Authors and books

An Open Lab-Notebook Experiment by Arthur Charpentier : https://freakonometrics.hypotheses.org/ / https://freakonometrics.github.io/

Finance, Systematic investissement with R (author name unknow) http://systematicinvestor.github.io/ https://github.com/systematicinvestor

Yihui Xie, J. J. Allaire, Garrett Grolemund “R Markdown: The Definitive Guide”: https://bookdown.org/yihui/rmarkdown/

Yihui Xie, Amber Thomas, Alison Presmanes Hill “Creating Websites with R Markdown” https://bookdown.org/yihui/blogdown/

Yihui Xie bookdown “Authoring Books and Technical Documents with R Markdown”: https://bookdown.org/yihui/bookdown/

The Yoda of Silicon Valley : Donald Knuth, master of algorithms, reflects on 50 years of his opus-in-progress, “The Art of Computer Programming.” : https://www.nytimes.com/2018/12/17/science/donald-knuth-computers-algorithms-programming.html

Introduction to Econometrics with R Christoph Hanck, Martin Arnold, Alexander Gerber and Martin Schmelzer 2018-12-19 : https://www.econometrics-with-r.org/

Cours de C/C++ Christian Casteyde http://casteyde.christian.free.fr/cpp/cours/online/book1.html

R for Data Science by Garrett Grolemund and Hadley Wickham https://r4ds.had.co.nz/

Joseph Larmarange, Introduction à l’analyse d’enquêtes avec R et RStudio http://larmarange.github.io/analyse-R/

Introduction à R et au tidyverse by Julien Barnier https://juba.github.io/tidyverse/index.html

Posts on blog

Who is the greatest finisher in soccer? : https://www.r-bloggers.com/who-is-the-greatest-finisher-in-soccer/

vitae: Dynamic CVs with R Markdown : https://www.r-bloggers.com/vitae-dynamic-cvs-with-r-markdown/

Generating Synthetic Data Sets with ‘synthpop’ in R : https://www.r-bloggers.com/generating-synthetic-data-sets-with-synthpop-in-r/

Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport https://www.r-bloggers.com/easy-time-series-prediction-with-r-a-tutorial-with-air-traffic-data-from-lux-airport/

Shinyfit: Advanced regression modelling in a shiny app https://www.r-bloggers.com/shinyfit-advanced-regression-modelling-in-a-shiny-app/

GARCH and a rudimentary application to Vol Trading https://www.r-bloggers.com/garch-and-a-rudimentary-application-to-vol-trading/

50+ Data Science and Machine Learning Cheat Sheets https://www.kdnuggets.com/2015/07/good-data-science-machine-learning-cheat-sheets.html

Using ggplot2 for functional time series https://www.r-bloggers.com/using-ggplot2-for-functional-time-series/

Visualizing Hurricane Data with Shiny https://www.r-bloggers.com/visualizing-hurricane-data-with-shiny/

Custom JavaScript, CSS and HTML in Shiny https://www.r-bloggers.com/custom-javascript-css-and-html-in-shiny/

Timing the Same Algorithm in R, Python, and C++ https://www.r-bloggers.com/timing-the-same-algorithm-in-r-python-and-c/

An Introduction to Docker for R Users https://www.r-bloggers.com/an-introduction-to-docker-for-r-users/

Dow Jones Stock Market Index (4/4): Trade Volume GARCH Model : https://www.r-bloggers.com/dow-jones-stock-market-index-4-4-trade-volume-garch-model/

Papers with Code: A Fantastic GitHub Resource for Machine Learning https://www.kdnuggets.com/2018/12/papers-with-code-fantastic-github-resource-machine-learning.html / https://github.com/zziz/pwc

r2d3 - R Interface to D3 Visualizations https://blog.rstudio.com/2018/10/05/r2d3-r-interface-to-d3-visualizations/

A shiny Web App from LEGO— truck + trailer By Sebastian Wolf https://www.r-bloggers.com/a-shiny-web-app-from-lego-truck-trailer/ https://github.com/zappingseb/biowarptruck

Lecture slides: Real-World Data Science (Fraud Detection, Customer Churn & Predictive Maintenance) By Dr. Shirin Glander https://www.slideshare.net/ShirinGlander/realworld-data-science-fraud-detection-customer-churn-predictive-maintenance

Survival analisys with R by Joseph Rickert (on R views) https://rviews.rstudio.com/2017/09/25/survival-analysis-with-r/

STHDA : Statistical Tools For High-Throughput Data Analysis http://www.sthda.com/french/

PaperswithCode “The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.” https://paperswithcode.com/