R / CORE CONCEPTS
Vectors, factors, matrices, and missing values
Learn how vectors, factors, matrices, and missing values works in R, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
What you will learn
- Explain vectors in R using the correct mental model
- Trace a focused R example and predict its result before execution
- Recognize a boundary case involving factors and handle it deliberately
Understanding Vectors, factors, matrices, and missing values
Vectors, factors, matrices, and missing values belongs to the practical core of R. Start by identifying the values or state involved and the rule that connects the input to the result.
Trace the example one operation at a time. Keep vectors visible in the code rather than hiding it behind an abstraction before the behavior is understood.
Test a normal case and a boundary case. The difference between the prediction and the observed result is the most useful signal for deciding what to review next.
Vectors, factors, matrices, and missing values is a defining part of practical R work. Start by identifying the data or state involved, then trace the operation that changes or interprets it. Pay attention to the rules R applies at this boundary, because those rules explain both the useful behavior and the common failure modes. This lesson keeps the example deliberately small, then connects it to vectorized operators and recycling rules so the ideas form a coherent progression rather than a list of isolated syntax facts.
Worked examples
Vectors, factors, matrices, and missing values example
A focused R example for vectors.
scores <- c(72, 81, 90)
mean(scores)
# Lesson 4: vectors. Change one value and predict the result before running it.Example explained
Line 1Identify where vectors appears in the R example and name the data it operates on.
Line 2Trace the relevant R rule one operation at a time, recording any state, type, or control-flow change.
Line 3Change one input or boundary condition, predict the result, and compare that prediction with the documented outcome.
Important notes
Keep the first vectors example small enough to trace completely.
Use the normal R toolchain or browser workspace to compare the actual result with your prediction.
Common mistakes
Treating vectors as punctuation to memorize instead of a R behavior to reason about.
Ignoring factors until it appears in production data or a larger program.
Try it yourself
Change, predict, then run
Create a small R example that demonstrates vectors. Add a normal case and a boundary case, write down the expected result for each, then explain which R rule produces that result. Lesson 4 should remain small enough to trace without guessing.
Open R workspaceCheck your understanding
What is the best first step when working with vectors?
- Identify the data and predict the result
- Add more abstraction immediately
- Ignore boundary cases
- Memorize punctuation only
Show answer
A clear input, operation, and predicted result create a testable mental model.