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Foundations of Multi-Dimensional Data
AI014 Lesson 3
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In R, the Foundations of Multi-Dimensional Data rest on the principle that higher-order structures are not distinct storage types. Instead, they are atomic vectors or factors augmented by a dimension vector. By applying a dimension attribute using dim(), we transform a linear sequence into a k-way array, mapping a single memory index to a multi-coordinate system.

1. Metadata as Shape

The array() function acts as a constructor that wraps data (arrays, vectors, or factors) into a structure where the dim() attribute dictates how functions interpret the organization of elements.

2. Structural Transformation

The transition from 1D to ND occurs via assignment syntax: dim(z) <- c(3,5,100). This re-indexes the underlying data without changing its values.

data_vectordim(Z) <- c(3,4,2)k-way array (3x4x2)

3. Initializing State

Multi-dimensional structures are often instantiated with placeholders: Z <- array(0, c(3,4,2)) allocates a $3 \times 4 \times 2$ space, organizing 24 elements into a grid.

main.py
TERMINAL bash — 80x24
> Ready. Click "Run" to execute.
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