Data analysis and visualization with R

Key Points

A brief introduction to R	R is a strong statistical computing environment Thousands of packages for R Use `variable <- value` to assign a value to a variable in order to record it in memory. Objects are created on demand whenever a value is assigned to them. Use `read.table` and `write.table` to import / export data. The function `str` describes the data frame. Use `object[x, y]` to select a single element from a data frame. Use `from:to` to specify a sequence that includes the indices from `from` to `to`. All the indexing and slicing that works on data frames also works on vectors. Use `#` to add comments to programs. Use `mean`, `max`, `min` and `sd` to calculate simple statistics. Use `tapply` to calculate statistics across the groups in a data frame. Use `ggplot` to create both simple and advanced visualizations.
Explore a gene expression data set	Use vignettes to get introduced to a new package. Gene expression analysis edgeR can be quite straight-forward.
Dynamic Reports with knitr	Use knitr to generate reports that combine text, code, and results. Use Markdown to format text. Put code in blocks delimited by triple back quotes followed by `{r}`.

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