What you can test here: R
This R page is a scratchpad, not a ceremony. Paste the smallest useful program, run it from the sample area, and keep the output panel close while you make changes.
Runs R via Piston from main.r, using the R version currently available in the runner. That matters because the runtime boundary explains most surprises: packages, files, network access, and server state are different once code leaves a full local project.
We like this page for vectors, data frames in miniature, summaries, plotting-free statistics, and function checks. This page is good for small data-shape questions. For serious analysis, keep a script and data files locally.
The execution loop
The loop is intentionally plain. Write code in the editor above, provide STDIN or arguments if the page exposes those controls, and press Run.
The page packages the source and input, sends them to the configured runner or preview frame, and prints standard output plus errors in the output area. Short path. Useful signal.
When something fails, read the first error before changing the whole snippet; many compiler messages are noisy at the bottom but precise near the top, especially when a missing bracket causes a chain of follow-up complaints.
If the first run succeeds, resist the urge to paste the whole project next. Add one feature, one input case, or one R construct at a time, because a runner like this is best at showing the exact moment a simple idea stops being simple.
Small walkthrough
The saved example below is intentionally left unchanged. Run it once as written, then make a small edit and run it again; that gives you a known-good baseline before you test your own idea.
# Create a sample dataset
scores <- c(85, 92, 78, 95, 88, 76, 91, 83, 87, 94)
# Compute basic statistics
cat("Sample size:", length(scores), "\n")
cat("Mean:", mean(scores), "\n")
cat("Median:", median(scores), "\n")
cat("Std Dev:", sd(scores), "\n")
cat("Min:", min(scores), "\n")
cat("Max:", max(scores), "\n")
# Summary statistics
cat("\nFull summary:\n")
print(summary(scores))The next preserved block belongs to the same example. Keep it nearby when you are comparing input, output, or the shape of the result. Small examples expose mistakes quickly.
Sample size: 10
Mean: 86.9
Median: 87.5
Std Dev: 6.436886
Min: 76
Max: 95
Full summary:
Min. 1st Qu. Median Mean 3rd Qu. Max.
76.00 83.50 87.50 86.90 91.75 95.00After the sample works, try one edge case that exercises the page's limits. Vector recycling is handy until it is not; mismatched lengths can quietly produce output you did not mean. That single change often teaches more than pasting a large program and trying to guess which part failed.
Boundaries
This page is strongest when the problem is small and visible. It is weakest when the problem depends on package installs, large data sets, graphics devices, and reproducible research projects. Two caveats.
- Do not paste secrets, tokens, private URLs, or customer data.
- The environment is intentionally constrained, so a snippet that works here still deserves a local check when compiler flags, packages, server state, or exact versions matter.
- Timeouts are normal for runaway loops. They protect the shared runner.
We deliberately keep the sandbox narrow. That makes the output easier to trust for vectors, data frames in miniature, summaries, plotting-free statistics, and function checks, while making it clear when you have outgrown the page.
One practical test: if you cannot explain the snippet in one sentence, split it. The runner is happiest when each run answers a single question, and you will be happier too when the error message points at one idea instead of a pile of guesses.
Everyday uses
Three uses come up often: checking syntax while reading docs, reducing a bug report to something another person can run, and trying a small variation before editing a larger project.
For R, the best examples are boring in a useful way. They fit on one screen, they name the input, and they show the exact output you expected or the exact error you got.
A good habit is to keep one saved version that passes, then make the risky change in a copy. When the output changes, you know which line caused it; when it does not, you have learned that the bug probably lives in setup, data, or assumptions rather than the syntax itself.
Questions
These answers focus on the runner's boundaries and the language details most likely to trip up a small R example.
How to compile an R program?
R code is usually run, not compiled into a standalone program. Locally, Rscript main.r is a common command-line workflow. R can byte-compile functions internally, and packages may contain compiled C, C++, or Fortran code, but normal learning starts with running scripts and checking the printed results. For example, Rscript main.r is the usual command-line way to run an R script.
Can I install external packages like tidyverse or ggplot2?
Assume package installs are not available here unless the page already loads a library for the demo. That keeps the run predictable and quick. If your example needs a package manager, create a local project and add the dependency there, then use this page only for the small language part. That keeps the example focused on the bug instead of package installation or module resolution.
How do I view plots and charts online?
Plot support depends on the runner, and many online console tools are better at text output than graphics. For this page, print summaries, tables, or calculated values first. If you need charts, test locally in RStudio, Quarto, or a notebook where the graphics device is part of the workflow. For example, Rscript main.r is the usual command-line way to run an R script.
Is my data persistent across sessions?
Do not expect data to persist across sessions. Treat the page as temporary. If you need repeatable work, keep a small data frame creation snippet in your code, such as data.frame(x = 1:3, y = c(2, 4, 8)), so the example rebuilds itself every time. When sharing, include the setup data too, otherwise the next person may not see the same result.
How do I handle missing values (NA) in an online session?
In R, missing values are represented by NA. Many functions return NA if missing values are present unless you set an option such as na.rm = TRUE. For example, mean(c(1, NA, 3)) returns NA, while mean(c(1, NA, 3), na.rm = TRUE) returns 2. For example, Rscript main.r is the usual command-line way to run an R script. R recycles vectors in some operations, which is handy until a mismatched length quietly changes your result.
Reference links
Reference pages are better than folklore when an error message gets specific. Start with official docs, then use tutorials for context once the rule is clear.