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The question is addressed to those who already have experience in programming, graphics 2D or 3D. What is the mathematical background needed for the programming schedules? And how often do you have to apply this knowledge in practice?
Personally, I don't think it matters what language or framework you are using, it comes down to 3 areas you will need to consider from a background knowledge point of view.
Vector Math
Trigonometry
Discrete Algorithms
I'd be tempted to get a good grip on all three of these before you begin. Also, start simple - Vectors and Trig are a lot simpler in 2D, then once you get the hang of it progress to adding 3D.
Good luck, have fun!
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What were the design decisions that led to R having often more than one way of doing things, that have subtle difference? See, for a good example,
https://www.r-bloggers.com/r-na-vs-null/
More more such issues are here, some which are justified, some which are not http://r4stats.com/articles/why-r-is-hard-to-learn/
From a software engineering perspective, having such choices in a language screams for having subtle and hard-to-find bugs in your code (e.g. in Python the whole point of writing "pythonic" code, that avoids ambiguity and is easy to read and consistent in style). So there must be some major advantages of having that. What are they?
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R is a functional programming language. Many for-statements can be replaced by one of the apply-functions. Thus, isn't the for-statement against the functional programming paradigm? Is using for-statements considered bad style, in the sense of functional programming?
Yes, a for loop is against the functional programming paradigm. However, R is not a pure functional programming language. It allows side effects.
There are scenarios where a for loop is appropriate. In particular, if you don't need a return value, but only a side effect such as plotting or exporting files, for loops are more appropriate than *apply functions.
Then there are some tasks that a just easier to solve with a for loop. E.g., if you look at the source of the Reduce function you'll find a for loop.
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Can anyone explain how the functional programming model differs from the procedural or object orientated models.
I cannot conclude a good answer myself.
in my opinion FP is about pure functions (that is functions in a mathematical sense) - which implies referential transparency and, if you continue the thouhgt, immutable data.
This is the biggest difference I see: you don't mutate data - and most other aspects either directly follow from this or from cool type-systems (which are not necessary for a language to be called functional) and the academic nature.
But of course there is far more to it and you can read papers, complete books or just wikipedia about it.
please note that you can dispute the pure property and then things get a lot more fuzzy ... which should not surprise you, as most functional languages in wide to allow for mutation (Clojure, Scala, F#, Ocaml, ...) and there are not many pure ones.
In this case the biggest difference might be the way you abstract things with higher-order-functions (at least functions should be first class citizens - meaning you can pass them around and have them as values).
But overall this question is really opinionated and will very likely be closed as to broad or something - maybe you should ask for details instead of the big picture
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The question is in the Title. Basically I'm looking for an alternative to R.
I've been using R a bit, there are some really good stuff about it (especially data.frame plyr and ggplot), however I really love Haskell and type inference, so I was wondering if using Haskell to do "simple" statistic analysis would be a good choice.
My basic needs are :
read/write CSV
import SQL table
do some basic 'mapReduce' on the data. Which where R is great but I assume Haskell should be equally good.
However my experience with Haskell is everything is fine until you process realworld data. You always encounter performance issue (and soonish) because even though in theory you should write functional code and don't worry about what's the computer is doing, if you don't and don't use the appropriate library and are not an Haskell expert, stuff are damned slow.
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It was hard for me to understand linear models in R. There are a lot of documents for the case, but many of them are technical manuals rather than teaching the concept.
I found this article really simple and instructive, I hope it would be useful for the other people who have the same problem.
Do you have any better suggestion?
You can check here or Linear Mixed Model, its also simple and easy to understand.