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packages-r

R packages

Last reviewed Jun 1, 2026 Content v20260601
Track mode
server_script
Means
Server runner
Reading
~1 min
Level
advanced

This lesson

This lesson teaches R packages: the syntax, patterns, and safety habits you need before advancing in R.

Teams still ship R packages in R codebases—skipping it leaves gaps in debugging and code reviews.

You will apply R packages in contexts like: Research pipelines, Shiny dashboards, and statistical reporting.

Write R in the editor and click Run on server—the dev runner executes with Rscript; use print() or cat() and base R in playground snippets (tidyverse locally; LEARNING_RUNNER_ENABLED=true).

When pointers, structs, and basic control flow from intermediate lessons are familiar.

Packages bundle functions, data, and docs—install from CRAN with install.packages(), load with library(). Use renv locally to pin versions per project.

Install and load (local)

install.packages("dplyr")
library(dplyr)

Namespace

dplyr::filter() calls explicitly without attaching—avoids masking base filter conflicts.

Important interview questions and answers

  1. Q: library vs require?
    A: require returns FALSE if missing; library errors—use require for optional deps.
  2. Q: CRAN vs Bioconductor?
    A: CRAN general; Bioconductor genomics—different install repositories.

Self-check

  1. What installs packages?
  2. Why use package::function()?

Pitfall: Attaching many packages masks functions—prefer dplyr::filter() over blind library().

Interview prep

library vs ::?

package::fn() avoids masking conflicts from attaching many libraries.

Interview tip Lesson completion confidence

Can you explain this lesson in 30 seconds without reading notes?

Not saved yet.

Playground

Runs on the configured server runner (dev: npm run runner with LEARNING_RUNNER_ENABLED=true). Output appears below the editor.

Check yourself

Multiple choice — immediate feedback.

Discussion

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Starter discussion topics

  • library vs require?
  • DESCRIPTION file?

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