• Kontakt Scripting Manual

In this manual all commands are given in code boxes, where the R code is printed in black, the comment text in blue and the output generated by R in green. All comments/explanations start with the standard comment sign ' #' to prevent them from being interpreted by R as commands.

Kontakt Scripting Manual

This way the content in the code boxes can be pasted with their comment text into the R console to evaluate their utility. Occasionally, several commands are printed on one line and separated by a semicolon ';'. Commands starting with a ' $' sign need to be executed from a Unix or Linux shell. Windows users can simply ignore them. Integrating R with Vim and Tmux Users interested in integrating R with vim and tmux may want to consult the. Finding Help Reference list on R programming (selection)., by Robert Gentleman., by Hadley Wickham., by W. Venables and B.

How do I reduce the number of programs that run in the background in Windows? Closing programs running in the background on your computer can free up system resources. Free Download Aimsun Scripting Manual Programs. Free Download Aimsun Scripting Manual Programs For Single. A platform for running a server- side Java.

Ripley., by John M. Chambers. &, Henrik Bengtsson, Lund University., University of Warwick., Paul Johnsson, University of Kansas., Norm Matloff, UC Davis., Dirk Eddelbuettel tutorial presented at., Gopi Goswami Control Structures Conditional Executions.

Loops The most commonly used loop structures in R are for, while and apply loops. Less common are repeat loops. The break function is used to break out of loops, and next halts the processing of the current iteration and advances the looping index.

For Loop For loops are controlled by a looping vector. In every iteration of the loop one value in the looping vector is assigned to a variable that can be used in the statements of the body of the loop. Usually, the number of loop iterations is defined by the number of values stored in the looping vector and they are processed in the same order as they are stored in the looping vector. Improving Speed Performance of Loops Looping over very large data sets can become slow in R. However, this limitation can be overcome by eliminating certain operations in loops or avoiding loops over the data intensive dimension in an object altogether. The latter can be achieved by performing mainly vector-to-vecor or matrix-to-matrix computations which run often over 100 times faster than the corresponding for or apply loops in R. For this purpose, one can make use of the existing speed-optimized R functions ( e.g.: rowSums, rowMeans, table, tabulate) or one can design custom functions that avoid expensive R loops by using vector- or matrix-based approaches.

Alternatively, one can write programs that will perform all time consuming computations on the C-level. (1) Speed comparison of for loops with an append versus an inject step. ## In the following the colums are named according to their selection in myList myList. Miscellaneous Utilities (1) Batch import and export of many files. In the following example all file names ending with.txt in the current directory are first assigned to a list (the ' $' sign is used to anchor the match to the end of a string). Second, the files are imported one-by-one using a for loop where the original names are assigned to the generated data frames with the assign function. Consult help with?read.table to understand arguments row.names=1 and comment.char = 'A'.

Third, the data frames are exported using their names for file naming and appending the extension.out. $ R CMD BATCH options myscript.R outfile The output file lists the commands from the script file and their outputs.

If no outfile is specified, the name used is that of infile and.Rout is appended to outfile. To stop all the usual R command line information from being written to the outfile, add this as first line to myscript.R file: options(echo=FALSE).

If the command is run like this R CMD BATCH -no-save myscript.R, then nothing will be saved in the.Rdata file which can get often very large. More on this can be found on the help pages: $ R CMD BATCH -help or?BATCH. (2.3) Another alternative for running R programs as silently as possible. ################################# #!/bin/bash cd $PBSOWORKDIR R CMD BATCH -no-save myscript.R ################################# This script doesn't need to have executable permissions.

Use the following qsub command to send this shell script to the Linux cluster from the directory where the R script myscript.R is located. To utilize several CPUs on the Linux cluster, one can divide the input data into several smaller subsets and execute for each subset a separate process from a dedicated directory. Object-Oriented Programming (OOP) R supports two systems for object-oriented programming (OOP). An older S3 system and a more recently introduced S4 system. The latter is more formal, supports multiple inheritance, multiple dispatch and introspection. Many of these features are not available in the older S3 system. In general, the OOP approach taken by R is to separate the class specifications from the specifications of generic functions (function-centric system).

The following introduction is restricted to the S4 system since it is nowadays the preferred OOP method for R. More information about OOP in R can be found in the following introductions:, and. Define S4 Classes (A) Define S4 Classes with setClass and new. Note: this is an optional but very convenient function to get started with a new package. The given example will create a directory named mypackage containing the skeleton of the package for all functions, methods and classes defined in the R script(s) passed on to the codefiles argument. The basic structure of the package directory is.

The package directory will also contain a file named ' Read-and-delete-me' with the following instructions for completing the package:. Edit the help file skeletons in man, possibly combining help files for multiple functions. Edit the exports in NAMESPACE, and add necessary imports. Put any C/C/Fortran code in src.

If you have compiled code, add a useDynLib directive to NAMESPACE. Run R CMD build to build the package tarball. Run R CMD check to check the package tarball. Read for more information.

(B) Once a package skeleton is available one can build the package from the command-line (Linux/OS X). Includes sequence batch import, sub-setting, pattern matching, AA Composition, NEEDLE, PHYLIP, etc. The script ' demonstrates how R can be used as a powerful tool for managing and analyzing large sets of biological sequences. This example also shows how easy it is to integrate R with the project or other external programs. The script provides the following functionality:. Batch sequence import into R data frame. Motif searching with hit statistics.

Analysis of sequence composition. All-against-all sequence comparisons. Generation of phylogenetic trees To demonstrate the utilities of the script, users can simply execute it from R with the following source command.