[graalvm-users] Tightly coupling Ruby and R

[Rodrigo Botafogo]

Hello...

I've being working on coupling Ruby and R for some time now (since rc1) and
I think a have managed to advance quite a bit, thanks in great part to the
help from people here, specially Chris and Stepan: thanks!!  So, now,
instead of posting a question, this post is tho show where I'm at and see
if you have any suggestions, opinions, feedback, etc.

Bellow two examples of how the coupling works.  The first is a scatter plot
extracted from
https://urldefense.proofpoint.com/v2/url?u=http-3A__r-2Dstatistics.co_Top50-2DGgplot2-2DVisualizations-2DMasterList-2DR-2DCode.html&d=DwIFaQ&c=RoP1YumCXCgaWHvlZYR8PZh8Bv7qIrMUB65eapI_JnE&r=CUkXBxBNT_D5N6HMJ5T9Z6rmvNKYsqupcbk72K0lcoQ&m=V4perA7MCLoMrUH-4v2X9TEXm7WzhucqB9Quklf-V8g&s=EuFjHNgCyBdxBylfpIDxJFydO57YqQEr4t1YIYHh-YE&e=
and the second a 5th degree polynomial regression. The idea of the coupling
is to use Ruby syntax, but emulate R as closely as possible in order to
make it trivial to use for an R developer.

====================================================================
require 'cantata'
require 'ggplot'

# set options
R.options(scipen: 999)  # turn-off scientific notation like 1e+48
R.theme_set(R.theme_bw)  # pre-set the bw theme.

# read the R 'midwest' dataset onto the 'midwest' variable
midwest = ~:midwest

R.awt

# Scatterplot
gg = midwest.ggplot(E.aes(x: :area, y: :poptotal)) +
       R.geom_point(E.aes(col: :state, size: :popdensity)) +
       R.geom_smooth(method: "loess", se: false) +
       R.xlim(R.c(0, 0.1)) +
       R.ylim(R.c(0, 500000)) +
       R.labs(subtitle: "Area Vs Population",
                   y: "Population",
                   x: "Area",
                   title: "Scatterplot",
                   caption: "Source: midwest")

puts gg

[image: image.png]


===================================================================

As another example, here is how to get a polynomial regression using the
Boston data from R package ISLR.

require 'cantata'
R.require 'MASS'
R.require 'ISLR'

lm_fit5 = R.lm(R.formula("medv ~ poly(lstat, 5)"), data: :Boston)
puts lm_fit5.summary

[This output was cut from the total print...]

Residuals:
     Min       1Q   Median       3Q      Max
-13.5433  -3.1039  -0.7052   2.0844  27.1153

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)
(Intercept)       22.5328     0.2318  97.197  < 2e-16 ***
poly(lstat, 5)1 -152.4595     5.2148 -29.236  < 2e-16 ***
poly(lstat, 5)2   64.2272     5.2148  12.316  < 2e-16 ***
poly(lstat, 5)3  -27.0511     5.2148  -5.187 3.10e-07 ***
poly(lstat, 5)4   25.4517     5.2148   4.881 1.42e-06 ***
poly(lstat, 5)5  -19.2524     5.2148  -3.692 0.000247 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.215 on 500 degrees of freedom
Multiple R-squared:  0,6817,    Adjusted R-squared:  0,6785
F-statistic: 214,2 on 5 and 500 DF,  p-value: < 2.2e-16

===================================================================

In order to test this:

* Install GraalVM (rc6)
* Install TruffleRuby (follow the simple instructions)
* Install FastR
* Clone the git repo: https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_rbotafogo_cantata.git&d=DwIFaQ&c=RoP1YumCXCgaWHvlZYR8PZh8Bv7qIrMUB65eapI_JnE&r=CUkXBxBNT_D5N6HMJ5T9Z6rmvNKYsqupcbk72K0lcoQ&m=V4perA7MCLoMrUH-4v2X9TEXm7WzhucqB9Quklf-V8g&s=JMJtiz_aw-o6IW_FMB8solXtUJwYRuh6nzsv02v5g_E&e=
* gem install rspec
* Use rake to run the tests ('rake -T' shows all available tasks)

Doing:

* rake specs:all -- Runs all the specs.  Reading the specs shows much of
how to use the language
* rake sthda:all -- Runs a slideshow with over 80 plots
* rake islr:all -- Runs some 'labs' from the Introduction to Statistical
Learning book



-- 
Rodrigo Botafogo
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