How this R track works
- Rscript in the playground — write runnable scripts; the dev runner executes with Rscript. Use
print()orcat()for output—vectors anddata.frame()work in base R. - Statistics and data analysis focus — this track teaches R syntax, data frames, visualization concepts, and reproducible workflows. Compare scripting patterns with the Python track and SQL querying in the Data Science topic.
- Prerequisites — finish Python or general programming basics (variables, functions, control flow). SQL helps when you join R analysis with warehouse queries.
Tidyverse packages (dplyr, ggplot2, readr) and R Markdown run locally via install.packages()—playground lessons keep runnable snippets to base R unless noted.
Install on your device (macOS, Linux, Windows)
Install R for statistics and tidyverse work locally.
macOS
brew install ror CRAN macOS installer.- Optional IDE: RStudio Desktop.
Linux
- Debian/Ubuntu:
sudo apt install -y r-base r-base-dev - Fedora:
sudo dnf install -y R
Windows
- Installer from CRAN Windows.
Verify: R --version and Rscript --version.
Run code on this site (Backend & language playgrounds)
- Clone or open this project locally; copy
.env.exampleto.env. - Ensure
LEARNING_RUNNER_ENABLED=trueandLEARNING_RUNNER_URL=http://127.0.0.1:9999/v1/execute. - Terminal 1:
php artisan serve(orcomposer run devfor Laravel + Vite + runner together). - Terminal 2:
npm run runner— keep it running while you click Run on server.
Install packages locally: install.packages("tidyverse") in R console.
R is a language built for statistics, data analysis, and visualization. Created by statisticians, it centers on vectors, data frames, and reproducible research—making it the default tool in academia, biostatistics, and many analytics teams.
How this track differs from Python and SQL
After the Python track, you know scripting, functions, and data structures. R shares interactive analysis workflows but uses 1-based indexing, vectors everywhere, and data.frame as the tabular workhorse. Compare warehouse querying with the SQL track and broader pipelines in Data Science.
Unlike general-purpose Python, R optimizes for statisticians: built-in modeling, plotting, and package ecosystems like tidyverse install locally with install.packages().
What you will learn
- Syntax: variables, vectors, factors, control flow, and functions
- Data: data frames, subsetting, missing values, dplyr/tidyr concepts (local install)
- Viz: base plotting and ggplot2 grammar (local install)
- Stats: descriptive summaries, probability, hypothesis tests, linear models
- Workflow: R Markdown, packages, reproducibility, Shiny teaser
Playground setup
This topic uses server_script: the runner executes scripts with Rscript. Use print() or cat() for output. Runnable snippets stick to base R—vectors and data.frame()—while tidyverse lessons explain local setup.
Important interview questions and answers
- Q: What is R best known for?
A: Statistical computing, data visualization, and reproducible analysis with rich CRAN packages. - Q: Why learn R after Python?
A: Python excels at general scripting and ML pipelines; R excels at stats-native APIs, academic workflows, and ggplot2—many analysts use both.
Self-check
- In one sentence, what is a data frame in R?
- Where does SQL fit alongside R analysis?
Tip: Finish Python first—R builds on scripting skills with vectors and data.frame at the center.
Interview prep
- What is R in one sentence?
A language for statistical computing and graphics centered on vectors, data frames, and reproducible analysis with CRAN packages.
- Why learn R after Python?
Python teaches general scripting; R adds stats-native modeling, factors, and ggplot2 workflows common in analytics and research.