Chapter 2 Statistics

2.1 Introductory R

The official CRAN ‘*Intro2R’* https://cran.r-project.org/doc/manuals/r-release/R-intro.html,

Wickham and Grolemund’s *‘R4DS’* https://r4ds.had.co.nz/,

Douglas et al.’s ‘*Intro2R’* https://intro2r.com/

2.2 Advanced R

Wickham’s *‘Advanced R’* https://adv-r.hadley.nz/,

Wickham & Bryan’s *‘R Packages’* https://r-pkgs.org/,

Jeroen Janssens’s *‘DS at the CL’* https://www.datascienceatthecommandline.com/1e/,

Other readings can include the RMarkdown and Bookdown readings:

Xie, Dervieux & Riederer’s *‘R Markdown Cookbook’* https://bookdown.org/yihui/rmarkdown-cookbook/ and Xie, Allaire & Grolemund’s R Markdown: ‘*The Definitive Guide’* https://bookdown.org/yihui/rmarkdown/,

Xie’s *‘bookdown’* https://bookdown.org/yihui/bookdown/ & *‘blogdown’* https://bookdown.org/yihui/blogdown/

Lovelace, Nowosad & Muenchow’s *‘Geocomputation in R’* https://geocompr.robinlovelace.net/,

Fay et al.’s *‘Engineering Production-Grade Shiny Apps’* https://engineering-shiny.org/

2.3 Introductory Statistical Programming

I’ve found W. Chang’s *‘Cookbook for R’* http://www.cookbook-r.com/, UCLA’s Intro to R https://stats.oarc.ucla.edu/stat/data/intro_r/intro_r_interactive_flat.html & BU’s Basic Statistical Analysis https://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/R/R-Manual/R-Manual_print.html the best single-page introduction for teaching R for statistics

Once you learn R and want a blend of R and statistical theory, A. Swoeney’s https://antoinesoetewey.com/ excellent ‘Stats and R’ Blogdownhttps://github.com/AntoineSoetewey/statsandr provides a PDF in ‘What statistical test should I do?’ https://statsandr.com/blog/files/overview-statistical-tests-statsandr.pdf that users click the end-node links to follow.

More theory can be found at *Statistics for Biologists* https://www.nature.com/collections/qghhqm and its sub-page *Points of Significance* https://www.nature.com/collections/qghhqm/pointsofsignificance

*Handbook of Statistical Analyses Using R* HSAUR 3rd ed. https://rdrr.io/cran/HSAUR3/ entirely available online as individual chapter PDFs, with the associated HSAUR3 https://cran.r-project.org/web/packages/HSAUR3/index.html package in CRAN with Vignettes and official documentation reference manual https://cran.r-project.org/web/packages/HSAUR3/HSAUR3.pdf

2.4 Intermediate Statistical Programming

Regression: I’ve struggled to find reputable open-sourced pages of regression education, let alone incorporation in R, but the PSU STAT 501 https://online.stat.psu.edu/stat501/ has caught my attention

Ecology statistics with Oksanen’s *vegan* GitHub https://github.com/vegandevs/vegan, rdocumentation https://rdocumentation.org/packages/vegan and CRAN https://cran.r-project.org/web/packages/vegan/index.html

2.5 Advanced Statistical Programming

High dimensional statistics can be learned from Borg & Groenen’s ‘*Modern Multidmensional Scaling’* https://link.springer.com/book/10.1007/0-387-28981-X

2.6 Data Visualization

Data visualization should use ggplot2 from the Tidyverse https://www.tidyverse.org/,

Wickham’s ‘*ggplot2’* https://ggplot2-book.org/,

Wilke’s *‘Fundamentals of Data Visualization’* https://clauswilke.com/dataviz/,

W. Chang’s *‘R Graphics Cookbook’* 2e https://r-graphics.org/, and

**DEFINNITELY** give the ggplot2 extensions gallery https://exts.ggplot2.tidyverse.org/gallery/ a peek, that I most highly recommend Patil’s *ggstatplot* https://github.com/IndrajeetPatil/ggstatsplot/