RTutor

What is RTutor?

RTutor is an AI tool built for anyone learning programming and data science — students, educators, and working professionals. It provides interactive tutorials and hands-on coding exercises covering R programming, data manipulation, statistical analysis, and machine learning. You can practice coding, get instant feedback on your work, and use a library of code snippets and examples. It also supports collaborative learning through discussion forums and project sharing.

Who created RTutor?

RTutor was developed by Dr. Steven Ge of Orditus LLC, who shares updates on Twitter and LinkedIn. Feedback and questions can be sent to [email protected]. Orditus LLC offers RTutor for free to non-profit organizations. Commercial use requires a license, and businesses can also customize and install RTutor locally for their data insights needs at a reasonable cost.

What is RTutor used for?

RTutor helps you learn and work with R and data science:

• Interactive tutorials and coding exercises on R programming, data manipulation, statistical analysis, and machine learning.
• Practice complex concepts directly to sharpen your coding abilities.
• A straightforward interface for writing, running, and testing code in your web browser.
• Automated assessments and instant feedback to track progress and identify areas needing practice.
• A coding assistant for professionals, with intelligent autocomplete and syntax highlighting.
• A library of code snippets and examples for quick reference and troubleshooting.
• Built-in discussion forums and project sharing for collaborative learning.

Who is RTutor for?

• Students building a foundation in programming and data analysis.
• Educators looking for interactive teaching tools for these subjects.
• Professionals in programming and data science who want to enhance their skills or streamline their workflow.

How to use RTutor?

1. For an overview, check out the 8-minute YouTube video.
2. For source code, bug reports, or installation instructions, see GitHub.
3. Start exploring your data in the EDA tab for initial analysis.
4. Begin with simple requests, such as looking at distributions or creating basic plots, then increase complexity as you get comfortable.
5. The system defaults to the GPT-4o model. To adjust a plot, refer back to the previous code chunk.
6. When cleaning and preparing data, work step by step and make sure all data types are correct.
7. Your data automatically loads into R as a data frame named df.
8. Additional files can be uploaded for analysis alongside your main data.