Free to read ยท Three levels
R Programming: explicit data, reliable functions, and auditable analysis
A complete base-R course covering vector semantics, tables, functions, graphics, statistical interfaces, object behavior, diagnostics, and reproducible analysis.
Start learningIn this course
Learn R as a language with explicit object, type, shape, and evaluation contracts. Eighteen substantial lessons move from scripts and vectors to typed data import, functions, grouping, joins, graphics, probability, model interfaces, S3 methods, conditions, resource ownership, performance reasoning, and auditable result artifacts. Every lesson includes five developed sections, two explained examples, three diagnostic mistakes, and two worked exercises.
All examples use supplied tiny synthetic values, in-memory text, or self-created objects and require only base and recommended R facilities. No extra package, external dataset, service, credential, or model download is used. The review environment has no R or Rscript executable. Every code block is therefore explicitly illustrative with runnable:false and derived expected output; no R parsing, execution, rendering, fitting, serialization, or performance measurement is claimed.
What you will learn
- Predict coercion, recycling, indexing, missingness, and shape behavior.
- Design explicit function, table, key, and category contracts.
- Interpret graphics and statistical interfaces without confusing computation with evidence.
- Explain S3 dispatch, lexical environments, conditions, cleanup, and allocation tradeoffs.
- Assemble a typed synthetic analysis with clear rejection policies, artifacts, and validation boundaries.
Your learning path
Three levels
Language and data foundations
Objects, vectors, indexing, structured data, functions, and typed text.
- Scripts, objects, and session boundaries
- Vectors, types, coercion, and recycling
- Indexing, missingness, and shape preservation
- Lists, data frames, and factors
- Functions, lexical scope, and validation
- Text import, dates, and data contracts
Analysis interfaces and table integrity
Traversal, grouping, joins, graphics, probability, linear models, and inference.
- Iteration, vectorization, and the apply family
- Grouping, reshaping, and joins
- Base graphics and honest scales
- Probability, randomness, and simulation
- Linear models and design matrices
- Tests, intervals, and dependence
Evaluation, ownership, and reproducible results
Formula semantics, object methods, conditions, allocation, portability, and an auditable pipeline.
- Formulas, contrasts, and generalized models
- S3 methods, environments, and evaluation
- Conditions, cleanup, and contract tests
- Profiling, allocation, and algorithmic cost
- Reproducible artifacts and portability
- An auditable analysis pipeline
Who this is for
- Developers and analysts learning the R language and its data-oriented semantics.
- Learners who want to make small R analyses reviewable before adding external packages or production data.
Before you start
- Basic programming ideas including variables, functions, loops, and conditional execution.
- Elementary arithmetic, means, variance, and introductory probability.
- Familiarity with rows, columns, identifiers, units, and simple text tables.