R statistics package
short description of the R statistics package on the LRZ Linux cluster
|R is a statistics package and was developed as a free successor to the S and Splus languages. It is probably a bit harder to learn than other statistics tools but once you are used to the functional programming approach of R it gives you great flexibility. You can accomplish complex tasks with just very few commands and produce publication quality hardcopy output. It also allows you to add functionality and automate processes. R is available on all the most common platforms.|
Availability and starting R
R is available on the LRZ-Linux cluster and can be used interactively
and in batch-mode. For using R interactively log into one of the
interactive nodes and type
module load R
for loading R
for starting R.
Short example (reading data and visualisation)
In most cases you will have some data that you would like to read and
analyse lateron. The most straightforward way for loading data into R is
reading from a text file. The file 'measurements.txt' contains tab separated data
columns (of performance measurements for the Itanium2 processor). (It is
also possible to read data in other formats or reading from a database;
please refer to the R documentation for further information).
Having started R in the directory where your datafile resides you can read the file into a so called data-frame 'measurements' by using the read function:
> measurements <- read.table("measurements.txt", header=TRUE)
It is possible to inspect the contents of the data-frame and all other
data objects by simply entering the respective name at the R
The measurements contain data for different sampling intervals which
are given in one column. It is common that the available data needs to be
grouped by the contents of one column; this can be achieved very
conveniently with using factors; in the following a factor is created
from the sampling interval 'stime':
> stimef <- factor(measurements["stime"][,1])
Then a boxplot containing a separate box for each sampling interval
giving the variability of the measurements for that sampling interval can
be created by:
> plot(measurements["FP_OPS_RETIRED"][,1]/measurements["stime"][,1]/1.0E+9 ~ stimef,
main="variability of 100 samples (5 min. sampling interval)",
xlab="sampling length [s]", ylab = "[GFlop/s]")
Data can be prepared for hardcopy in a variety of formats (like e.g.
postcript, pdf, png,...). For creating a png-file, first set up the
> png("boxplot_GFlop.png", width=600, height=400)
Then you have to perform the plot command(s) that you would like to
have output to the file you entered. Finally switch off the png device
for writing the data and closing the output file:
Now you should find a file 'boxplot_GFlop.png' in the current
The above is only a short example for giving you a feeling what R is like. You can find further information in the references given below.
The first address for further information is the homepage of the R-project. Another useful
source of information might be the Wiki of the R-project.
If you have any questions, suggestions or would be interested in additional packages to be installed on the machines, please feel free to direct an email to lxadmin_AT_lrz-muenchen.de.