rinruby

This is a forked version of RinRuby. The advantage of this forked version is mainly addition of functionality.

Specific information of this version

INSTALL

• sudo gem install specific_install # Only required first time
• sudo gem specific_install https://github.com/fenrir-naru/rinruby.git dev-fenrir

LICENSE (same as the original version)

GPL-3. See LICENSE.txt for more information.

Original information (copied)

DESCRIPTION

RinRuby is a Ruby library that integrates the R interpreter in Ruby, making R's statistical routines and graphics available within Ruby. The library consists of a single Ruby script that is simple to install and does not require any special compilation or installation of R. Since the library is 100% pure Ruby, it works on a variety of operating systems, Ruby implementations, and versions of R. RinRuby's methods are simple, making for readable code. The website rinruby.ddahl.org describes RinRuby usage, provides comprehensive documentation, gives several examples, and discusses RinRuby's implementation.

Copyright 2005-2008 David B. Dahl

Developed by David B. Dahl. Documented by David B. Dahl and Scott Crawford

Homepage: http://rinruby.ddahl.org

Maintainer: Claudio Bustos

Contributors:

• fenrir-naru

FEATURES/PROBLEMS

• Pure Ruby. Works on Ruby 2.1, 2.2, 2.4 and JRuby-head (2018/03/29). There isn't any specific code that impides to use Ruby < 2.0, but is deprecated.
• Slower than RSRuby, but more robust

SYNOPSIS

Below is a simple example of RinRuby usage for simple linear regression. The simulation parameters are defined in Ruby, computations are performed in R, and Ruby reports the results. In a more elaborate application, the simulation parameter might come from input from a graphical user interface, the statistical analysis might be more involved, and the results might be an HTML page or PDF report.

Code

  require "rinruby"
  n = 10
  beta_0 = 1
  beta_1 = 0.25
  alpha = 0.05
  seed = 23423
  R.x = (1..n).entries
  R.eval <<EOF
      set.seed(#{seed})
      y <- #{beta_0} + #{beta_1}*x + rnorm(#{n})
      fit <- lm( y ~ x )
      est <- round(coef(fit),3)
      pvalue <- summary(fit)$coefficients[2,4]
  EOF
  puts "E(y|x) ~= #{R.est[0]} + #{R.est[1]} * x"
  if R.pvalue < alpha
    puts "Reject the null hypothesis and conclude that x and y are related."
  else
    puts "There is insufficient evidence to conclude that x and y are related."
  end

Output

  E(y|x) ~= 1.264 + 0.273 * x
  Reject the null hypothesis and conclude that x and y are related.

REQUIREMENTS

• R