Compression
tar -zcvf [path]<compression-filename>.tar.gz [path]<compressed-files>
Decompression
tar -xzvf [path]<compression-filename>.tar.gz
The later command will extract all compressed data and retains the data folder structures.
tar+gz – How2
Parallel Computing with R
R is an open-source programming language and software environment for statistical computing and graphics. The core R installation provides the language interpreter and many statistical and modeling functions. Many statistical analysis tasks in areas such as bioinformatics are computationally very intensive, while lots of them rely on embarrassingly parallel computations. Multiple computers or even multiple processor cores on standard desktop computers, which are widespread nowadays, can easily contribute to faster analyses. However, providing software for parallel or high performance computing (HPC) with R was not a development goal.
Getting started with R
For the first time with R, do not try doing much of things. A successful installation of R, running the program and getting some idea on how the R programming environment looks like are more than enough. Thus, how to develop an R script for a specific problem is not a question at this time. This article is based on these principles.
An introduction to R
R is an open source programming language and software environment for statistical computing and graphics. It is widely used by statisticians and data mining researchers for developing statistical software and data analysis methods. The use of R has increased substantially in recent years.
