Links to interesting stuff
In this section, you will ifnd some links to interesting very useful and interesting website around statistical methodology issues and R programming.
Statistical methodology
https://discourse.datamethods.org/: a forum with discussions about (applied) statistical methology, especially in a medical context. Especially see:
https://www.slideshare.net/MaartenvanSmeden/presentations: Slides from Maarten van Smeden's talks (methodologst)
https://www.andrewheiss.com/teaching/: Andrew Heiss' website
https://osf.io/8q59y/: slides from the Oxford/Berlin Summer School on Open Science
https://catalogofbias.org/: Oxford catalogue of bias
R programming
DataViz
https://krzjoa.github.io/awesome-r-dataviz/#/: Curated resources for Data Visualization, Drawing & Publishing in R.
http://www2.stat.duke.edu/courses/Spring21/sta323.001/slides/lecture/lec_09.html#1: Advanced Visualization Techniques, Duke University Course
http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html: Top 50 ggplot2 Visualizations - The Master List (With Full R Code). (2020)
https://www.andrewheiss.com/teaching/: Andrew Heiss' website with stunning resources for causal inference and dataViz
https://www.cedricscherer.com/: Cedric Scherer's website, giving tips for stunning dataViz
https://bbc.github.io/rcookbook/#how_to_create_bbc_style_graphics: BBC Visual and Data Journalism cookbook for R graphics
Big Data management
https://www2.stat.duke.edu/courses/Spring21/sta323.001/slides/lecture/lec_25.html#1: Bigger than RAM data, Duke University Course
https://www2.stat.duke.edu/courses/Spring21/sta323.001/slides/lecture/lec_26.html#1 https://www2.stat.duke.edu/courses/Spring21/sta323.001/slides/lecture/lec_27.html#1: Spark & sparklyr part I and II, Duke University Course
Tuto
https://www.book.utilitr.org/index.html: [In French] tuto destiné aux agents de l'Insee
Epidemiology in R
https://epirhandbook.com/index.html : Handbook of applied epidemiology in R