Chapter 8 Lab 7: R Markdown and Literate Programming

Objectives:

To understand the concept of literate programming
To integrate code, text, and scientific/mathematical notation in a single document
To customize code chunk behavior (echo, eval, results)
To export a reproducible report to HTML and PDF

Back in Lab 1 you built your first R Markdown notebook. Now that you know a lot more R, it’s time to dig into what makes R Markdown genuinely powerful: literate programming — writing your explanation and your code side-by-side, as one continuous, reproducible document.

8.1 What is literate programming?

Literate programming means the document you write to explain your analysis IS the document that runs your analysis. There’s no separate “script” and “report” that can drift out of sync — they’re the same file.

Question 1

Think back to Lab 1’s electronic notebook. What could go wrong if you kept your code in one file and your write-up in a separate Word document? Give one concrete example.

8.2 Controlling code chunks

Every code chunk can take options that control what happens when you knit:

Option	Effect
`echo=FALSE`	runs the code but hides it from the output (only shows results)
`eval=FALSE`	shows the code but does NOT run it
`include=FALSE`	runs the code but hides both code and output
`results='hide'`	runs the code, shows nothing of the output
`message=FALSE`, `warning=FALSE`	suppress package-loading messages/warnings

## [1] 25

Question 2

Create a code chunk that calculates the mean of c(3,6,9,12) but set echo=FALSE so only the result shows. Knit it and confirm the code is hidden but the answer appears.
Create a second chunk showing a library() call with message=FALSE so the loading message doesn’t clutter your report. Explain why a reader of your report might prefer this.

8.3 Scientific and mathematical notation

R Markdown supports LaTeX math notation, which is essential for writing formulas cleanly in a scientific report. Inline math is wrapped in single dollar signs, block equations in double dollar signs.

Inline: $E = mc^2$ renders as $E = mc^2$

Block:

$$\bar{x} = \frac{1}{n}\sum_{i=1}^{n}x_i$$

renders as:

\[\bar{x} = \frac{1}{n}\sum_{i=1}^{n}x_i\]

Some useful symbols:

Code	Renders
`\alpha`, `\beta`	$\alpha$, $\beta$
`x^2`	$x^2$
`x_i`	$x_i$
`\frac{a}{b}`	$\frac{a}{b}$
`\sqrt{x}`	$\sqrt{x}$
`\sum`	$\sum$

Question 3

Write the formula for sample standard deviation in LaTeX math notation and confirm it renders correctly when knitted:

\[s = \sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(x_i - \bar{x})^2}\]

Write out, in LaTeX notation, the formula you used for a t-test or ANOVA in Lab 6 (you can find the general formula online — cite where you found it)

8.4 Exporting your report

R Markdown can knit to several output formats, controlled by the YAML header at the very top of your .Rmd file:

---
title: "My Lab Report"
author: "Your Name"
date: "2026-09-03"
output: html_document
---

Change output: html_document to output: pdf_document to knit to PDF instead (this requires a LaTeX installation — RStudio will prompt you to install tinytex if needed), or output: word_document for a .docx file.

Question 4

Add a proper YAML header to your notebook with a title, your name, and r Sys.Date() for an automatically updating date
Knit your document to both HTML and PDF (or Word, if PDF gives you trouble). Add a note on any differences you noticed between the two output formats

Code	Renders
`\alpha`, `\beta`	\(\alpha\), \(\beta\)
`x^2`	\(x^2\)
`x_i`	\(x_i\)
`\frac{a}{b}`	\(\frac{a}{b}\)
`\sqrt{x}`	\(\sqrt{x}\)
`\sum`	\(\sum\)