r — consolidated language guide — DedSec Project

R

Mental model

R performs vectorized statistical transformations; vector recycling, missing values and data-frame semantics demand explicit checks.

Minimal source sample

x <- c(2, 4, NA)
mean(x, na.rm = TRUE)

Experiment to carry out

Compare the mean with and without na.rm and explain how missing observations affect denominators.

A specific failure to distinguish

Automatic vector recycling can yield plausible but wrong results; check lengths and warnings before interpreting statistics.

To evaluate the exercise, record the initial input, predicted behavior and actual observation in a compatible R environment. Add a second run changing only one variable or argument; this makes the comparison attributable to a concrete cause. If the required runtime is not installed, treat the example as unexecuted source analysis and never label an imagined output as a passing test.

R — Expanded practice workbook

Inspect the smallest source

greet <- function(name) paste0("Hello, ", name, "!")
print(greet("Ada"))

Run or compilation context: Rscript hello.R.. Treat this as a baseline: save a copy, confirm the toolchain version and note whether the example merely evaluates a value, prints text, or creates external effects. Production accounts, datasets and hardware must not be used for first attempts.

Work through the actual mechanism

Summarize a data.frame with missing values using mean(x, na.rm=TRUE), group observations by category and test the difference between NA and NaN.

Diagnose the sharp edge

Factors, character vectors and numeric coercion can change sorting/aggregation silently. Distinguish tibble behavior from base data.frame.

R — Practical runnable code lab

How to run the samples

These independent samples use the appropriate language toolchain. Representative command: Rscript {file}. Replace placeholders with the actual filename, keep test data isolated, and do not interpret this as automatic installation.

R — Technical notes and common pitfalls

Core knowledge

1. Vectorization is central, and missing values propagate through many computations.
2. Recycling rules can silently change results; check lengths and dimensions.
3. Factor values represent categorical levels, not merely ordinary character arrays.