Lesson 4 of 22

Functions, Pipes, and Project Workflow

Turn repeated calculations into validated functions and portable, reproducible workflows.

Motivation

Why begin here

Small functions with clear contracts turn one successful calculation into a process another analyst can test and safely modify.

Why this matters

Turn repeated calculations into validated functions and portable, reproducible workflows.

Packages and data

Reproducible setup

Base R 4.1 or newer for the native |> pipe.

Explanation 1

The live starter code is self-contained and uses only runner-compatible functions.

Explanation 2

Book-only outputs are labelled when the separate source dataset or advanced package was not supplied.

Explanation 3

R code, paths, function names, and formulas remain left-to-right in every locale.

Terminology

Core ideas and notation

Function contract

Arguments state inputs; validation enforces assumptions; return value states output.

compound_value(principal, rate, years)

Pure function

Depends only on inputs and does not alter external state.

mean-centering a supplied vector

Reproducibility

Relative paths, fixed seeds, and session metadata explain how results were made.

set.seed(404)

Notation and formulas

Key relationship

future value = principal × (1 + annual rate)^years

Keep full precision in the object and round only for display.

Step 1Define every variable and unit.
Step 2Check assumptions and information timing.
Step 3Calculate, verify, and interpret in context.

Worked example

Worked analysis

Scenario

What is $1,000 worth after ten years at 5% annual compounding?

Variable: Principal, annual decimal rate, and nonnegative horizon.
Model choice: Keep full precision in the object and round only for display.
Calculation: Future value = 1628.895; seeded simulation mean is approximately -0.01050.
Interpretation: The function exposes the compounding model and rejects impossible inputs; the seed reproduces this pseudorandom stream.
Reasonableness check: A fixed seed does not make one simulation representative, and a constant 5% rate is a modelling assumption.

R check

compound_value <- function(principal, annual_rate, years) {
  stopifnot(principal >= 0, annual_rate > -1, years >= 0)
  principal * (1 + annual_rate)^years
}
print(compound_value(1000, 0.05, 10))
set.seed(404)
print(mean(rnorm(1000)))

Hints

Start by naming the question and unit.
Inspect intermediate values before the final result.
Explain what the calculation cannot establish.

The function exposes the compounding model and rejects impossible inputs; the seed reproduces this pseudorandom stream. Limitation: A fixed seed does not make one simulation representative, and a constant 5% rate is a modelling assumption.

Visual

Compounding path

Future value rises over longer horizons under a constant positive rate.

R connection

Live, editable R practice

Run the self-contained starter code in the protected STATLAB R runner. The code prints an auditable result and avoids network or unrestricted file access.

Live R Lab

INTRO-R-CH04: Functions, Pipes, and Project Workflow

Run a self-contained example, verify its output, and explain one limitation for functions, pipes, and project workflow.

Editable R code

Results

Ready to run

Ready to run. Results, errors, tables, plots, and fallback messages will appear here.

State the question, data, and units.

Run and inspect the printed output.

Interpret the result and one limitation.

Common mistake

Do not install packages or change the working directory inside an analysis script.

STATLAB Tip

Give each function one job and test ordinary, boundary, and invalid inputs.

Guided practice

Scenario

Write annualize_volatility(x, periods = 12), validate periods, and explain the result for constant x.

Hints

Reuse a small tested object.
Change one element at a time.
Finish with an interpretation and limitation.

Compare your result with the definition, units, and model assumptions—not only with a target number.

Exercises

Independent practice

Complete these without looking at the selected solutions. More than one defensible program may exist.

Explanation 1

Write a numeric percent converter.

Explanation 2

Rewrite a nested calculation with |>.

Explanation 3

Record sessionInfo() without setwd().

Selected solutions

Check one defensible route

These are compact solution routes. Confirm dimensions, units, and any changed modelling choices.

Explanation 1

to_percent <- function(x) { stopifnot(is.numeric(x)); 100*x }

Explanation 2

x |> mean() |> round(2)

Explanation 3

capture.output(sessionInfo(), file=file.path('outputs','session_info.txt'))

Chapter summary

What to carry forward

Turn repeated calculations into validated functions and portable, reproducible workflows.

Explanation 1

Functions expose reusable rules.

Explanation 2

Pipes clarify linear transformations.

Explanation 3

Pure calculations are easiest to test.

Explanation 4

Seeds and relative paths support reproducibility.

Terminology

Chapter cheat sheet

function()

define a rule

Use in Functions, Pipes, and Project Workflow.

stopifnot()

assert conditions

Use in Functions, Pipes, and Project Workflow.

|>

native pipe

Use in Functions, Pipes, and Project Workflow.

set.seed()

reproduce draws

Use in Functions, Pipes, and Project Workflow.

References and provenance

Safavi (2026), Chapter 4. Student notes: Mohammad Safavi, Ph.D., STATLAB Academy, Version 1.0.

Resource

Download the complete student notes

The authoritative 125-page English PDF accompanies this native lesson.

Download

Reflection

Pause and write

What assumption in functions, pipes, and project workflow most affects the conclusion?

Name the assumption, evidence you would seek, and how the recommendation might change.

Exit check

Chapter knowledge check

Reach 70% to complete the chapter. Explanations appear after submission.

Checkpoint