From first look to reproducible report.

DataSum is an R toolkit for rigorous first-pass data diagnostics. It helps statisticians, researchers, professors, scientists, and students move from a raw data frame to transparent summaries, quality warnings, distribution checks, group comparisons, and reproducible reports.

Release status: GitHub contains the new DataSum 1.0 API. CRAN currently serves the legacy 0.1.1 release, so install from GitHub to use the functions documented below.

Start in 60 seconds

install.packages("remotes")
remotes::install_github("Uzairkhan11w/DataSum")
library(DataSum)
summary <- summarize_data(iris, by = "Species", digits = 3)
profile <- profile_data(iris)
profile$warnings

What DataSum gives you

Capability	What it answers
NA-aware summaries	How much usable data is present in every variable?
Robust statistics	What do median, IQR, MAD, skewness, and excess kurtosis reveal?
Mode handling	Are there tied modes, and how frequent are they?
Outlier diagnostics	Which variables exceed the transparent 1.5 x IQR rule?
Normality diagnostics	Which test ran, what was its p-value, and what does the decision mean?
Grouped profiles	How do variables differ across treatments, classes, or cohorts?
Analyst warnings	Which missingness, duplicate, outlier, or distribution issues need attention?
Reproducible reports	Can the diagnostic record be shared as Quarto HTML, PDF, or DOCX?
Interactive app	Can a non-programmer upload a CSV and explore the same diagnostics?

Clean 1.0 API

Function	Purpose
`summarize_vector()`	One-row diagnostic summary for a single vector
`summarize_data()`	One row per variable, optionally within groups
`profile_data()`	Dataset overview, variable summaries, and warnings
`datasum_report()`	Quarto diagnostic report source and optional rendering
`run_datasum_app()`	Interactive Shiny interface

Try the diagnostics

summarize_vector(
  c(12, 14, 14, 16, NA, 21, 45),
  name = "response_time",
  digits = 2
)
summarize_data(iris, by = "Species", digits = 2)
profile <- profile_data(airquality, digits = 2)
profile$dataset
profile$summary
profile$warnings

Normality output is deliberately cautious. DataSum reports evidence against normality or no evidence against normality; it does not claim that a sample has proven a population distribution.

Launch the app

run_datasum_app()

The Shiny app opens in your browser and provides:

CSV upload
dataset and variable diagnostics
warning tables
numeric histograms and categorical bar charts
downloadable Quarto report source

This launches locally on your computer. A public hosted version is part of the project roadmap.

Create a reproducible report

Create a portable Quarto source file without extra software:

report <- datasum_report(
  iris,
  path = "iris-diagnostic-report.qmd",
  format = "qmd",
  render = FALSE
)

With the optional quarto package and Quarto CLI installed, render directly:

datasum_report(
  iris,
  path = "iris-diagnostic-report.html",
  format = "html",
  render = TRUE
)

The report contains the dataset overview, variable diagnostics, analyst warnings, formula definitions, and interpretation guidance.

Designed for trust

Tested by GitHub Actions on current R releases for Linux, Windows, and macOS
Deterministic tied-mode output
Safe behavior for missing, empty, constant, and non-numeric vectors
Explicit formulas and thresholds
No silent two-decimal rounding
Source, tests, documentation, and roadmap kept in public

Project links

Citation

GitHub displays a Cite this repository button from CITATION.cff. From R, you can also run:

citation("DataSum")

Community

DataSum is being built in public. Bug reports, statistical-method discussions, teaching use cases, documentation improvements, and research workflow ideas are welcome through GitHub Issues.

DataSum is diagnostic software, not a substitute for study design, domain expertise, or model-specific assumption checking.