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.
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.
Basic R Language: Lesson Choices
-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.
Data Manipulation: Lesson Choices
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.
Text Processing, Regular Expression, & Physical Memory: Lesson Choices
-Choice 1: Get credit while using swirl | Choice 2: Get credit by providing a code from swirl
-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.
Roger D. Peng, PhD and Brooke Anderson