Clojure Data Analysis Cookbook

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
• Introduction
• Cleaning 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 ensure
• Introducing 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 reducers
• Harnessing 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 Pallet

• Chapter 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
• Generating summary statistics with $rollup
• Differencing variables to show changes
• Scaling variables to simplify variable relationships
• Working with time series data with Incanter Zoo
• Smoothing variables to decrease noise
• Validating sample statistics with bootstrapping
• Modeling linear relationships
• Modeling non-linear relationships
• Modeling multimodal Bayesian distributions
• Finding data errors with Benford's law

• Chapter 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 matrices to Mathematica from Clojuratica
• Evaluating Mathematica scripts from Clojuratica
• Creating functions from Mathematica
• Processing functions in parallel in 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

• Chapter 9: Clustering, Classifying, and Working with Weka
• Introduction
• Loading CSV and ARFF files into Weka
• Filtering and renaming 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

• Chapter 10: Graphing in Incanter
• Introduction
• Creating scatter plots with Incanter
• Creating bar charts 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
• Saving Incanter graphs to PNG
• Using PCA to graph multi-dimensional data
• Creating dynamic charts with Incanter

• Chapter 11: 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
• Visualizing graphs with force-directed layouts
• Creating interactive visualizations with D3

• Index