- Advanced R book club
- Schedule: https://github.com/waldronlab/data-science-seminar/wiki
- Fridays at 11:00am Eastern Standard Time (we will switch to Eastern Daylight Savings Time starting the week of March 10th)
- Meetings are on Zoom (please join the Google Group to receive link)
- Email list: https://groups.google.com/g/cuny-data-science-book-club
- Calendar ical http
During January 19th - May 2024, we will complete reading the new book Advanced R by Hadley Wickham . See the schedule wiki for the schedule of presentations.
This repository represents the joint effort of the City University of New York Graduate School of Public Health and Health Policy. During active semesters we hold weekly meetings, where a chapter of a book is presented by a developing instructor with a focus on modern applied statistical methodology and using the R language. Our meetings are open to all (see details below), and materials we produce are licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License. We hope you find these materials useful and will join our sessions.
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Leave a comment on the "Welcome to the Advanced R Book Club" Issue to introduce yourself and to let us know your GitHub username.
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Join our Google Group (open membership) and sign up to receive emails by visiting https://groups.google.com/g/cuny-data-science-book-club.
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Install R and RStudio following these instructions. Here is a short video showing how to use RStudio to contribute to this Github repo.
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Sign up for a GitHub account, then introduce yourself on the "Welcome to the Advanced R Book Club" issue of this repository, under Issues. You will then be able to contribute your presentation and/or exercise notes using file upload directly here, or by using git. If you want to use git instead of simple file upload but don't know what that means, follow this tutorial. The process in RStudio is documented here or there is a video here.
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Pick the date or topic that best suits you and reserve it on the presentation schedule wiki, adding your GitHub username to the schedule table.
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Read the required section of the book, and do the associated exercises that you will present.
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Edit the presentation file, using the template provided in the folder corresponding to the textbook name. See more information about making Slidy presentations.
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Commit the presentation to GitHub so that it is available to others. Don't know what that means? The process is documented here or there is a video here.
Past textbooks have included:
- Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber.
- Data Analysis for the Life Sciences by Rafael A Irizarry and Michael I Love (Print Version) (HTML Version)
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath, with supplement by Solomon Kurz and lectures by McElreath.
- The Art of Data Science (https://leanpub.com/artofdatascience or https://bookdown.org/rdpeng/artofdatascience/) by Roger D. Peng and Elizabeth Matsui.
- Causal Inference: What If by Miguel Hernan and Jamie Robins