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: DataSum 1.0.0 is available from CRAN, the GitHub v1.0.0 release, and the Zenodo archive.

Start in 60 seconds

install.packages("DataSum")

# Development version:
# 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

• Roadmap
• Contributing guide
• Support
• Security policy
• Code of conduct
• CRAN release checklist
• Changelog
• Zenodo v1.0.0 archive
• Citation metadata

Citation

GitHub displays a Cite this repository button from CITATION.cff.

Preferred citation for the exact v1.0.0 software archive

Khan, Uzair Javid, Immad Ahmad Shah, and Sukhdev Mishra (2026). DataSum: Modern Data Summaries and Diagnostic Reports for Statistical Analysis (Version 1.0.0) [R package]. Zenodo. https://doi.org/10.5281/zenodo.21352624

• Exact version DOI: 10.5281/zenodo.21352624
• All-versions Zenodo DOI: 10.5281/zenodo.21352623
• CRAN package DOI: 10.32614/CRAN.package.DataSum

Use the version DOI for a reproducible scholarly citation and the concept DOI when referring to DataSum across all releases. 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.