3 projects for "r markdown" with 2 filters applied:

  • Full-stack observability with actually useful AI | Grafana Cloud Icon
    Full-stack observability with actually useful AI | Grafana Cloud

    Our generous forever free tier includes the full platform, including the AI Assistant, for 3 users with 10k metrics, 50GB logs, and 50GB traces.

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  • Add Two Lines of Code. Get Full APM. Icon
    Add Two Lines of Code. Get Full APM.

    AppSignal installs in minutes and auto-configures dashboards, alerts, and error tracking.

    Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
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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
    Last Update:
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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
    Last Update:
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  • 3
    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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