Thinking in Vectors, Not Loops

R doesn’t iterate — it transforms.
That’s the first truth to swallow if you want to stop fighting R and start flowing with it.

The Loop Mindset

Most programmers come to R from Python, C, or Java — languages that live on loops.
You take a list, run a loop, mutate an element, and build results piece by piece.
That mindset works there — but it breaks R’s rhythm.

# Python style
numbers = [2, 3, 5, 7, 11]
squares = []
for n in numbers:
    squares.append(n**2)
print(squares)

In Python, you describe steps.
In R, you describe relations.

The Vector Mindset

A vector is not a loop — it’s a container of intent.
When you square a vector, you’re not saying “for each element, square it.”
You’re saying:

Apply this operation to the entire idea of the data.

numbers <- c(2, 3, 5, 7, 11)
squares <- numbers^2
squares

That’s it.
No iteration, no indexing, no mutation.
Just transformation — clean, readable, fast.

Why This Matters

Vectorization isn’t just about performance.
It’s a mental shift from how to what.

When you think in vectors:

You focus on relationships, not procedures.
You express logic declaratively.
Your code mirrors your reasoning — not your control flow.

That’s why R feels more like math than engineering.
You’re describing a system of transformations, not writing machinery.

Building Intuition

Let’s stretch it a bit.

temps <- c(29.4, 30.1, 31.6, 33.2, 32.0)
above_31 <- temps > 31
temps[above_31]

This tiny example carries big weight.
You’ve just filtered a dataset using logic as a vector — not a condition inside a loop.
This is R’s quiet genius: logical, numeric, and character vectors are all the same kind of object, and you can use one to filter another.

You can’t think that way in most languages — but in R, it’s the natural order.

The Tidyverse Connection

The entire tidyverse rests on this principle.
Functions like mutate(), filter(), summarize() — they’re all vectorized transformations.
Each one describes what should happen to columns of data, not how to iterate through them.

library(dplyr)

data.frame(x = 1:5, y = 6:10) |>
  mutate(sum_xy = x + y)

Every column is a vector.
Every operation is a transformation.
That’s why tidyverse code reads like poetry — concise, declarative, expressive.

Exercises

Create a vector of 20 random integers using sample(1:100, 20) and compute their cube roots without using loops.
Given scores <- c(85, 92, 77, 63, 95, 88), write one line to return all scores ≥ 90.
In your own data analysis scripts, find one for loop you can rewrite as a vectorized expression.

When you think in vectors, you stop commanding data — you start conversing with it.