Notebooks Now - Talk materials

Here are the links to the materials I mentioned in my talk at Notebooks Now!:

Papers

• https://github.com/eliocamp/shceof

• https://github.com/eliocamp/asymsam

• https://github.com/ewallace/pseudonuclease_evolution_2020

• I have one paper ( https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245103) where also the main text was written exclusively with markdown (source: https://github.com/cas-bioinf/covid19retrospective/).

• The whole statistical analysis (a supplement) for https://onlinelibrary.wiley.com/doi/10.1002/humu.23862 was also written and rendered in rmarkdown (source: https://github.com/martinmodrak/bbs-metaanalysis-bayes/).

• https://www.tandfonline.com/doi/full/10.1080/26939169.2022.2138645

• http://rmflight.github.io/ccPaper/inst/doc/skin_muscle_preprocessing.html ,

• https://github.com/MoseleyBioinformaticsLab/manuscript.peakCharacterization,

• https://www.mdpi.com/2218-1989/12/6/515,

• https://github.com/moseleybioinformaticslab/manuscript.ICIKendallTau/

• https://www.biorxiv.org/content/10.1101/2022.02.24.481854v2

• https://github.com/bakerdh/FourierStats/tree/master/manuscript

• https://osf.io/e62wu/

Workshops

• https://github.com/eliocamp/reproducibility-with-r/

• These are the #rstats course notebooks that we used to teach R programming at OHSU: https://github.com/jminnier/sph_r_programming

• Webpage: https://psyteachr.github.io/ . All of our #psyteachr materials (UofGlasgow School of Psychology and Neuroscience) are made using #rstats markdown and #bookdown Wide selection of different books - stats teaching, data skills, visualisations, glossary.

Tutorials

• https://rpubs.com/Data4HR/curso-r-para-rrhh-clase-00

• https://rpubs.com/Data4HR/curso-r-rrhh-clase-01

• https://rpubs.com/Data4HR/925696

• We wrote some #rstats tutorials for our 🇫🇷 colleagues https://www.book.utilitr.org/. Source: https://github.com/InseeFrLab/utilitR.

• https://github.com/thoughtfulbloke?tab=repositories I suggest resbaz21dh as an introductory text analysis tutorial as an Rmd, and Road_accident_fatigue which is an Rmd analysis of weekly road crash patterns.

• https://debruine.github.io/lmem_sim/index.html

• https://psyteachr.github.io/ads-v1/reports.html

• http://steveharoz.com/research/comments/sampling.html

• https://statnmap.github.io/teach-package-dev-rmdfirst//stagiaire_complet.html#1

Books

• https://r-spatial.org/book

• https://github.com/bakerdh/ARMbookOUP

Targets

• http://rmflight.github.io/

• http://rmflight.github.io/affyMM/

• https://github.com/rmflight/affyMM

I’m very gratfull with all the people that shared their materials with me!