Search Results for "r markdown"

A collection of user-friendly LaTeX CV and résumé templates (packaged within the R Markdown vitae ecosystem), offering simple themes and templates for creating professional CVs without heavy TeX coding. Supports multiple display themes such as classic, modern, sidebar layouts.

Have you always wished Jupyter notebooks were plain text documents? Wished you could edit them in your favorite IDE? And get clear and meaningful diffs when doing version control? Then, Jupytext may well be the tool you’re looking for. Only the notebook inputs (and optionally, the metadata) are included. Text notebooks are well suited for version control. You can also edit or refactor them in an IDE - the .py notebook above is a regular Python file. Text notebooks with a .py or .md extension...

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.

gusty allows you to control your Airflow DAGs, Task Groups, and Tasks with greater ease. gusty manages collections of tasks, represented as any number of YAML, Python, SQL, Jupyter Notebook, or R Markdown files. A directory of task files is instantly rendered into a DAG by passing a file path to gusty's create_dag function. gusty also manages dependencies (within one DAG) and external dependencies (dependencies on tasks in other DAGs) for each task file you define. All you have to do is provide a list of dependencies or external_dependencies inside of a task file, and gusty will automatically set each task's dependencies and create external task sensors for any external dependencies listed. gusty works with both Airflow 1.x and Airflow 2.x, and has even more features, all of which aim to make the creation, management, and iteration of DAGs more fluid, so that you can intuitively design your DAG and build your tasks.

...Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions.