Tables Are Coordinated Vectors, Not Grids

If you think of a data frame as a grid of cells, you will fight R forever.

R does not see tables as rectangles.
It sees them as agreements between vectors.

A data frame is nothing more—and nothing less—than a collection of vectors that:

Rows are convenience.
Columns are truth.

The Illusion of Rows

Open any spreadsheet and your eyes go to rows first.
R refuses this habit.

df <- data.frame(
  species = c("A", "B", "C"),
  weight  = c(12.3, 15.8, 9.4)
)

Ask R what this object really is:

str(df)

You do not see rows.
You see named vectors.

Each column stands on its own, carrying meaning independently.
The table exists only because these vectors agree to align.

When analysts ask:

How do I loop through rows?

They are revealing a mismatch in worldview.

Row-wise logic:

If your logic is row-wise, your structure is wrong.

Fix the structure.
The computation will follow.

This is why the most common R verbs act on columns:

df$weight
mean(df$weight)

No indexing gymnastics.
No ceremony.

A column is already the right shape for analysis.

A valid data frame makes a promise:

Break this contract—by embedding multiple values in a cell, mixing units, or encoding meaning in column names—and R will not stop you.

But your analysis will quietly rot.

R assumes discipline.
It does not enforce it.

R’s table model mirrors how statistics works.

Models do not consume rows.
They consume vectors.

Plots do not reason about cells.
They reason about variables.

The data frame is not a spreadsheet with ambitions.
It is a statistical object with a human-friendly face.

If you treat a data frame like a grid, you will write code that looks busy and says nothing.

If you treat it as coordinated vectors,
analysis becomes alignment instead of effort.