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I'm fairly fluid about using different languages for different
things; here are a couple of the less-well-known ones I'm keen on
advocating.
The R projectThe R project is a mature and stable open source implementation of a language for data analysis and statistics called S. It fits more into the category containing Matlab and IDL than other statistical systems like SPSS; it's a complete and well-designed programming language, comfortable for scripts or interactive use.You can read about it properly at the R project website, but here are a couple of the reasons I like it:
I use R for many medical image processing tasks; it's convenient having very high-quality statistical and modelling facilities handy, the language is pleasant to use, and `serious' number crunching code can easily be written in C and hauled dynamically. RubyRuby, despite its widespread use in Japan, is generally seen as a less popular sibling of Python; indeed it's syntactically similar, but it has more consanguinity to Perl and Scheme. Like these languages, it's interpretted and dynamically typed. Other highlights:
It fills many of the same gaps as Perl and Python do: it's great as a web or database scripting language, for GUI or toplevel development, for rapid prototyping and easy deployment. But while aiming at perl's market, it supplies the readability and maintainability perl always lacked. Ruby is slowly become more pervasive; it can be found in Mac OS X installs, it has its own rapidly growing CPAN equivalent (RAA), a mod_ruby for apache, a number of books, and so on. Find out more at the Ruby website. |