- Clojure Data Analysis Cookbook Second Edition
- Table of Contents
- Clojure Data Analysis Cookbook Second Edition
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 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
- Querying RDF data with SPARQL
- Aggregating data from different formats
- 2. Cleaning and Validating Data
- Introduction
- Cleaning data with regular expressions
- Maintaining consistency with synonym maps
- Identifying and removing duplicate data
- Regularizing numbers
- Calculating relative values
- Parsing 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
- 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 ensure
- Introducing safe side effects into the STM
- Maintaining data consistency with validators
- Monitoring processing with watchers
- Debugging concurrent programs with watchers
- Recovering from errors in agents
- Managing large inputs with sized queues
- 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
- Combining function calls with reducers
- Parallelizing with reducers
- Generating online summary statistics for data streams with reducers
- Using type hints
- Benchmarking with Criterium
- 5. Distributed Data Processing with Cascalog
- Introduction
- Initializing Cascalog and Hadoop for distributed processing
- Querying data with Cascalog
- Distributing data with Apache HDFS
- Parsing CSV files with Cascalog
- Executing complex queries with Cascalog
- Aggregating data with Cascalog
- Defining new Cascalog operators
- Composing Cascalog queries
- Transforming data with Cascalog
- 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
- 7. Statistical Data Analysis with Incanter
- Introduction
- Generating summary statistics with $rollup
- Working with changes in values
- Scaling variables to simplify variable relationships
- Working with time series data with Incanter Zoo
- Smoothing variables to decrease variation
- Validating sample statistics with bootstrapping
- Modeling linear relationships
- Modeling non-linear relationships
- Modeling multinomial Bayesian distributions
- Finding data errors with Benford's law
- 8. Working with Mathematica and R
- Introduction
- Setting up Mathematica to talk to Clojuratica for Mac OS X and Linux
- Setting up Mathematica to talk to Clojuratica for Windows
- Calling Mathematica functions from Clojuratica
- Sending matrixes to Mathematica from Clojuratica
- Evaluating Mathematica scripts from Clojuratica
- Creating functions from Mathematica
- Setting up R to talk to Clojure
- Calling R functions from Clojure
- Passing vectors into R
- Evaluating R files from Clojure
- Plotting in R from Clojure
- 9. Clustering, Classifying, and Working with Weka
- Introduction
- Loading CSV and ARFF files into Weka
- Filtering, renaming, and deleting columns in Weka datasets
- Discovering groups of data using K-Means clustering
- Finding hierarchical clusters in Weka
- Clustering with SOMs in Incanter
- Classifying data with decision trees
- Classifying data with the Naive Bayesian classifier
- Classifying data with support vector machines
- Finding associations in data with the Apriori algorithm
- 10. Working with Unstructured and Textual Data
- Introduction
- Tokenizing text
- Finding sentences
- Focusing on content words with stoplists
- Getting document frequencies
- Scaling document frequencies by document size
- Scaling document frequencies with TF-IDF
- Finding people, places, and things with Named Entity Recognition
- Mapping documents to a sparse vector space representation
- Performing topic modeling with MALLET
- Performing naïve Bayesian classification with MALLET
- 11. Graphing in Incanter
- Introduction
- Creating scatter plots with Incanter
- Graphing non-numeric data in bar charts
- Creating histograms with Incanter
- Creating function plots with Incanter
- Adding equations to Incanter charts
- Adding lines to scatter charts
- Customizing charts with JFreeChart
- Customizing chart colors and styles
- Saving Incanter graphs to PNG
- Using PCA to graph multi-dimensional data
- Creating dynamic charts with Incanter
- 12. Creating Charts for the Web
- Introduction
- Serving data with Ring and Compojure
- Creating HTML with Hiccup
- Setting up to use ClojureScript
- Creating scatter plots with NVD3
- Creating bar charts with NVD3
- Creating histograms with NVD3
- Creating time series charts with D3
- Visualizing graphs with force-directed layouts
- Creating interactive visualizations with D3
- Index
R, and the many incredibly useful packages that have been developed for it, provides a rich environment to do statistical computing. To access any of this, however, we'll need to be able to call functions from Clojure. We do this by constructing R expressions as strings, sending them to the R server, and getting the results back. The Rserve Java library helps us convert the results to Java objects that we can access.
We must first complete the recipe, Setting up R to talk to Clojure, and have Rserve running. We must also have the Clojure-specific parts of that recipe done and the connection to Rserve made.
Once we have a connection to Rserver, we can call functions by passing the complete call—function and arguments—to the server as a string and evaluating it. Then, we have to pull the results back out, as follows:
user=> (map #(.asDouble %)
(.. *r-cxn* (eval "qr(c(1,2,3,4,5,6,7))") asList))
(-11.832159566199232 1.0 1.0845154254728517 1.0)To call an R function, we make a series of method calls on the RConnection object that we created in Setting up R to talk to Clojure. The first call, eval, takes the code to evaluate as a string and passes it to R.
Next, we have to convert the output to a Clojure data structure. We first call asList on the result, which converts it to a type that implements java.lag.Iterable. This can be passed to map, which is used to convert the list's members to doubles.
The example in this recipe called the R function qr. This calculates the QR decomposition of a matrix. For more information on this function, see http://www.math.montana.edu/Rweb/Rhelp/qr.html.
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