Languages are for completing tasks, and each has varying strengths and weaknesses for different tasks. What specifically do you want to be able to do?
If you are a scientist or engineer who needs to quickly and accurately answer questions from quantitative data or perform statistical inference, R is the way to go. It also has a great interactive command line with powerful data visualization tools and plotting functions. The experience of “playing with” and manipulating data to quickly ask questions, and consider the data in different ways directly from the R command line is amazing.
If you want to do web development, I would recommend Python.
If you want a general low level language to better understand how computers work, or to develop very high performance code I recommend C which is practically a portable assembly language.
Don’t waste your time with proprietary languages where without purchasing expensive licenses your code and skill are useless: visual basic, MatLab, etc. unless you’re employed by a company that requires it.
In general once you learn a few programming languages, learning new ones becomes easier and easier. For example, as a person proficient in half a dozen other languages I was able to quickly complete big projects in Python without taking any time to explicitly learn it- just by looking at example code, and referencing digital copies of the O’Reilly Python books whenever necessary.
That’s a learning strategy I highly recommend: don’t waste time just to learn a programming language with tedious examples, just choose a programming project and immediately start learning what you need to know to finish it once step at a time.
Full disclosure, I am biased because I co-authored several of those… but I really do think they’re quite good. They’re oriented primarily towards people that want to do biology/bioinformatics with R.
Do you recommend any of the 3 tutorials/books? The first one sounds good if it would let one kill two birds with one stone: both learn R and learn Bayesian statistics.
Languages are for completing tasks, and each has varying strengths and weaknesses for different tasks. What specifically do you want to be able to do?
If you are a scientist or engineer who needs to quickly and accurately answer questions from quantitative data or perform statistical inference, R is the way to go. It also has a great interactive command line with powerful data visualization tools and plotting functions. The experience of “playing with” and manipulating data to quickly ask questions, and consider the data in different ways directly from the R command line is amazing.
If you want to do web development, I would recommend Python.
If you want a general low level language to better understand how computers work, or to develop very high performance code I recommend C which is practically a portable assembly language.
Don’t waste your time with proprietary languages where without purchasing expensive licenses your code and skill are useless: visual basic, MatLab, etc. unless you’re employed by a company that requires it.
In general once you learn a few programming languages, learning new ones becomes easier and easier. For example, as a person proficient in half a dozen other languages I was able to quickly complete big projects in Python without taking any time to explicitly learn it- just by looking at example code, and referencing digital copies of the O’Reilly Python books whenever necessary.
That’s a learning strategy I highly recommend: don’t waste time just to learn a programming language with tedious examples, just choose a programming project and immediately start learning what you need to know to finish it once step at a time.
I’ve been wanting to learn R. Do you have any reccommendations for tutorials?
I recommend these: Girke Lab R manuals
Full disclosure, I am biased because I co-authored several of those… but I really do think they’re quite good. They’re oriented primarily towards people that want to do biology/bioinformatics with R.
Bayesian content is in the works...
That’s sweet, thanks!
R is severely lacking free tutorials. (As is bayesian stats)
This might be an approach.
http://stackoverflow.com/questions/570029/learning-applied-statistics-with-a-focus-on-r seems like a good starting point.
Doing Bayesian Data Analysis: A Tutorial with R and BUGS
Introduction to Statistical Thinking (With R, Without Calculus)
R Videos
R-Bloggers.com is a central hub (e.g: A blog aggregator) of content collected from bloggers who write about R (in English).
RStudio, a free and open source integrated development environment (IDE) for R.
Do you recommend any of the 3 tutorials/books? The first one sounds good if it would let one kill two birds with one stone: both learn R and learn Bayesian statistics.
Wow, thanks.