An Introduction to R Programming: From Scripting to Programming

The transition from scripting to programming in R marks the shift from executing isolated, interactive statements to building automated, scalable pipelines. This evolution involves organizing data into robust containers like vector, matrix, factor, array, and list to handle complex information architectures.

1. Programmatic Control Flow

Instead of manual repetition, we use logical blocks to manage complexity. R provides structured control flow mechanisms:

Iteration: for (name in expr_1) expr_2, while (condition) expr, and repeat expr.
Conditionals: if (expr_1) expr_2 else expr_3 for logical branching and the vectorized ifelse for element-wise decisions.

# Grouping expressions
{ expr_1 ; ... ; expr_m }

2. Vectorized Thinking & Functional Mapping

Programming in R leverages functional iteration to process data structures without explicit loops. Functions like apply, lapply, sapply, and tapply allow you to map logic over arrays or lists. For example, using split to group a data frame by a factor and then applying a mean, var, or sqrt(sum(x)) calculation significantly reduces code debt and errors.

TERMINAL bash — 80x24

> Ready. Click "Run" to execute.

QUESTION 1

Which structure allows you to group multiple R expressions into a single logical block?

( expr_1 , expr_2 )

{ expr_1 ; ... ; expr_m }

block[ expr_1 ]

list( expr_1 )

✅ Correct!

Braces {} are used to group multiple statements into one expression, often used in function bodies or loops.

❌ Incorrect

R uses curly braces {} to encapsulate a multi-line code block.

QUESTION 2

When would a programmer choose lapply over a standard for loop?

When the data is stored in a matrix only.

To explicitly use lsfit on single vectors.

To apply a function to each element of a list and return a list.

To stop execution immediately via repeat.

✅ Correct!

lapply is the functional way to iterate over lists, returning a result of the same length.

❌ Incorrect

lapply stands for 'list apply' and is part of R's functional programming toolkit.

QUESTION 3

What is the primary advantage of using for (i in 1:length(yc)) in the context of linear regression on multiple sets?

It automatically calculates the mean of xc.

It ensures logic remains concise regardless of the number of datasets.

It converts a list into a factor.

It forces the use of ifelse for plotting.

QUESTION 4

How does ifelse differ from if...else?

ifelse is used only inside repeat loops.

if...else is vectorized, while ifelse is not.

ifelse is vectorized; if...else handles only a single logical value.

There is no difference in R.

✅ Correct!

ifelse can process whole vectors at once, whereas if expects a single TRUE/FALSE.

❌ Incorrect

if is for control flow; ifelse is a vectorized function.

QUESTION 5

Which function would you use to group data by categories before applying a summary statistic like sum?

split

lsfit

abline

sqrt

✅ Correct!

split divides data into groups based on a factor, which can then be processed by the apply family.

❌ Incorrect

split is essential for 'Split-Apply-Combine' workflows in R.