Next steps

We have barely touched the surface of what R has to offer today. If you want to take your skills to the next level, here are some topics to investigate:

Programming

  • Writing functions.
  • Using if statements.

The Software Carpentry in R course introduces R as a programming language.

Tidying and summarizing data

  • plyr, dplyr, and tidyr packages by Hadley Wickham.
  • magrittr’s %>% operator for chaining together data frame manipulations.

These tools play well with ggplot2, which we saw in the previous chapter.

Statistics

  • Many statistical tests are built in to R.

  • 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 which we will be using to test for differential gene expression.
    • Many R function repurpose the ~ formula syntax for other ways of relating response and explanatory variables.

See “The R Book” by Michael J. Crawley for general reference.

The books “Linear Models with R” and “Extending the Linear Model with R” by Julian J. Faraway cover linear models, with many practical examples.

Bioinformatics

Bioconductor is a collection of bioinformatics related packages, including the popular limma and edgeR packages for RNA-Seq analysis developed at the Walter and Eliza Hall Institute.

Getting help

Talk to the Monash Bioinformatics Platform for help and pointers.

Stackoverflow-style sites are also great for getting help:

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