# ropensci/skimr

A frictionless, pipeable approach to dealing with summary statistics

Repository: https://github.com/ropensci/skimr
Canonical: https://ross.abutalabs.com/products/skimr
Homepage: https://docs.ropensci.org/skimr
Language: HTML
License Family: other
Topics: unconf17, r, summary-statistics, ropensci, unconf, r-package, rstats, peer-reviewed
Last push: 2026-01-08T23:45:34+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 61, release rhythm 8, longevity 100
- inputs: {"age_days": 3387, "days_push": 237, "days_rel": 402, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1141, forks 81 (observed 2026-08-28T04:03:44.650800+00:00)

## What it is
skimr is an R package providing compact, pipeable summary statistics for data frames, with readable console output including spark-bars and per-type statistics. It returns a skim_df object that integrates with tidyverse pipelines.

## Use cases
- quickly summarize a data frame in R
- get summary statistics beyond base summary()
- explore missing values and distributions per column
- include data summaries in a dplyr pipeline
- profile datasets during exploratory data analysis

## When to choose
- you work in R and want fast, readable data summaries
- you need statistics grouped by column type with missing-value counts
- you want summaries that fit into tidyverse pipes

## When to avoid
- you need full statistical modeling or visualization, not just summaries
- you work outside R

## Facets
- artifact type: library
- maturity: active
- function: data-science, analytics, developer-tools
- domain: data-science, analytics, developer-tools
- platform: python, cli
- tags: r, rstats, summary-statistics, exploratory-data-analysis, ropensci, tidyverse, cran

## Member repositories
- ropensci/skimr (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:44.650800+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:35:24.850373+00:00, confidence not recorded.
  - readme: https://github.com/ropensci/skimr (fetched 2026-08-28T04:03:44.650800+00:00, sha a9275c19eb2e)
  - homepage: https://docs.ropensci.org/skimr (fetched 2026-08-29T12:40:30.855720+00:00, sha 3b47f342ddbc)
  - site_page: https://docs.ropensci.org/skimr/news/index.html (fetched 2026-08-29T12:40:30.861658+00:00, sha 2dc170f56bc2)
  - site_page: https://docs.ropensci.org/skimr/authors.html (fetched 2026-08-29T12:40:30.863913+00:00, sha b5c72c14019b)
  - site_page: https://ropensci.org/about (fetched 2026-08-29T12:40:30.865502+00:00, sha 21e3e45abb74)
  - site_page: https://docs.ropensci.org/ (fetched 2026-08-29T12:40:30.867172+00:00, sha db4231ca5a42)
- Data as of 2026-08-30T08:39:29.467469+00:00.