Showing 4 open source projects for "software projects"

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  • 1
    renv

    renv

    renv: Project environments for R

    renv is an R dependency management toolkit that enables project-level library isolation and reproducibility. It tracks package versions in a lockfile and can restore exact library states across machines or over time, making R projects portable and consistent.
    Downloads: 0 This Week
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  • 2
    ggthemes

    ggthemes

    Additional themes, scales, and geoms for ggplot2

    ggthemes is an R package that provides extra themes, scales, and geoms for ggplot2. It supplements the default ggplot2 offerings by allowing users to apply special themes (e.g., inspired by classic publications, external visualization styles), additional scale functions, and specialized geoms or color scales. It is often used to make ggplot2 plots adhere to aesthetic styles from famous news outlets, scientific journals, or presentation decks. Additional color scales and palettes for discrete...
    Downloads: 0 This Week
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  • 3
    box

    box

    Write reusable, composable and modular R code

    box is an R package providing a modular system / module loader for organizing reusable R code outside of full packages. It allows users to treat R scripts (files/folders) as modules — possibly nested — with explicit exports, imports, and scoping. The idea is to let users structure code in a more modular, composable way, without needing every reusable component to be a full CRAN-style package. It also provides a cleaner syntax for importing functions or modules (via box::use) that allows...
    Downloads: 0 This Week
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  • 4
    DataScienceR

    DataScienceR

    a curated list of R tutorials for Data Science, NLP

    The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting,...
    Downloads: 0 This Week
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