We have barely touched the surface of what R has to offer. If you want to take your skills to the next level, here are some topics to investigate. However let us make this simple: you need to read “R for Data Science”.
“R for Data Science” by Garrett Grolemund and Hadley Wickham is a good modern introduction to R, and can be read online. This covers use of a collecition of packages called the Tidyverse. The
dplyr package is of particular importance.
Hadley Wickham also has several excellent books covering specific topics online.
See “The R Book” by Michael J. Crawley for general reference.
“Modern Applied Statistics with S” by W.N. Venable and B.D. Ripley is a well respected reference covering R and its predecessor S.
“Linear Models with R” and “Extending the Linear Model with R” by Julian J. Faraway cover linear models, with many practical examples. Linear models, and the linear model formula syntax
~, are core to much of what R has to offer statistically. Many statistical techniques take linear models as their starting point, including
limma for differential gene expression,
glm for logistic regression (etc), survival analysis with
coxph, and mixed models to characterize variation within populations.
- RStudio’s collection of cheat sheets cover newer packages in R.
- An old-school cheat sheet for dinosaurs and people wishing to go deeper.
- Bioconductor cheat sheet
- CRAN has hundreds of contributed packages which can be installed with
- Bioconductor is another huge collection of packages with a biological focus.
Life outside R
Not all data analysis is done in R. The Software Carpentry workshops give a broader introduction to computing in science.
Stackoverflow-style sites are great for getting help:
- support.bioconductor.org for bioconductor related questions.
- biostars.org for general bioinformatics questions.
- stats.stackexchange.com for statistics questions.
- stackoverflow.com for general programming questions.
The Monash Bioinformatics Platform offers:
- Weekly drop in sessions where you can get help with R, or general bioinformatics problems.
- Informal Wednesday afternoon talks, which often relate to R.
- Courses on various topics through the year.
Also, the COMBINE student and early career researcher organization runs Software Carpentry workshops.