Then, like many in the US and other parts of the world experienced, the news came that I would be teaching online for the rest of the semester, my husband would be working from home, and we’d have our elementary school kids home learning, too. At the time, the only tool I had heard of for doing such a thing was blogdown The instructor training had me motivated to finally build a website and that break seemed like a perfect time to do it. R Studio instructor training and was about to be on spring break from my job teaching at Macalester College. My journey with using the distill package to make a website began back in March of 2019 with the fancier, more customizable blogdown package. I hope this might help some of you decide which tools you might use in creating your own website. This is not a how-to tutorial (although I will provide links to resources I used to help me along the way) but rather a reflection on why I made the choices I made.
Along the way, I took some detours using R Markdown to create webpages and a website for a course I teach. Share and manage access to R- and Python-based interactive applications, dashboards, and APIs, all in a single place.This post describes my journey to creating a personal website using the distill package.
Leverage a single infrastructure to launch and manage Jupyter Notebooks, JupyterLab, VSCode and the RStudio IDE, while giving your team easy access to Kubernetes and other resources.
In this webinar, you will learn how RStudio helps organizations tackle these challenges, with a focus on some of the recent additions to our products that have helped deepen the happy relationship between R and Python:Įasily combine R and Python in a single Data Science project using a single IDE. Even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. Data Science leaders and their business partners find it difficult to make key data science content easily discoverable and available for decision-making, while IT Admins and DevOps engineers grapple with how to efficiently support these teams without duplicating infrastructure. Many Data Science teams today leverage both R and Python in their work, but struggle to use them together.
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