Unified ggplot2 interface for visualizing statistical results from popular R packages.
ggfortify is an R package that provides a unified interface for automatically visualizing statistical model outputs using ggplot2. It extends ggplot2's `autoplot()` and `fortify()` functions to work with over 50 model classes from popular R packages like stats, forecast, survival, and cluster, enabling users to create publication-quality plots directly from model objects without manual data wrangling.
R users, data scientists, statisticians, and researchers who regularly build statistical models (regression, time series, clustering, survival analysis) and need efficient, customizable visualization of results within the ggplot2 ecosystem.
Developers choose ggfortify because it drastically reduces the code required to visualize statistical outputs, ensures consistency across plot types, and leverages the full customization power of ggplot2 while providing specialized diagnostics and interactive extensions via autoplotly.
Define fortify and autoplot functions to allow ggplot2 to handle some popular R packages.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Provides a single autoplot() function that works with over 50 statistical model classes from packages like stats and forecast, drastically reducing code complexity for diverse analyses.
Outputs are ggplot2 objects, allowing full customization using familiar ggplot2 syntax and themes, as highlighted in the vignettes for extending plots.
Automatically generates regression diagnostics like residual and QQ plots for lm and glm models, streamlining model validation without manual data extraction.
Visualizes forecasts, decompositions, and ACF/PACF from packages like forecast and stats with minimal code, as demonstrated in the time series vignette.
The supported classes list is fixed and may not include newer or custom statistical packages, requiring manual plotting workarounds for unsupported models.
Relies on ggplot2 and other packages for specific models, increasing package load times and complexity for installations aiming to minimize dependencies.
Deep customization beyond default plots requires proficient ggplot2 knowledge, which can be a steep learning curve for users unfamiliar with ggplot2's extensive syntax.
Maintained by a single contributor since 2015, which might raise risks for long-term support and timely updates with new R versions or package changes.