Vectors Are Not Containers — They Are the Default

Most languages treat vectors as a convenience.
R treats them as reality.

If you think of a vector as a box holding many values, you will misuse R.
A vector is not a collection—it is the fundamental unit of computation.

There are no scalars in R.
There are only vectors of length one.

This is not a quirk.
It is the axis around which the language turns.

The Scalar Illusion

x <- 5
length(x)

The answer is not zero.
It is not special.
It is one.

This single fact explains why R behaves the way it does when you least expect it.

Consider:

1:5 + 10

Nothing is being looped explicitly.
No instruction says “repeat.”

R assumes that operations apply to entire vectors.

This is not syntactic sugar. It is a declaration of intent:

The analyst thinks in sets, not steps.

You can write loops in R.

for (i in 1:5) {
  print(i + 10)
}

But this is not how R wants to think.

Loops:

They answer how instead of what.

R prefers you to state the transformation, not choreograph the movement.

c(1, 2, 3) + c(10, 20)

This line alarms beginners.
It should.
Recycling is powerful—and dangerous.

It exists because R assumes vector logic by default.
It trusts the analyst to know when lengths align meaningfully.

Silence here is not negligence.
It is expectation.

This is why if() behaves the way it does:

if (c(TRUE, FALSE)) {
  "yes"
}

It fails—not because R is inconsistent, but because if() demands a single logical value.

When you want vector logic, you must say so:

ifelse(c(TRUE, FALSE), "yes", "no")

Explicitness is the price of power.

Vector-first thinking explains:

Once this clicks, R stops feeling slow and starts feeling inevitable.

R does not ask you to tell it how many times to act.
It asks you to tell it what structure to act upon.

Think in vectors. Let loops retire.