The tidyverse, ggplot2, statistical modeling, tidymodels for machine learning, and R Markdown for reproducible reporting. The R skills data scientists and researchers actually use — not base R syntax lectures.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on r programming you can find — even without producing a single minute of custom video.
This course is built by engineers who ship r programming systems for a living. It reflects how these tools actually behave in production — not how the documentation describes them.
Every day includes working code examples you can copy, run, and modify right now. The goal is understanding through doing, not passive reading.
Instead of re-explaining existing documentation, this course links to the definitive open-source implementations and the best reference material on r programming available.
Each day is designed to finish in about an hour of focused reading plus hands-on work. Do the whole course over a week of lunch breaks. No calendar commitment, no live classes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
RStudio setup, vectors and data frames, dplyr for data manipulation (filter, select, mutate, summarize, group_by), piping with |>, and why the tidyverse dialect makes R more readable.
The grammar of graphics, layers, aesthetics, geoms, scales, and facets. Building publication-quality charts from scratch — histograms, scatter plots, line charts, and faceted grids.
Descriptive statistics, hypothesis testing (t-test, ANOVA, chi-square), correlation, linear regression with lm(), model summaries, and the statistical output R produces vs what it actually means.
The tidymodels meta-framework — recipes for preprocessing, parsnip for model interfaces, rsample for cross-validation, yardstick for metrics, and running logistic regression, random forest, and xgboost through the same interface.
R Markdown documents, code chunk options, knitting to HTML and PDF, parameterized reports, version control for R projects with renv, and connecting R outputs to Quarto for modern publishing.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Complete tutorials on dplyr, tidyr, and the pipe — the tidyverse dialect that makes R genuinely readable for data manipulation.
The grammar of graphics applied with ggplot2 — from basic scatter plots to publication-quality faceted charts.
The tidymodels ecosystem for consistent ML workflows in R — preprocessing, model fitting, cross-validation, and evaluation in a unified interface.
Hypothesis testing, regression, and statistical inference in R — with clear explanations of when to use each test.
Reproducible reporting in R — from basic R Markdown documents to parameterized reports and Quarto publishing.
The best way to deepen understanding is to read the canonical open-source implementations. Clone them, trace the code, understand how the concepts in this course get applied in production.
Meta-package and organization for the tidyverse. The individual package repos (dplyr, ggplot2, tidyr) are the best source code references for understanding R data manipulation.
The tidymodels meta-package. The /vignettes directory has the canonical examples for every component of the ML workflow.
The ggplot2 source. Understanding the layer and aes() internals explains why the grammar-of-graphics approach is so flexible.
Quarto is the modern successor to R Markdown — multi-language, multi-format scientific publishing. The examples directory covers every output format.
R remains the dominant language for statistical research. This course covers the modern R workflow that academic and industry researchers actually use.
ggplot2 and dplyr make R the most powerful spreadsheet you've ever used. This course is the fastest path from Excel to publication-quality R analysis.
R's statistical libraries and ggplot2 still lead Python for certain research tasks. This course gives Python practitioners enough R to read and run existing R code.
The 2-day in-person Precision AI Academy bootcamp covers data science and statistical programming in depth — hands-on, with practitioners who build AI systems for a living. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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