# R on the Web - List of Links Useful links for people interested in R. ------------------------------------------------------------------------ Online books ---------- First, many websites are listed and hosted on https://bookdown.org/home/archive/. One book rules them all! **[The big book of R](https://www.bigbookofr.com)** lists many many books written in and for R! **-- Data Science --** **[R for data science](https://r4ds.had.co.nz)**: **THE** reference. Must read, must have. **[Introduction to Data Science](https://rafalab.github.io/dsbook/)**: a huge book on many many topics in Data Science, by Rafael Irizarry, from Harvard. Another **[Introduction to Data Science](https://ubc-dsci.github.io/introduction-to-datascience/)**: a great alternative to the preceding monograph. Maybe slightly more accessible. **[Data Science in Education Using R](http://www.datascienceineducation.com)**: Another great book, very didactic with lots of examples. **[Modern R with the tidyverse](https://b-rodrigues.github.io/modern_R/)**: Great resource for beginners, really starts from scratch. **-- Shiny --** **[Mastering Shiny](https://mastering-shiny.org)**: Hadley Wickham's secrets on how to build Shiny apps. **[The Shiny AWS book](https://business-science.github.io/shiny-production-with-aws-book/)**: a great tutorial that covers many topics beyond Shiny and AWS (ex: Docker & Git) **[Outstanding User Interfaces with Shiny](https://divadnojnarg.github.io/outstanding-shiny-ui/)**: An advanced book on Shiny's html/JavaScript capabilities - and how to customize the layout of Shiny apps. **-- Visualization --** **[Fundamentals of Data Visualization](https://serialmentor.com/dataviz/)**: An exhaustive book on visualization tools & tips. **[R graphics cookbook](https://r-graphics.org)**: the way to go if you're starting on ggplot(). **-- Machine Learning --** **[Hands on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)**: a great (the?) reference for ML in R. **[An Introduction to Machine Learning with R](https://lgatto.github.io/IntroMachineLearningWithR/index.html)**: introductory material on ML in R. **-- Statistics --** **[Foundations in statistics](https://bookdown.org/speegled/foundations-of-statistics/)**: an incredibly didactic book on various topics in statistics: warmly recommended! **[Statistical Inference via Data Science](https://moderndive.netlify.com/index.html)**: a book on regressions & hypothesis testing. **[Introduction to Econometrics with R](https://www.econometrics-with-r.org/index.html)**: a great monograph on econometrics with lots of examples. **-- Finance --** **[Machine learning for factor investing](https://www.mlfactor.com)**: a book on quantitative finance with lots of R code + a financial dataset. **[Financial Engineering Analytics](https://bookdown.org/wfoote01/faur/)**: a general purpose book on R & finance. **[Technical Analysis with R](https://bookdown.org/kochiuyu/Technical-Analysis-with-R/)**: a book on technical analysis in finance. **[Tidy Portfolio Management in R](https://bookdown.org/sstoeckl/Tidy_Portfoliomanagement_in_R/)**: Portfolio management with some packages in R (xts, PortfolioAnalytics, etc.). **-- Misc. --** **[Rcpp for everyone](https://teuder.github.io/rcpp4everyone_en/)**: A book dedicated to the integration of C++ in R. Very useful to accelerate simple routines. **[Advanced R](http://adv-r.had.co.nz)**: for well seasoned users. **[Text mining with R](https://www.tidytextmining.com)**: one reference book on the subject. **[Twitter for R programmers](https://www.t4rstats.com)**: very useful for people who want to scrap data from Twitter. **[Twitter for scientists](https://www.t4scientists.com)**: a book for people who want to get better at using Twitter - not R specific. Curated lists of resources / packages ---------- **[Machine Learning](https://github.com/josephmisiti/awesome-machine-learning#r-general-purpose)**: by Joseph Misiti **[List of cool packages I](https://awesome-r.com)**: by awesome R **[List of cool packages II](https://support.rstudio.com/hc/en-us/articles/201057987-Quick-list-of-useful-R-packages)**: by Garrett Grolemund (RStudio) Learning / Pedagogy ---- Bradley Boehmke's courses: **[Beginner](https://github.com/uc-r/Intro-R)**, **[Intermediate](https://github.com/uc-r/Intermediate-R)** and **[Expert](https://github.com/uc-r/Advanced-R)**: great material for all levels! **[Josehp Larmarange's wiki](https://larmarange.github.io/analyse-R/)**: Great overview of R (in French). **[Julien Barnier's intro](https://juba.github.io/tidyverse/index.html)**: Great intro to R and the tidyverse (in French). **[Florian Privé's book/wiki](https://privefl.github.io/advr38book/)**: Good intro to R/RStudio (in French). **[tidyverse snapshot](https://speakerdeck.com/hadley/welcome-to-the-tidyverse)**: Impressive deck by Hadley Wickham. **[More on tidy data](https://jules32.github.io/2016-07-12-Oxford/dplyr_tidyr/#1_tidy_data_overview)**: dplyr & tidyr by Julie Lowndes. **[Data Science in a Box!](https://datasciencebox.org/)**: very large scope course on data science with lots of material. **[Data Science repo](https://github.com/Chris-Engelhardt/data_sci_guide)**: an aggregation of Data Science resources. **[Interactive web-based data visualization with R, plotly, and shiny](https://plotly-r.com/index.html)**: exhaustive book on (online) visualization techniques. **[Yan Holt's intoduction to R tools](https://www.yan-holtz.com/teaching)**: much focused on visualization. **[Krista DeStasio's best practices](https://kdestasio.github.io/post/r_best_practices/)**: tips on project & code organisation. **[Kelly Bodwin's course on statistics with R](https://stat150.blog)**: more data science than pure stats in my opinion... ------------------------------------------------------------------------ Machine learning ---------- **[Keras for R](https://blog.rstudio.com/2017/09/05/keras-for-r/)**: arguably the best solution for neural networks. **[Homepage here](https://keras.rstudio.com)** **[The xgboost package](https://xgboost.readthedocs.io/en/latest/R-package/xgboostPresentation.html)**: one of the best for boosted trees (with lightgbm). **[The caret package](https://www.machinelearningplus.com/machine-learning/caret-package/)**: probably the most complete ML package. **[Book here](https://topepo.github.io/caret/index.html)** **[Erin LeDell's large scale tutorial](https://koalaverse.github.io/machine-learning-in-R/)**: an efficient overview of the main algorithms with lots of code chunks. **[Christoph Molnar's online book](https://christophm.github.io/interpretable-ml-book/)**: a great resource for interpretability of ML models. **[Tidymodels](https://www.r-bloggers.com/tidymodels/)**: a meta package dedicated to: preparing data, assessing models, exporting results, etc. **[Reference here](https://github.com/tidymodels)** ------------------------------------------------------------------------ Visualisation ---------- **[Maps with R I](http://eriqande.github.io/rep-res-web/lectures/making-maps-with-R.html)** **[Maps with R II](https://github.com/Robinlovelace/Creating-maps-in-R)** **[BBC cookbook for ggplot](https://bbc.github.io/rcookbook/)**: recipes to create graphs like those of the BBC data crew! **[Animated plots with gganimate](https://www.data-imaginist.com/2018/what-are-we-plotting-what-are-we-animating/)**: ggplot, only, in motion! Home repo [here](https://github.com/thomasp85/gganimate). Generative art ---------- **[Generative Art with R (1)](https://github.com/cutterkom/generativeart)**: amazing geometric plots. **[Generative Art with R (2)](https://github.com/marcusvolz/mathart)**: amazing plots: the power of ggplot! **[Mandalas in R](https://fronkonstin.com/2018/03/11/mandalas-colored/)**: geometry & color combined. **[Drawing fractals](https://fronkonstin.com/2019/03/27/drrrawing-with-purrr/)**: fronkostin again! **[Fractal flowers](https://github.com/aschinchon/julia-flowers)**: ggplot + colourlovers! **[Clifford attractors](https://fronkonstin.com/2017/11/07/drawing-10-million-points-with-ggplot-clifford-attractors/)**: large scale simulations! **[Tridokus](https://fronkonstin.com/2018/06/01/coloring-sudokus/)**: Coloring sudokus. **[aRt project](https://github.com/will-r-chase/aRt/blob/master/README.md)**: William Chase's monthly productions. ------------------------------------------------------------------------ Text mining ---------- **[Basic text mining in R](https://rstudio-pubs-static.s3.amazonaws.com/265713_cbef910aee7642dc8b62996e38d2825d.html)**: a smooth introduction. **[Scrapping & text mining tripadvisor](https://towardsdatascience.com/scraping-tripadvisor-text-mining-and-sentiment-analysis-for-hotel-reviews-cc4e20aef333)**: sentiment analysis. **[Sentiment from news](https://github.com/aleszu/textanalysis-shiny/blob/master/README.md)**: nice flexible shiny app for sentiment in user-uploaded text. ------------------------------------------------------------------------ Finance ---------- **[Reproducible finance](https://www.reproduciblefinance.com/)**: amazing resources for portfolio management. **[Portfolio volatility shiny app](https://rviews.rstudio.com/2017/08/09/portfolio-volatility-shiny-app/)**: tailor made vol plots. **[The derivmkts pacakge](https://github.com/rmcd1024/derivmkts)**: option pricing with R. ------------------------------------------------------------------------ Miscellaneous ---------- **[R Weekly](https://www.rweekly.org/)**: frequent updates in the R community. **[Send tweets from R](https://www.r-bloggers.com/send-tweets-from-r-a-very-short-walkthrough/)**: one application of the Twitter API. **[Most Starred R Packages on GitHub](https://stevenmortimer.com/most-starred-r-packages-on-github/)**: Packages by popularity. **[reticulate](https://rstudio.github.io/reticulate/)**: combining Python & R. ## Appendix: Links - [[2-Areas/MOCs/R]] - [[R Shiny]] - [[R Package Development Resources List]] - [[Development]] *** *Backlinks:* ```dataview list from [[R on the Web - List of Links]] AND -"Changelog" ```