What's Next?

Congratulations

You have completed all 16 lessons. You now have a solid foundation in R: variables, vectors, control flow, functions, lists, matrices, data frames, apply functions, and string operations.

That is a real accomplishment. You understand the core building blocks that make R powerful for data analysis.

What to Explore Next

Here are topics to dive deeper into:

  • ggplot2 -- The most popular R visualization package. Learn the grammar of graphics.
  • dplyr -- Data manipulation with filter, select, mutate, summarize, and group_by.
  • tidyr -- Reshape data with pivot_longer and pivot_wider.
  • R Markdown -- Create reproducible reports combining code, output, and narrative.
  • Shiny -- Build interactive web applications entirely in R.
  • Statistical modeling -- Linear models, GLMs, and machine learning with caret or tidymodels.

Build Something

The best way to learn is to build. Some project ideas:

  • An exploratory data analysis -- download a dataset from Kaggle and analyze it with dplyr and ggplot2.
  • A statistical report -- run hypothesis tests and build regression models in R Markdown.
  • A Shiny dashboard -- create an interactive visualization app.
  • A data pipeline -- clean, transform, and analyze a messy real-world dataset.

References

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