We had a great discussion about our favorite R books and why do we still use books.
To summarize our discussion, we settled on that although most of us use mainly Stackoverflow and other web ressources to seek information about specific problems and solutions, books are still a great ressource to lean on when we need to go more in depth into a specific topic.
Here are the various books we discussed during our meeting:
By Stefano Allesina and Madlen Wilmes https://press.princeton.edu/titles/13268.html
What Chris says:
- This book is like a toolbox
- Task oriented
- help to choose what tool to use to match the task you need to accomplish
You might also like: Practical computing for biologist: https://practicalcomputing.org
By Kieran Healy http://socviz.co
What Allison says: Tells you everything about why should you be concerned about data visualization and how to best visualize data Even more fun because it is not a coding book ;)
You might also like: ggplot2: Elegant Graphics for Data Analysis https://www.springer.com/us/book/9780387981413
What Ben says: amazing ressources free, online and always up-to-date Especially check out R for Data Science and Rmarkdown books!
By Hadley Wickham https://adv-r.hadley.nz
What Julien says: Best for R users who starts to wonder why R does this? E.g. coercion: Why do I get a number when I use the sum function on TRUE/FALSE values? Then “Foundations” section is a starting point
By John Paul Mueller and Luca Massaron
What Bob says: Give great examples in both Python and R Covers the basics and also Image recognition
By JJ Allaire https://www.manning.com/books/deep-learning-with-r
What Lukazs says: Great intro both to Keras and how to use it from R
You might also like: Allaire’s keynote on tensorFlow: https://www.rstudio.com/resources/videos/machine-learning-with-tensorflow-and-r/
https://www.edx.org/course/statistics-r-harvardx-ph525-1x-1
What Emilio says: Great intro to basic regression analysis Taking these courses help me to combine Psychometric packages with Rmarkdown for reporting purposes;
By Colin Gillespie and Robin Lovelace https://csgillespie.github.io/efficientR/
What Tommy says: a good reference to learn about R efficiency, although I wish there will be more advice on which data structures are the most efficient in R
Thank you to all the participants who joined this session with a special thanks to Chris for hosting the event. Cheris also compile great information about the most liked and cited R books out there: https://cjlortie.github.io/R_books/
Still want to add your favorite book on R to the pile? Here is your chance: https://docs.google.com/forms/d/e/1FAIpQLSeK_RbyBkjXesn78ASdT-JkhcSHzAfO356QY0yOwLWuIOvPpA