blogdown

Data Science Hangout | Javier Orraca-Deatcu, Centene | Excel to data science to lead ML engineer

We were joined by Javier Orraca-Deatcu, Lead Machine Learning Engineer at Centene.

Among many topics covered, Javier shared how his background in finance and consulting led to his interest in data science to automate some of his work - and how he helped get other data scientists together in his organization.

(26:31) How did you organize and recruit people for the data science community group at Centene?

So I sort of piggybacked from a general data science community chat that we had at the company. There were several hundred people on this of varying backgrounds and expertise levels so there was a lot of conversation happening. There was already a Python group that was meeting– I think every other month.

So three weeks after I started, I got really excited about the possibility of potentially creating something similar for R users.

1. It started by just trying to figure out who owns that already existing data science chat and see if they could help support the idea of creating an R user group, something to meet once a month or once every two months. At larger companies especially, getting that type of top-level executive stamp of approval and support can go a long way, especially if that individual is part of the already existing IT or data science function.

2. At the time, I created a Blogdown site. For those of you who are familiar with R Markdown, Blogdown is a package that allows you to create static websites and blogs with R Markdown. Now with Quarto you can do the same thing and create websites. I love the syntax of Quarto.

3. We had partnerships with Posit, so we were able to get some people to come in and do workshops as well.

4. We also had reticulate sessions, where it was a co-branded Python & R workshop where we were looking at ways in which we can actually communicate between teams of different languages a lot easier. I had a great experience with it. Everyone was so collaborative and it was such a great way to see the excitement around what you could do with both R and Python.

I think what started as 13 users the first month, jumped to about 100 - 125 monthly users on this monthly meetup.

…And on the journey to machine learning engineer, what was the hardest part? (49:10):

Because of SQL, I had a really good understanding of at least how tabular data could be joined and the different transformations that could be done to these data objects.

I think I would have really struggled without that basic understanding. But having said that, I think the part where I really struggled at first was function writing. Function writing was not intuitive to me.

Basic function writing was but in general, I found it to be very complicated and it took a solid three to six months of practice to feel actually comfortable with it.

Even when I started building Shiny apps– basic Shiny is quite easy but large functions underpin the entirety of a Shiny app. Everything you do within Shiny is effectively writing functions.

The process of learning Shiny and becoming more comfortable with Shiny was very difficult and something that just took a lot of repetitions but it all sort of played together.

While people may think of Shiny more as a frontend type of system, it did make me a much better programmer in the way I thought about actual functions and function writing.

Other things that I found hard, looking back, I’m sort of embarrassed to say this, was reproducibility of machine learning – being able to reproduce a code set and get the exact same predictions every time.

I wasn’t quite sure why this wasn’t working or how to create these fixed views, setting a seed or whatever you need to do to ensure that someone else downstream could replicate your study or analysis and get the exact same findings themselves.

Bryan Shalloway | From summarizing projects to setting tags, uses of parsing R files | RStudio

I’ll walk through a few potential uses of parsing out the functions and packages in projects.

BioC 2022 - Hello, Quarto!

Mine Çetinkaya-Rundel, PhD., Professor of the Practice at Duke University, Data Scientist and Professional Educator at RStudio, Inc., gives her keynote presentation at the Bioconductor Conference 2022. Dr. Cetinkaya presents Quarto, an open-source scientific and technical publishing system built on Pandoc.

Dr. Mine began her presentation by introducing Quarto and her personal experience using and teaching it to others. She then continued by giving an overview of the R Markdown ecosystem which includes packages like xaringa, Distill, Blogdown, Rmarkdown, and more. She explained the use of Quarto with these packages along with some other Quarto highlights. Afterward, Dr. Mine gave her first demo of Quarto which included setting up and sharing some handy features. Dr. Mine also gives another demonstration on publishing with Quarto and an overview of the features of collaboration. Dr. Mine then gives a second demonstration of collaborating and teaching with Quarto. To wrap up the presentation, Dr. Mine shared about reimagining open source and the work done by Openscapes and their mission for open practices accelerating data-driven solutions, and increasing diversity, equity, inclusion, and belonging in science. The presentation ended with a questions and answers session from the audience.

Main Sections

0:00 Introduction 4:29 Quarto! 6:12 Share 10:57 The R Markdown ecosystem 11:59 Quarto highlights 13:40 Demo of Quarto 21:40 Demo Quarto Publishing 22:58 Quarto rundown 25:23 Collaborate 28:43 Demo 2 34:07 Teaching with Quarto 40:39 Reimagine 44:03 Q&A and Resources

More Resources

Isabella Velásquez | Building a Blog with R | RStudio

Building a Blog With R Presented by: Isabella Velásquez

Here are a bunch of resources Isabella shared ⤵️

Yihui Xie | One R Markdown Document, Fourteen Demos | RStudio (2020)

R Markdown is a document format based on the R language and Markdown to intermingle computing with narratives in the same document. With this simple format, you can actually do a lot of things. For example, you can generate reports dynamically (no need to cut-and-paste any results because all results can be dynamically generated from R), write papers and books, create websites, and make presentations. In this talk, I’ll use a single R Markdown document to give demos of the R packages rmarkdown,

Rebecca Barter | Becoming an R blogger | RStudio (2020)

Blogging is an excellent way to learn, improve your communication skills, and gain exposure in the R and data science communities. In this talk, I will discuss how and why I started blogging, and why you should too. I will guide you through choosing topics, writing your blog using RStudio and blogdown, hosting it on netlify, and sharing your blog with the world. This talk is for you if you’ve wanted to start a blog on R, data science, or to showcase your data analyses, but don’t know where to start.

Introduction to Blogdown (R Package) | RStudio Webinar - 2017