[graalvm-users] Tightly coupling Ruby and R
chris.seaton at oracle.com
Mon Oct 1 08:11:03 PDT 2018
Thanks for the summary of your progress, Rodrigo.
Are you planning a blog post or something about this in the future? Let us know if you need any more support from us.
> On 28 Sep 2018, at 16:00, Rodrigo Botafogo <rodrigo.a.botafogo at gmail.com> wrote:
> 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 http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html 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
> # 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
> 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...]
> Min 1Q Median 3Q Max
> -13.5433 -3.1039 -0.7052 2.0844 27.1153
> 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://github.com/rbotafogo/cantata.git
> * gem install rspec
> * Use rake to run the tests ('rake -T' shows all available tasks)
> * 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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