daru
====

Data Analysis in RUby

[![Gem Version](https://badge.fury.io/rb/daru.svg)](http://badge.fury.io/rb/daru)

## Introduction

daru (Data Analysis in RUby) is a library for storage, analysis and manipulation of data. It aims to be the preferred data analysis library for Ruby. 

Development of daru was started to address the fragmentation of Dataframe-like classes which were created in many ruby gems as per their own needs. 

This creates a hurdle in using these gems together to solve a problem. For example, calculating something in [statsample](https://github.com/clbustos/statsample) and plotting the results in [Nyaplot](https://github.com/domitry/nyaplot).

daru is heavily inspired by `Statsample::Dataset`, `Nyaplot::DataFrame` and the super-awesome pandas, a very mature solution in Python.

## Data Structures

daru employs several data structures for storing and manipulating data:
* Vector - A basic 1-D vector.
* DataFrame - A 2-D matrix-like structure which is internally composed of named `Vector` classes.

daru data structures can be constructed by using several Ruby classes. These include `Array`, `Hash`, `Matrix`, [NMatrix](https://github.com/SciRuby/nmatrix) and [MDArray](https://github.com/rbotafogo/mdarray). daru brings a uniform API for handling and manipulating data represented in any of the above Ruby classes.

Currently things work as expected for Arrays only. Rest will added over the next few weeks.

## Testing

Install jruby using `rvm install jruby`, then run `jruby -S gem install mdarray`, followed by `bundle install`. You will need to install `mdarray` manually because of strange gemspec file behaviour. If anyone can automate this then I'd greatly appreciate it! Then run `rspec` in JRuby to test for MDArray functionality.

Then switch to MRI, do a normal `bundle install` followed by `rspec` for testing everything else with NMatrix functionality.

## Roadmap

* Automate testing for both MRI and JRuby.
* Enable creation of DataFrame by only specifying an NMatrix/MDArray in initialize. Vector naming happens automatically (alphabetic) or is specified in an Array.
* Destructive map iterators for DataFrame and Vector.
* Completely test all functionality for NMatrix and MDArray.
* Basic Data manipulation and analysis operations: 
    - Different kinds of join operations
    - Dataframe/vector merge
    - Creation of correlation, covariance matrices
    - Verification of data in a vector
    - Basic vector statistics - mean, median, variance, etc.
* Vector arithmetic - elementwise addition, subtraction, multiplication, division.
* Transpose a dataframe.
* Option to express a DataFrame as an NMatrix or MDArray so as to use more efficient storage techniques.
* Assignment of a column to a single number should set the entire column to that number.
* == between daru_vector and string/number.
* Multiple column assignment with []=
* Creation of DataFrame from Array of Arrays.
* Multiple value assignment for vectors with []=.
* Load DataFrame from multiple sources (excel, SQL, etc.).
* Allow for boolean operations inside #[].
* Deletion of elements from Vector should only modify the index and leave the vector as it is so that compacting is not needed and things are faster.
* Add a #sync method which will sync the modified index with the unmodified vector.
* Ability to reorder the index of a dataframe.

Copyright (c) 2014, Sameer Deshmukh
All rights reserved