Using R Efficiently. Felix Andrews, ANU

Transcription

1 Using R Efficiently Felix Andrews, ANU

2 Using R Efficiently R can be a blessing or a curse: a time-waster or a time-saver. Three Styles of Using R 1.Interactive 2.Scripts, functions 3.Documents / Packages Code Management Help and Reference Productivity tips

3 Three Styles of Using R 1. Interactive, line by line 2. Running a script and defining functions 3. Writing a document with embedded code (using Sweave), or writing an R package. In fact these are a continuum. All analysis has an interactive component; Move to higher levels as much as possible.

4 From interactive to a script Philosophy: the source code is real; objects / plots are just transient instantiations of them Original idea of S: Users are Developers For methods or results you want to keep history(100) and copy into a text file.r source( file.r, echo=true) Be careful with interactive tools like edit() and playwith(): when done, update source code.

5 Scripts: functions Don't Repeat Yourself (DRY)*: this invites inconsistencies and errors. Wrap up your code into functions, and make variables into arguments, with default values: addnoise <- function(x, sderr = sd(x)*f,..., f = 0.1) { x + rnorm(x, sd = sderr,...) } addnoise(1:10, f = 0.5) * see The Pragmatic Programmer:

6 Scripts: functions Another example: to copy and paste a data frame to/from Excel (on Windows)* cpy <- function(x,...) write.table(x, file = "clipboard", sep = "\t", col.names = NA,...) pst <- function(..., row.names = 1) read.delim(file = "clipboard", row.names = row.names,...) cpy(iris) # paste into Excel, edit, then copy again iris2 <- pst() * do be aware that data types may change, especially strings / factors.

7 Code management Yes, you need a code editor: Syntax highlighting, indenting, and bracket matching Function index Sending code to R, etc. Some of the common code editors: Tinn-R (Windows) Emacs+ESS (or vi) Eclipse plug-in SciViews-K JGR Yes, you need a Version Control System too

8 Sweave: embedded analysis Write a report with embedded R code; when compiled or weaved, results, figures and tables are generated. If one step of the analysis changes, just run it again to update all the results. This is known as literate programming, or reproducible research, in different contexts. (An Sweave talk is planned)

9 R Packages Packages are collections of functions, data and documentation. Easy to distribute, just one file. R CMD check will do lots of code checking Will run examples in help pages, as a test suite, whenever you compile a new version For Windows: google Rtools installer

10 Help Note, help() is often not very helpful! It is for a specific function: argument types, return values, and examples. Assumes you know which function you need, and often that you know the basic concepts. Finding help, finding packages and functions: General introduction to a package: vignette() demo()

11 A Google Custom Search Results grouped into Introductions (documents) Task Views (edited surveys of subject areas) Support Lists (heaps of questions and answers) Functions Books Related Tools

12 Reference information help(); help.start() for a list of package and functions To do a local search:??normal To remind yourself of just the arguments to a function: args(read.table) Completion! Start typing & press Tab-Tab for: Function / object names Arguments File names (inside quotes ) methods(class = lm ); see also hints pkg

13 Productivity tips Understand indexing: A vector of integers A vector of negative integers A vector of names A vector of logical values Or blank Double square brackets [[.]] (or $Name) to extract one item Indexing can be used for Reordering, Resampling, Repeating, Querying, Replacing, Adding elements, Deleting elements (mylist$foo <- NULL)

14 Productivity tips Use lists, not multiple named objects Use vectorised functions, not loops, where appropriate (high-level loops are fine) Can be clearer / easier to collate results Faster to run in some cases For applying arbitrary functions: lapply, sapply, tapply, aggregate, etc (see also plyr package). Compiled functions for specific tasks, e.g. rowmeans, table, diff, filter, cumsum, match, unique, %*% (matrix multiply),...

15 Productivity: R Startup Put startup code in a file.rprofile in your working directory, home directory, or $R_HOME (installation directory). On Windows you can set the working directory on a shortcut to R (Start in...) ## example of.rprofile options(digits = 5).First <- function() { library(lattice) lattice.options(panel.error = NULL, default.args = list(as.table = TRUE)) utils::timestamp() }

16 Productivity It all comes down to: Use the right tool for the job. How to learn? Experiment, talk to people...