Links and Resources for Data Processing and Analysis in Ruby
Data Science is a new "sexy" buzzword without specific meaning but often used to substitute Statistics, Scientific Computing, Text and Data Mining and Visualization, Machine Learning, Data Processing and Warehousing as well as Retrieval Algorithms of any kind.
A lot of useful resources on this list come from the development by The Ruby Science Foundation, our contributors and our own day to day work on various data intensive applications. Read why this list is awesome.
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- Ruby vs. Python vs. Julia vs. R
- Standing on the shoulders of giants
- Data Manipulation
- Distributed Computing
- Data Structures
- Data sets
- Numeric and Symbolic Computation
- Interactive Computing
- Input and Output
- Provisioning Infrastructure
- Machine Learning
- Articles, Posts, Talks, and Presentations
- Related resources
- Wait but why?
Ruby vs. Python vs. Julia vs. R
Standing on the shoulders of giants
Ruby is (for now) not a Data Science centric language with a very large established library. Leveraging libraries from R, Python, and Julia helps Ruby to solve your tasks!
- pycall — Bridge into the Python world.
- rserve-client — Ruby connector for Rserve, R's binary server.
- kiba — lightweight Ruby ETL (Extract-Transform-Load) framework.
- jongleur — Workflow manager using DAG definitions to execute ETL tasks.
- ruby-spark — Ruby Interface to Apache Spark 1.x.x.
- jruby-spark — JRuby based bindings for Apache Spark.
- daru — Data Frame and Vector structures with comprehensive manipulating and visualization methods.
- numo-narray — n-dimensional Numerical Array for Ruby.
- nmatrix — dense and sparse linear algebra library for Ruby via SciRuby.
- kdtree — blazingly fast native 2d k-d tree.
- mdarray —
Array structure for
- spreadsheet — manipulation library for MS Excel spreadsheets.
- networkx — Ruby based NetworkX clone that handles various usecases of the Graph Data Structure.
- cumo — CUDA-aware numerical Array library with NArray similar interface.
- rdatasets — Data sets available in R via Rdatasets.
- red-datasets — Growing collection of publicly available data sets such as CIFAR-10, Iris, MNIST etc.
- rb-gsl — Ruby interface to the GNU Scientific Library. [dep: GLS]
- simple_stats —
Enumerablepatches for descriptive statistics.
- enumerable-statistics —
fast implementation of descriptive statistics for the
- statsample — basic and advanced statistics for Ruby. [dep: GLS]
- statsample-glm —
statsampleby Generalized Linear Models.
- statsample-bivariate-extension —
statsampleby Bivariate Correlations.
- statsample-timeseries —
statsampleby Time Series estimators.
- pca — Principal Component Analysis (PCA) in Ruby.
- descriptive-statistics —
descriptive extensions for the
Enumerablemodule or standalone usage.
- distribution — probabilistic distributions and descriptive measures for them.
- statistics2 — Normal, Chi-square, t- and F- probability distributions for Ruby.
Numeric and Symbolic Computation
- numo-linalg — linear algebraic operations for NArray.
- numo-gsl — Math and Statistics for NArray using GSL.[dep: GSL]
- symengine — Symbolic Computation with SymEngine.
- numo-ffte — Fast Fourier Transformation for NArray using the FFTE package.[FFTE]
Comprehensive tools for Data Visualization.
- matplotlib — Ruby based wrapper around matplotlib. [dep: matplotlib]
- mathematical — PNG and MathML renderings for your equations.
- daru-view — daru-view is interactive plotting gem for web application (any Ruby web application framework like Rails/Sinatra/Nanoc/Hanami) & IRuby notebook. It is a plugin gem for daru.
- daru-plotly — Plotly based visualization for Daru.
- ruby-graphviz [dep: Graphviz]
- gnuplot [dep: gnuplot]
- numo-gnuplot — gnuplot interface for the Numo package.
Input and Output
- ox — Optimized for speed XML parser and object marshaller.
- oj — High-speed JSON parser.
Domain specific formats
- inih — fast C based INI parser for Ruby.
- bolognese — conversion tool for citation formats like BibTeX, RIS, or Crossref XML.
Please look at our extensive Awesome ML with Ruby list.
Articles, Posts, Talks, and Presentations
- Awesome Data Science with Python
- Awesome Big Data - awesome curated list on all around Big Data.
- Awesome Spark — awesome list on Apache Spark goodies.
Wait but why?
There are a lot of software lists with tools related to the Data Science. There are a couple of lists with Ruby related projects. There are no lists of only working and tested software with documented scope. We'll try to make one!
What is awesome? Awesome are documented, maintained and focused tools.
Can something turn not awesome at a point? Yes! Abandoned projects with broken dependencies aren't awesome any more! They leave this list.
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Awesome Data Science with Ruby.
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