What will you learn in The R Programming Environment Course
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Grasp the fundamentals of R syntax, console fluency, and how to read data into R.
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Understand and apply tidy data principles and use tidyverse tools for data manipulation.
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Perform advanced data manipulation: summarizing, filtering, merging, and handling tricky date/time data.
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Work with text: text processing, regular expressions, and managing physical memory constraints.
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Handle large datasets efficiently and produce tidy datasets from diverse data sources.
Program Overview
Module 1: Basic R Language
⌛ 1 hour
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Topics: Basics of R syntax; tidy data principles and processes; reading data into R (including tabular data, web-based data, web APIs, web scraping, parsing JSON/XML/HTML)
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Hands-on: Swirl assignments and readings to practice foundational R concepts (objects, vectors, matrices, lists, tidy verse principles, data ingestion methods)
Module 2: Basic R Language: Lesson Choices
⌛ 6 hours
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Topics: Interactive, choice-based learning via swirl lessons to reinforce core R language concepts
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Hands-on: Complete swirl-based assignment and programming exercises (2 × 180 min) to deepen understanding
Module 3: Data Manipulation
⌛ 1 hour
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Topics: Summarizing, filtering, merging, and manipulating data in R; working with dates, times, and time zones; using piping, adding/changing columns, spreading/gathering data
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Hands-on: Readings to practice key data manipulation workflows using tidyverse tools and date-time handling in R
Module 4: Data Manipulation: Lesson Choices
⌛ 6 hours
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Topics: Advanced interactive learning applying data manipulation skills via swirl
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Hands-on: Swirl-based assignment and programming (2 × 180 min) to reinforce module 3 skills
Module 5: Text Processing, Regular Expression, & Physical Memory
⌛ 1 hour
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Topics: Text data handling and regular expressions in R (string, regex functions); understanding and managing physical memory usage in R
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Hands-on: Readings to practice text manipulation, regex usage, and internal memory management concepts
Module 6: Text Processing… Lesson Choices
⌛ 6 hours
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Topics: Interactive swirl-based reinforcement of text processing and memory management techniques
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Hands-on: Swirl lessons (assignment + programming) to apply learning in an interactive format
Module 7: Large Datasets
⌛ 5 hours
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Topics: Handling large datasets in R—strategies for in-memory and out-of-memory processing; diagnosing issues; how to search for help effectively (“How to Google Your Way Out of a Jam,” asking for help)
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Hands-on: Readings and an assignment (reading and summarizing data) to practice dealing with large datasets and applying problem-solving strategies
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Job Outlook
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Builds a strong foundation for data-intensive roles such as Data Scientist, Data Analyst, Statistician, or Data Engineer, especially in environments leveraging R and tidy workflows.
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Supports career paths that require reproducible data work, including Data Visualization Specialist, Quantitative Researcher, and Business or Financial Analyst.
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Last verified: March 12, 2026
