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|Additional Physical Format:||Print version:
Clojure data analysis cookbook.
Birmingham : Packt Pub., 2013
|Material Type:||Document, Internet resource|
|Document Type:||Internet Resource, Computer File|
|All Authors / Contributors:||
|ISBN:||9781782162650 1782162658 9781680154160 1680154168|
|Description:||1 online resource (iv, 329 pages) : illustrations|
|Contents:||Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Importing Data for Analysis; Introduction; Creating a new project; Reading CSV data into Incanter datasets; Reading JSON data into Incanter datasets; Reading data from Excel with Incanter; Reading data from JDBC databases; Reading XML data into Incanter datasets; Scraping data from tables in web pages; Scraping textual data from web pages; Reading RDF data; Reading RDF data with SPARQL; Aggregating data from different formats; Chapter 2: Cleaning and Validating Data. IntroductionCleaning data with regular expressions; Maintaining consistency with synonym maps; Identifying and removing duplicate data; Normalizing numbers; Rescaling values; Normalizing dates and times; Lazily processing very large data sets; Sampling from very large data sets; Fixing spelling errors; Parsing custom data formats; Validating data with Valip; Chapter 3: Managing Complexity with Concurrent Programming; Introduction; Managing program complexity with STM; Managing program complexity with agents; Getting better performance with commute; Combining agents and STM. Maintaining consistency with ensureIntroducing safe side effects into the STM; Maintaining data consistency with validators; Tracking processing with watchers; Debugging concurrent programs with watchers; Recovering from errors in agents; Managing input with sized queues; Chapter 4: Improving Performance with Parallel Programming; Introduction; Parallelizing processing with pmap; Parallelizing processing with Incanter; Partitioning Monte Carlo simulations for better pmap performance; Finding the optimal partition size with simulated annealing; Parallelizing with reducers. Generating online summary statistics with reducersHarnessing your GPU with OpenCL and Calx; Using type hints; Benchmarking with Criterium; Chapter 5: Distributed Data Processing with Cascalog; Introduction; Distributed processing with Cascalog and Hadoop; Querying data with Cascalog; Distributing data with Apache HDFS; Parsing CSV files with Cascalog; Complex queries with Cascalog; Aggregating data with Cascalog; Defining new Cascalog operators; Composing Cascalog queries; Handling errors in Cascalog workflows; Transforming data with Cascalog. Executing Cascalog queries in the Cloud with PalletChapter 6: Working with Incanter Datasets; Introduction; Loading Incanter's sample datasets; Loading Clojure data structures into datasets; Viewing datasets interactively with view; Converting datasets to matrices; Using infix formulas in Incanter; Selecting columns with ; Selecting rows with ; Filtering datasets with where; Grouping data with group-by; Saving datasets to CSV and JSON; Projecting from multiple datasets with join; Chapter 7: Preparing for and Performing Statistical Data Analysis with Incanter; Introduction.|