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intro-to-r-for-clinicians-rmed2020's Introduction

This is the GitHub repository for the R/Medicine 2020 pre-conference workshop R/Med 101: Intro to R for Clinicians. There is a course website here: https://skadauke.github.io/rmed2020-101-site/

About the instructors

Stephan Kadauke, MD, PhD is an Assistant Professor of Pathology and Lab Medicine at the Children’s Hospital of Philadelphia where he is the Assistant Director of the Cell and Gene Therapy Lab. He leads the Cell and Gene Therapy DataOps group which builds and deploys predictive models and other data products for the care of children who receive bone marrow transplants and other cell therapies.

Joseph Rudolf, MD is the medical director for the automated core laboratory at ARUP Laboratories in Salt Lake City, Utah. He earned his medical degree (2012) from the University of Washington School of Medicine in Seattle, Washington. He completed his residency training in Clinical Pathology (2015) and fellowship in Clinical Informatics (2017) at the Massachusetts General Hospital in Boston, Massachusetts. His clinical and research interests focus on the intersection of informatics and clinical operations including clinical decision support, utilization management, and reporting and analytics. He is also passionate about clinical process improvement and initiatives to support quality and safety.

Amrom Obstfeld MD, PhD, is the Medical Director of the Division of Pathology Informatics directs the Hematology and Coagulation Laboratories at Children’s Hospital of Philadelphia. In this role Amrom leads the development of analytic tools to aid in laboratory quality management, administration, and operation, supports the deployment of new IS infrastructure in pathology and interfaces with other groups throughout the hospital on informatics initiatives. Dr. Obstfeld plays a major role in designing and implementing educational experiences for pathology trainees and faculty at the University of Pennsylvania within the areas of clinical and pathology informatics.

License

All of the material in this GitHub repository is copyrighted under the Creative Commons BY-SA 4.0 copyright to make the material easy to reuse. We encourage you to reuse it and adapt it for your own teaching as you like!

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intro-to-r-for-clinicians-rmed2020's Issues

Slides peer review

Hi @skadauke ! Halfway thru slide review -- here are some notes for you on the first few slideshows (and I will add the second half next):

00- Welcome

  • Slides 2-6: New instructor + TA listings
  • Slide 8: Clarify “Meeting-style” is for tech help, people can indicate they need help and Joy will arrange for that learner to have 1:1 with an available TA
  • Slide 17: Link to new CoC
  • Slide 18: New version of this Your Turn appropriate for this workshop size/format

01 - Introduction

  • Slide 22: Worth mentioning that library("tidyverse”) can be written without quotation marks as library(tidyverse) ? Up to you
  • Slide 30: Consider reversing the order of these bullet points — I think Importing, functions, packages is more logical.
  • Slide 32: Consider a link to cheat sheets and/or more explicit mention (eg titling this slide (R Studio Cheat Sheets are helpful!) and bringing up where to find them: https://rstudio.com/resources/cheatsheets/
  • Slide 36: FWIW, R Markdown now natively supports many other languages including R using knitr language engines. This engine is based on the reticulate package but it may be helpful to present this as “R Markdown supports languages beyond R” and use this for framing: https://rmarkdown.rstudio.com/lesson-5.html

02 - Visualize

  • Slide 10: Consider writing out the three upcoming steps either on this slide or on a new slide. I feel like this would work well as a “recipe” to get learners prepared for what comes next.
  • Slide 15: The script here is a little verbose — consider consolidating language. I’d also consider explaining aesthetics as rules that tell ggplot how to draw on the screen. Aesthetic are things like colors, lengths, x/y placements, etc. The common thread is that they all take data and literally express that data as something visual.
    Also for the visual you use here, consider actually plotting a few of the observations on the plot to the right as a second animation. That would make it very clear!
  • Slides 22-24: You focus on onside/outside aes(), but make sure you clearly describe the different use cases of whether you’re mapping the color from a column/varaible in your dataset (in which case it’s used inside aes() so mapping uses that variable), or whether you wish to set it manually without mapping. In other words, less focus on aes(), more on mapping or no mapping.
  • Slides 39 and 41: Watch out for overspill with the cheat sheets! Off the slide in my view.

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