R Descriptive Statistics

How it works

A descriptive summary reports what was observed; it does not automatically generalise to a population. Count valid observations, report missing values and keep the measurement units. R functions often propagate NA unless a removal policy is chosen. This example summarises only finite observed fictional visit counts and makes that selection explicit.

Run this R example

These examples run locally with R, not PHP, Python or JavaScript. The reviewed baseline is R 4.5.2 on Windows x64/UCRT, using only packages shipped with R. This is a tested version, not a newest-release claim. Install your own R from the official R project; an IDE is optional. Save the complete source as descriptive-statistics.R in a separate learning folder and run Rscript --vanilla descriptive-statistics.R. Keep code and fictional fixtures separate from a real project.

Bhylu has no R browser runner. There is no Try in playground link because the existing Python and JavaScript editors do not execute R. No package installation, account, network dataset, Apache, MySQL or PHP setting is needed to run these base-R sources.

The complete download uses a local scope, restores its temporary options and treats unexpected warnings as errors. Examples that handle an expected warning do so narrowly. Random examples change only their R process’s RNG state. File examples own their temporary output; figure examples leave their named PNG in the current learning folder and refuse to overwrite it. Use a new empty folder to rerun a figure.

Download the complete descriptive-statistics.R · Download all source files with hash checks

For the optional collection downloader, save download-examples.mjs in an empty folder and run node download-examples.mjs. Local preview before publishing uses node download-examples.mjs http://127.0.0.1:8000/examples/r/. It refuses an existing bhylu-r-examples folder and does not install or execute R.

Example

local({
  previous_options <- options(warn = 2, digits = 8, stringsAsFactors = FALSE)
  on.exit(options(previous_options), add = TRUE)
  visits <- c(8, 12, NA, 10, 14)
  observed <- visits[!is.na(visits)]
  cat("observed:", length(observed), "missing:", sum(is.na(visits)), "\n")
  cat("range:", min(observed), "to", max(observed), "\n")
  cat("mean:", mean(observed), "\n")
  invisible(NULL)
})

visits <- c(8, 12, NA, 10, 14)
observed <- visits[!is.na(visits)]
cat("observed:", length(observed), "missing:", sum(is.na(visits)), "\n")
cat("range:", min(observed), "to", max(observed), "\n")
cat("mean:", mean(observed), "\n")

Expected result

observed: 4 missing: 1
range: 8 to 14
mean: 11

The mean denominator is four, not the five scheduled records. The minimum and maximum describe the available sample only. For an empty observed set, avoid calling min or max blindly: a report should emit an explicit unknown result or refuse the operation.