Modern Data Summaries and Diagnostic Reports for Statistical Analysis

Provides robust, NA-aware data summaries, variable diagnostics, normality decisions, missingness and outlier checks, and reproducible diagnostic report scaffolding for statistical analysis. DataSum is designed for researchers, professors, scientists, and analysts who need trustworthy first-pass insight into tabular data before modeling or publication.

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

Clean 1.0 API

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
• Citation metadata

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.