Resources

Best Practices

• Software Carpentry. http://software-carpentry.org

• Data Carpentry. http://www.datacarpentry.org/

• Why are your files and directories a mess? https://www.theverge.com/22684730/students-file-folder-directory-structure-education-gen-z

• Getting your project files in order, Hao Ye’s Notes on Data Organization in Spreadsheets and R. https://uf-repro.github.io/data-organization/notes.html

• Naming Things - by Jenny Bryan http://www2.stat.duke.edu/~rcs46/lectures_2015/01-markdown-git/slides/naming-slides/naming-slides.pdf

• Wilson, G. et al. 2014. Best Practices for Scientific Computing. PLoS Biol 12(1): e1001745. doi:10.1371/journal.pbio.1001745. URL: http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745

• White, E.P. et al. 2013. Nine simple ways to make it easier to (re)use your data. Ideas in Ecology and Evolution. 6(2): 1-10. URL: http://library.queensu.ca/ojs/index.php/IEE/article/view/4608

• Ram, K. 2013. Git can facilitate greater reproducibility and increased transparency in science. Source Code for Biology and Medicine. 8:7. doi:10.1186/1751-0473-8-7 URL: http://www.scfbm.org/content/8/1/7

• A data science blog greared towards beginneers with a mixture of R and python content http://www.dataschool.io/

Statistics

• Jack Weiss’s courses on statistics for ecologists and environmental scientists
  • Statistical Methods in Ecology
  • Statistics for Environmental Science
• Code to accompany A primer of ecological statistics (Gotelli and Ellison 2012)
  • https://harvardforest.fas.harvard.edu/ellison/pubs/primer/datafiles2e
• Datasets to accompany The R book (Jones et al. 2022)
  • https://bcs.wiley.com/he-bcs/Books?action=index&bcsId=12402&itemId=1119634326
• Introduction to Data Exploration and Analysis with R by Michael Mahoney
  • free ebook https://bookdown.org/mikemahoney218/IDEAR/
    • Basic statistics using R https://bookdown.org/mikemahoney218/IDEAR/basic-statistics-using-r.html
• Topics in R Statistical Language (Penn State Open Edu)
  • Statistics in R - Part 1
  • Statistics in R - Part 2
• Jason Fridley’s R based plant ecology course
  • http://plantecology.syr.edu/fridley/bio793/nichenotes_portal.html
• Patrick Breheny courses on statistics for biologists
  • http://myweb.uiowa.edu/pbreheny/teaching.html
• Linear Models with R (free ebook)
  • http://www.maths.bath.ac.uk/~jjf23/LMR/
• Mixed Effects Models and Extensions in Ecology with R by Zurr et al. (free for cofc students)
  • http://link.springer.com.nuncio.cofc.edu/book/10.1007%2F978-0-387-87458-6#?
• Anova
  • https://online.stat.psu.edu/stat485/lesson/welcome-stat-485
  • Type I/II/III:
    • http://goanna.cs.rmit.edu.au/~fscholer/anova.php
  • Contrasts:
    • http://www.clayford.net/statistics/tag/sum-contrasts/
    • http://www.unc.edu/courses/2006spring/ecol/145/001/docs/lectures/lecture30.htm
• Ordination webpage
  • Amazing resource for multivariate approaches for analyzing community ecology data.
  • http://ordination.okstate.edu/
• Anscombe’s quartet
  • https://en.wikipedia.org/wiki/Anscombe%27s_quartet
• Dynamic Ecology blog posts by Brian McGill related to statistics and data.
  • Why AIC appeals to ecologists lowests instincts
  • In praise of exporatory statistics
  • Ecologists need to do a better job of prediction (4 part series)
    • Part I – the insidious evils of ANOVA
    • Part II - partly cloudy and a 20% chance of extinction (or the 6 P’s of good prediction
    • Part III - mechanistic or phenomenological?
    • Part IV - quantifying prediction quality
  • Ten commandments for good data management
  • Why ecology is hard (and fun) – multicausality

R Programming

• Big Book of R: a collection of bookmarked R lessons and demos -https://www.bigbookofr.com/index.html

Basics

• Free manuals and tutorials provided by R users
  • http://cran.r-project.org/other-docs.html
• R programming style guide
  • http://style.tidyverse.org/ by Whickham
  • lintr for cleaning up R code
  • formatR
• Tryr by codeschool
  • Interactive online lesson for learning R basics
  • http://tryr.codeschool.com/
• A fairly comprehensive R reference card
  • http://cran.r-project.org/doc/contrib/Short-refcard.pdf
• Programming with R by Software Carpentry
  • The software carpentry team have some of the best lessons for learning computational tools on the web.
  • http://software-carpentry.org/v5/novice/r/index.html
• Five useful R functions for manipulating data
  • http://rpubs.com/danmirman/Rgroup-part1

Advanced

• Advanced R by Hadley Wickham:
  • One of the best references for taking your R to the next level
  • http://adv-r.had.co.nz/

Graphics

• Colors
  • Color Palettes

Git

• Git and GitHub by Hadley Wickham
  • A very nice overview and step by step instructions for using git and integrating it with Rstudio.
  • http://r-pkgs.had.co.nz/git.html
• git - the simple guide
  • http://rogerdudler.github.io/git-guide/
• Version Control by Software Carpentry
  • http://software-carpentry.org/v5/novice/git/index.html
• Git reference card:
  • http://www.cheat-sheets.org/saved-copy/Git_Quick_Reference.2011-09-04.pdf
• Git quick reference for beginners
  • http://www.dataschool.io/git-quick-reference-for-beginners/
• Git flight rules - for when things go wrong
  • https://github.com/k88hudson/git-flight-rules