What will you learn in The R Programming Environment Course
Grasp the fundamentals of R syntax, console fluency, and how to read data into R.
Understand and apply tidy data principles and use tidyverse tools for data manipulation.
Perform advanced data manipulation: summarizing, filtering, merging, and handling tricky date/time data.
Work with text: text processing, regular expressions, and managing physical memory constraints.
Handle large datasets efficiently and produce tidy datasets from diverse data sources.
Program Overview
Module 1: Basic R Language
⌛ 1 hour
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)
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
Topics: Interactive, choice-based learning via swirl lessons to reinforce core R language concepts
Hands-on: Complete swirl-based assignment and programming exercises (2 × 180 min) to deepen understanding
Module 3: Data Manipulation
⌛ 1 hour
Topics: Summarizing, filtering, merging, and manipulating data in R; working with dates, times, and time zones; using piping, adding/changing columns, spreading/gathering data
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
Topics: Advanced interactive learning applying data manipulation skills via swirl
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
Topics: Text data handling and regular expressions in R (string, regex functions); understanding and managing physical memory usage in R
Hands-on: Readings to practice text manipulation, regex usage, and internal memory management concepts
Module 6: Text Processing… Lesson Choices
⌛ 6 hours
Topics: Interactive swirl-based reinforcement of text processing and memory management techniques
Hands-on: Swirl lessons (assignment + programming) to apply learning in an interactive format
Module 7: Large Datasets
⌛ 5 hours
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)
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
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.
Supports career paths that require reproducible data work, including Data Visualization Specialist, Quantitative Researcher, and Business or Financial Analyst.
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Specification: The R Programming Environment Course
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FAQs
- The course is beginner-friendly with step-by-step lessons.
- No prior coding knowledge is required.
- Uses interactive “swirl” assignments to practice.
- Gradually introduces syntax, data, and text handling.
- Suitable for those new to programming and statistics.
- R is highly specialized for statistical computing.
- Built-in packages make data visualization simple.
- Strong community in academic and research fields.
- Handles data manipulation and large datasets efficiently.
- Python is broader; R is often preferred for pure statistics.
- Teaches strategies for in-memory and out-of-memory data.
- Covers diagnosing memory issues in R.
- Explains handling dates, times, and complex data.
- Includes techniques for text processing and regex.
- Prepares learners for real-world big data tasks.
- Builds foundation for Data Analyst and Data Scientist roles.
- Supports careers in research, finance, and statistics.
- Teaches reproducible workflows using tidyverse tools.
- Strengthens skills for data visualization specialists.
- Acts as a steppingstone for advanced R specializations.
- Hands-on swirl exercises create real coding experience.
- Includes projects on text handling, time series, and merging data.
- Teaches web data ingestion (APIs, JSON, HTML).
- Helps you produce clean, reproducible datasets.
- Portfolio projects highlight applied R programming skills.
