R Programming / Analysis interfaces and table integrity

R Programming β€” Analysis interfaces and table integrity

Traversal, grouping, joins, graphics, probability, linear models, and inference.

6 lessons Β· No account required

What you will learn

  • Predict traversal and join output contracts.
  • Connect chart and model interfaces to data meaning.
  • State sampling and randomness assumptions explicitly.

  1. Iteration, vectorization, and the apply family

    Choose a traversal whose input unit, output type, and empty-case behavior are explicit.

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  2. Grouping, reshaping, and joins

    Preserve observation grain while summarizing groups or combining tables by identity.

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  3. Base graphics and honest scales

    Choose plot encodings from the data question and verify the numbers behind the picture.

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  4. Probability, randomness, and simulation

    Distinguish exact enumeration, distribution calculations, and Monte Carlo evidence.

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  5. Linear models and design matrices

    Read a fitted model through its data, formula, numerical rank, residuals, and prediction boundary.

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  6. Tests, intervals, and dependence

    Use R’s inferential interfaces with an explicit estimand, sampling unit, and comparison family.

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