dr-cook:data-visualizer

You can generate publication-ready figures by providing omics, clinical, or network data to template-driven workflows that output runnable ggplot2 or matplotlib code. The tool applies predefined plot templates and color palettes to save high-quality outputs via ggsave or plt.savefig with explicit width, height, and dpi settings.

Yes, you can create volcano plots and Kaplan-Meier curves using ggplot2 or matplotlib. The tool generates code for omics figures like volcano plots, heatmaps, and PCA/UMAP, alongside clinical figures including Kaplan-Meier curves, forest plots, and boxplots.

The best way to automate network visualization is to use template-driven workflows that process network data and generate runnable code. The tool automatically applies consistent saving workflows with explicit width, height, and dpi to produce publication-quality network visuals for pharmacology.

Yes, it works with both R and Python by generating runnable code for ggplot2 and matplotlib. The template-driven workflows support both libraries to produce publication-quality figures, ensuring researchers can use their preferred environment for omics, clinical, and network data.

You ensure publication quality by using a consistent saving workflow with explicit width, height, and dpi settings. The tool enforces these standards through ggsave or plt.savefig commands embedded in the generated code, producing figures ready for journals and preprints.