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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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At this moment I am concerned about which is the best way to explain an algorithm intuitively.
I have try to read some pseudocode an wow it may be complex for some cases (specially for math applications even more than the formulas itself or pure code like in PHP, C++ or Py). I have thought how about describe algorithms from mathematical notation in a way such that a mathematician could understand it and a web developer too.
Do you think it is a good idea ? (IF all the grammars and structure, symbols and modelings of it will be well explained and it is compact)
Example:
Binary Search
It even could help to simplify algorithm complexity if a mathematical analysis is done (I think)
Depends on the algorithm. For me, I know I would have never gotten the concept of trees if I didn't get a visual drawing. Also the concept of nodes, while a drawing is good, actually seeing the data structure written down is better for that case.
It's student to student basis. I personally see that example of the Binary Search as the worst type of example but am sure some math individual would maybe understand that better.
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I am not able to find any definitive answer to what problem Julia solves compared to the two languages, I was told are most commonly used in working with data (data science), R and Python.
I am not asking for any opinions. Please support replies with factual information (sad I need to add this but it seems some people think this topic is opinion based).
Could anyone explain this?
The Julia Programming Language solves the same problems as R and Python. However, it can solve them extremely faster than those mentioned above, as it runs over C code and uses a JIT compiler. See the Julia Benchmark. This and other advantages that can be found at the language site, it's Twitter profiles: Julia Computing and Julia Language.
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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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I have done some searching around the Internet and SO looking for an introduction or analysis of what makes data.table so fast, but I've only found a lot of (very helpful) manuals, no breakdown of what goes into the programming. (I am more or less completely floored that I can't locate a published paper for data.table, not even something from JStatSoft.)
I've had an algorithms class so I know about sorts and linked lists and binary trees and such, but I don't want to make any amateur guesses (especially when I go to explain to academic people why it's a good idea to use it). Can anyone offer a short, topical summary with references? This question references a slide presentation which is cool, but the info comes in pieces (and even the documentation for, say, setkey() doesn't cite a data.table reference, but goes to Wikipedia).
What I am looking for is something that is both not the source code and not a list of Wikipedia topics, but an ideally "official", sourced answer (thus making it canonical, which could help a lot with all the questions orbiting around this topic).
(It would be great if there was a technical paper out there I could cite for this (the citation() for data.table is just the manual, but of course it's not directly relevant to the question as far as SO is concerned.)
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I would like to find a library for parsing and generating text in common lisp. I would like it to be maintained and documented and preferably with a community that could answer questions.
In the past I have used for the same job ANTLR/StringTemplate in Java and Boost.Spirit in C++. Both projects are well documented in my opinion. From what I am reading, Parsec for Haskell would also meet my requirements. But I have not found something similar for LISP. Perhaps lispers parse texts using their own hand-made parsers?
I am aware of this question, but the so-called documentation on smug is a technical report on monadic parsing, with examples in Haskell. The documentation of LispBuilder looks rather short also. Most projects in CLiki seem abandoned.
Which library would you recommend? The grammar I need to parse is not very complicated.
You might have a look at
Cl-yacc
Esrap