

Most learners liked this course

7 modules

Gain insight into a topic and learn the fundamentals.

1,167 reviews

Intermediate level

Some related experience required

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

92%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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This course is part of the Mastering Software Development in R Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Develop job-relevant skills with hands-on projects
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There are 7 modules in 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.

Module details

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.

What's included

1 video27 readings

1 videoTotal 2 minutes

Welcome to the R Programming Environment2 minutes

27 readingsTotal 109 minutes

Course Textbook: Mastering Software Development in R1 minute
Syllabus10 minutes
Swirl Assignments10 minutes
Datasets10 minutes
Lesson Introduction2 minutes
Evaluation3 minutes
Objects1 minute
Numbers1 minute
Creating Vectors1 minute
Mixing Objects1 minute
Explicit Coercion3 minutes
Matrices3 minutes
Lists2 minutes
Factors4 minutes
Missing Values3 minutes
Data Frames3 minutes
Names4 minutes
Attributes1 minute
Summary1 minute
The Importance of Tidy Data5 minutes
The “Tidyverse”4 minutes
Reading Tabular Data with the readr Package10 minutes
Reading Web-Based Data1 minute
Flat files online10 minutes
Requesting data through a web API10 minutes
Scraping web data2 minutes
Parsing JSON, XML, or HTML data3 minutes

What's included

1 assignment1 programming assignment

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.

What's included

11 readings

11 readingsTotal 87 minutes

Basic Data Manipulation10 minutes
Piping7 minutes
Summarizing data10 minutes
Selecting and filtering data10 minutes
Adding, changing, or renaming columns7 minutes
Spreading and gathering data10 minutes
Merging datasets10 minutes
Working with Dates, Times, Time Zones3 minutes
Converting to a date or date-time class7 minutes
Pulling out date and time elements5 minutes
Working with time zones8 minutes

What's included

1 assignment1 programming assignment

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.

What's included

9 readings

Choice 1: Get credit while using swirl | Choice 2: Get credit by providing a code from swirl

What's included

1 assignment1 programming assignment

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.

What's included

7 readings1 assignment

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Instructors

Instructor ratings

(89 ratings)

Roger D. Peng, PhD

Johns Hopkins University

37 Courses1,687,628 learners

Brooke Anderson

Johns Hopkins University

5 Courses77,358 learners

Offered by

Johns Hopkins University

Learner reviews

5 stars
59.12%
4 stars
24.85%
3 stars
7.71%
2 stars
3.34%
1 star
4.97%

Showing 3 of 1167

Reviewed on Jun 10, 2020

Overall, it is an excellent course. However, there was a big difference in terms of difficulty between the quizzes, especially with the last one.

Reviewed on Sep 13, 2021

Great Introduction, may we worth clarifying that for Data Manipulation the script must be saved before entering submit() as you cannot make progress.

Reviewed on Jul 22, 2019

What I liked most about this course was that it gave us a solid foundation on how to program in R and at the same time made us want to learn more about it.

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