Clojure Data Analysis Cookbook - download pdf or read online

By Eric Rochester

ISBN-10: 178216264X

ISBN-13: 9781782162643

Utilizing Clojure for facts research and assortment, this booklet will assist you to achieve clean insights and views out of your information with an important choice of sensible, based recipes.
«The Clojure info research Cookbook» offers recipes for each level of the knowledge research procedure. even if scraping info off an internet web page, appearing info mining, or developing graphs for the internet, this booklet has anything for the duty at hand.
Youll how you can collect facts, fresh it up, and rework it into beneficial graphs which could then be analyzed and released to the web. insurance contains complicated issues like processing info simultaneously, using robust statistical ideas like Bayesian modelling, or even information mining algorithms similar to K-means clustering, neural networks, and organization principles.

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ModuleContent]) time-stamp (normalize-date (find-time-stamp div))] (add-statements kb (mapcat (partial data->statements time-stamp) (map get-data (find-data div)))))) That's a mouthful, but now that we can get all the data into a triple store, we just need to pull everything back out and into Incanter. " [kb data-file data-url q col-map] (load-rdf-file kb (File. data-file)) (load-exchange-data kb (URL. data-url)) (to-dataset (map (partial rekey col-map) (query kb q)))) We'll need to do a lot of the setup we've done before.

Now we need a function that takes a result map and converts the variable names in the query into column names in the output dataset. " [coll] (into {} (map (fn [[k v]] [(header-keyword k) v]) coll))) 4. As usual, once all the pieces are in place, the function that ties everything together is short: (defn load-data [k rdf-file q] (load-rdf-file k rdf-file) (to-dataset (map fix-headers (query k q)))) 5. And using this function is just as simple: user=> (load-data t-store (File. net/data/countries/AF#AF "afghani" "2" "AFN" "pul" "Afghan afghani"] … How it works… First, some background: Resource Description Format (RDF) isn't an XML format, although it's often written using XML (there are other formats as well, such as N3 and Turtle).

For each data node, it gets its child nodes and converts them into a series of tag-name/content pairs. The pairs for each data node are converted into a map, and the sequence of maps is converted into an Incanter dataset. There's more… We used a couple of interesting data structures or constructs in this recipe. Both are common in functional programming or Lisp, but neither has made their way into more mainstream programming. We should spend a minute with them. zip/xmlzip. zip/ right. Being a functional programming language, Clojure prefers immutable data structures; and zippers provide an efficient, natural way to navigate and modify a tree-like structure, such as an XML document.

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Clojure Data Analysis Cookbook by Eric Rochester

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