The R Programming Environment Course Syllabus

Overview: This course provides a comprehensive introduction to the R programming environment, focusing on foundational skills essential for data science. Through a mix of readings, interactive swirl lessons, and hands-on programming exercises, learners gain fluency in R syntax, data manipulation, text processing, and handling large datasets. The course emphasizes practical data workflows using the tidyverse, real-world data challenges, and memory management. With approximately 25 hours of content, it is designed as the first step in the Mastering Software Development in R specialization, ideal for learners seeking a strong, practical foundation in R.

Module 1: Basic R Language

Estimated time: 1 hour

• Basics of R syntax
• Tidy data principles and processes
• Reading tabular data into R
• Accessing web-based data, APIs, and web scraping
• Parsing JSON, XML, and HTML data

Module 2: Basic R Language: Lesson Choices

Estimated time: 6 hours

• Interactive swirl lessons on R fundamentals
• Working with R objects: vectors, matrices, lists
• Practicing tidyverse principles
• Data ingestion methods through hands-on exercises

Module 3: Data Manipulation

Estimated time: 1 hour

• Summarizing and filtering data in R
• Merging and transforming datasets
• Working with dates, times, and time zones
• Using piping and tidyverse tools for data workflows
• Spreading and gathering data

Module 4: Data Manipulation: Lesson Choices

Estimated time: 6 hours

• Interactive swirl-based data manipulation exercises
• Applying dplyr and tidyr functions
• Advanced filtering and joining operations
• Hands-on practice with real-world datasets

Module 5: Text Processing, Regular Expression, & Physical Memory

Estimated time: 1 hour

• Text data handling in R
• Using string and regular expression functions
• Managing physical memory usage in R
• Understanding memory constraints with large objects

Module 6: Large Datasets

Estimated time: 5 hours

• Strategies for in-memory and out-of-memory data processing
• Diagnosing performance issues with large datasets
• Effective problem-solving: how to Google for help
• Practices for asking technical questions in the R community

Prerequisites

• Familiarity with basic computing and file navigation
• Interest in data analysis or programming
• No prior R experience required, but comfort with technology recommended

What You'll Be Able to Do After

• Read and import diverse data types into R, including web and structured formats
• Apply tidy data principles and manipulate data using tidyverse tools
• Process and clean text data using regular expressions
• Handle date/time data and time zones accurately
• Manage memory efficiently and troubleshoot issues with large datasets