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.
The R Programming Environment
This course is part of Mastering Software Development in R Specialization
Instructors: Roger D. Peng, PhD
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There are 7 modules in this course
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
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
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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Johns Hopkins University
Johns Hopkins University
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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 Apr 25, 2021
A thorough course that covers a lot of efficient data manipulation styles within the R environment. I learned a lot of neat tricks that help with quick analysis of large data frames.
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