Using YAML and Python in your R Workflow

November 29, 2019

Details

Introduction

Malcolm has been interning with RStudio and working on a package, ymlthis, which will allow you too write YAML code using R. The goal is to make it easier to create the metadata needed by markdown, bookdown, blogdown and other frameworks used in R.

R and Python are both popular languages in Data Science. There is no need to limit yourself to just one. Michael will show us how to incorporate python into your R workflow.

Come early, network, enjoy the food and talks.

Schedule

6:30 - 6:50 Networking 6:50 - 7:00 Welcome & general announcement 7:00 - 7:30 The YAML problem: Writing and working with YAML with ymlthis 7:30 - 8:00 Snakes in the Studio: Adding Python to your R Workflow 8:00 - Raffle 8:00 - 8:30 Networking and Clean-up

Talk 1

Title: The YAML problem: Writing and working with YAML with ymlthis

Speaker: Malcolm Barrett

Abstract

R Markdown is an easy-to-learn tool for reproducible research. Many people, however, bump into problems using YAML, the language that specifies metadata for R Markdown documents. ymlthis makes it easy to write valid YAML by handling the syntax and documenting the majority of YAML fields in one place. ymlthis will help you write metadata for R Markdown, bookdown, blogdown, and more. Let the Shiny add-in create a new R Markdown file for you or work with YAML and R Markdown files directly. ymlthis finally gives you a workflow that is better than trying to find and paste the YAML from the last R Markdown document you used.

Talk 2

Title: Snakes in the Studio: Adding Python to your R Workflow

Speaker: Michael Espero

Abstract

R and Python are among the most widely used programming languages for analysts and data scientists. While it’s great to feel comfortable in one open-source statistical programming language, you may encounter peers who prefer another. This walk-through aims to share some of RStudio’s versatility as we touch on useful commonalities between data science workflows in R and Python with attention to simple approaches to loading, wrangling, visualizing, and modelling publicly available data.

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