4 projects for "r markdown" with 1 filter applied:

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  • 1
    RStudio Cheatsheets

    RStudio Cheatsheets

    Curated collection of official cheat sheets for data science tools

    The cheatsheets repository from RStudio is a curated collection of official cheat sheets for R, RStudio, the tidyverse, Shiny, and related data science tools. Each cheat sheet is a single (or double) page PDF that condenses important syntax, functions, workflows, and best practices into a visually organized format ideal for quick reference. The repository contains source files (R Markdown or LaTeX) that generate the cheat sheets, version history, and metadata (title, author, description) for each. ...
    Downloads: 4 This Week
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  • 2
    Jupynium

    Jupynium

    Selenium-automated Jupyter Notebook that is synchronised with NeoVim

    It's just like a markdown live preview, but it's Jupyter Notebook live preview. Jupynium uses Selenium to automate Jupyter Notebook, synchronizing everything you type on Neovim. Never leave Neovim. Switch tabs on the browser as you switch files on Neovim. Note that it doesn't sync from Notebook to Neovim so only modify from Neovim.
    Downloads: 0 This Week
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  • 3
    Data Analysis for the Life Sciences

    Data Analysis for the Life Sciences

    Rmd source files for the HarvardX series PH525x

    This repository holds the R Markdown (.Rmd) source files for the PH525x / HarvardX course series (Data Analysis for the Life Sciences / Genomics) managed by GenomicsClass. It functions as the canonical source for course lab exercises, lecture modules, and reading materials in reproducible format. Students and learners use these R Markdown files to follow along, knit notebooks, run code samples, and complete the lab-based assignments.
    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, and reproducible workflows. ...
    Downloads: 0 This Week
    Last Update:
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