Association for Pathology Informatics R Workshop
Welcome!
This introductory R workshop aims to teach participants with little or no programming experience the basics of the R statistical programming language for reproducible laboratory data analytics. R is a freely available programming environment that is aimed squarely at common activities in data analysis including complex data manipulation, statistical analysis, automation, and publication-quality data visualization. We will introduce basic concepts of R programming as well as more generalizable best practices in working with laboratory data.
Before you arrive
Get Ready...
For the best experience, please have the following ready before the workshop begins:
- Preferably two monitors (or two laptops), one for the Zoom conference software, and one in which you will work.
- Install the latest version of Zoom.
- Install the latest version of Google Chrome as some older browsers may not be compatible with the RStudio application).
- We may need to use RStudio Cloud as a backup training environment. Please sign up for a free account here.
Get Set...
- User specific usernames and passwords will be emailed to you shortly before the workshop. Please have these available.
- Calendar invites with Zoom details will be sent prior to the workshop.
- Join the pre-course technology/AV check sessions tentatively schedule for 7/14/2021 at 1pm ET- details are in the pre-course email.
- Please complete the following survey so we can better understand your R experience and what you want out of the course: API R Workshop Participant Survey.
Go!
The workshop is scheduled to begin on 7/15 at 1 pm ET. Please make every effort to log into the Zoom conference 10 - 15 minutes early to allow time to get settled, ensure your computer audio and video is set up, and get RStudio cloud up and running.
Accessing/interacting with the course content
- Course content will be pre-loaded in the RStudio instance and is available for download there as a .zip file.
- The content is available at our course github page and can be downloaded from there as well.
A note on the mechanics and etiquette of the virtual workshop
- Please do not use anyone else's username.
- Please refrain from screen grabbing other user's information, recording the workshop, or otherwise disrupting the flow of the Zoom Meeting.
- Please exercise appropriate Zoom etiquette by muting your microphone during sessions. Breakout sessions will be used for those who request additional one-one-one assistance during exercises.
Installing RStudio onto your own computer
We will be utilizing our cloud based RStudio instance in the workshop. However, in the long term, you will need R and RStudio installed on your own computer in order to work on private data. You can find step by step instructions for installing these on macOS (here) or Windows (here).
Acknowledgments
All of the course instructors have previous experience implementing and executing R workshops at a variety of venues. The workshop we are presenting for the API community is in many ways a product of these past experiences. The workshop also integrates content, best practices, and lessons from a variety of educators in the R community. We would like to specifically acknowledge:
- MSACL Data Science 201, a course produced by Patrick Mathias and several collaborators, presented at the Mass Spectrometry: Applications to the Clinical Lab meeting.
- Stephan Kadauke's R workshop for Pathology trainees and faculty, developed at the Massachusetts General Hospital and the Hospital of the University of Pennsylvania
- Steve Master and Dan Holmes's AACC Introduction to R Workshop
- Data Science in the Tidyverse, a RStudio course with materials posted online
- R for Data Science, the online textbook by Garrett Grolemund and Hadley Wickham, is invaluable in navigating the tidyverse and learning R in general
- Blog posts and documentation by Jenny Bryan helped steer the project content and as well as some discussion about packages
- Amy Willis' Advanced R Course repository as a resource for understanding content in a longer, advanced R course
- Keith Baggerly and Karl Broman's Reproducible Research module at the Summer Institute in Statistics for Big Data - a big thank you to Keith Baggerly for all of his input and guidance!
- Greg Wilson's Teaching Tech Together, which offers practical advice about teaching programming.
- Claus Wilke's Fundamentals of Data Visualization, a compendium of Do's and Don'ts of data visualization.
- Method validation and some other content has been borrowed from the basic R course at AACC
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!