Bioinformatics Tool Chest: R Programming Language
Data
Scientists love data. Call it a character flaw, but most of us can’t get enough. More data, more! But the data alone are just the start. To really be useful, we have to do something with the data. Model. Summarize. Evangelize it. Something. Who hasn’t needed to plot a standard curve? Or find the mean value of a series of numbers? What should you do when you have these questions.
The Problem
Many scientists turn to our friend Excel to solve these problems. It’s easy to work with, and you can even make graphs easily. That isn’t necessarily a good thing, as perfectly nice people make really bad graphs because those fancy 3D features are so tantalizing. Everyone interested in bioinformatics or computational biology needs a tool in their tool chest that can handle:
- statistics
- figure, graph creation
- very large data
The Solution
Look no further friends, your savior has arrive, and its name is R. R is a free, cross-platform, open-source derivitive of the S language. In case you didn’t catch that last part: R is free. You can download R from the nearest mirror to get started.
The Good
- Freely available
- Open-source — can compile it to your needs (OS, cpu, available memory, optimization levels)
- Tons of add on packages
- Scriptable
- Ability to write own functions and packages
- Able to handle large datasets
- Interfaces with compiled languages
- Can save plots as Post-scripts (print quality)
- Extensive tutorials online along with mailing lists and archives for trouble shooting
The Bad
- Command-line interface
- Can be slow reading large files
- Interpreted language (can be slower than compiled code)
- No tech support line
- Steep learning curve for beginners, especially non-programmers
July 3rd, 2008 at 12:08 pm
Hi,
Do you consider R programming more useful than Perl for the average Bioinformaticist?
-Abbas
July 3rd, 2008 at 12:21 pm
For anything that involves stats or figure generation I use R because (1) there are so many packages that already know how to perform statistical tests (2) its easy for me to make my own functions and (3) I can generate postscripts exactly in the form I want using R.
But the bottom-line is that it depends entirely on what you want to do. If I’m doing text file manipulation, I’ll probably use Perl. But if I’m needing to perform an ANOVA on a set of data, I’ll go with R.
July 11th, 2008 at 7:48 pm
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