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JRuby
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JRuby vs R: What are the differences?

Developers describe JRuby as "A high performance, stable, fully threaded Java implementation of the Ruby programming language". JRuby is the effort to recreate the Ruby (http://www.ruby-lang.org) interpreter in Java. The Java version is tightly integrated with Java to allow both to script any Java class and to embed the interpreter into any Java application. See the docs directory for more information. On the other hand, R is detailed as "A language and environment for statistical computing and graphics". R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.

JRuby and R can be primarily classified as "Languages" tools.

"Java" is the primary reason why developers consider JRuby over the competitors, whereas "Data analysis " was stated as the key factor in picking R.

JRuby is an open source tool with 3.32K GitHub stars and 830 GitHub forks. Here's a link to JRuby's open source repository on GitHub.

According to the StackShare community, R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to JRuby, which is listed in 13 company stacks and 4 developer stacks.

- No public GitHub repository available -

What is JRuby?

JRuby is the effort to recreate the Ruby (http://www.ruby-lang.org) interpreter in Java. The Java version is tightly integrated with Java to allow both to script any Java class and to embed the interpreter into any Java application. See the docs directory for more information.

What is R?

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
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    What are some alternatives to JRuby and R?
    Ruby
    Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.
    Groovy
    Groovy builds upon the strengths of Java but has additional power features inspired by languages like Python, Ruby and Smalltalk. It makes modern programming features available to Java developers with almost-zero learning curve.
    Rails
    Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.
    Go
    Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.
    PHP
    Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.
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    Decisions about JRuby and R
    Eric Colson
    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 348.3K views
    atStitch FixStitch Fix
    Kafka
    Kafka
    PostgreSQL
    PostgreSQL
    Amazon S3
    Amazon S3
    Apache Spark
    Apache Spark
    Presto
    Presto
    Python
    Python
    R
    R
    PyTorch
    PyTorch
    Docker
    Docker
    Amazon EC2 Container Service
    Amazon EC2 Container Service
    #AWS
    #Etl
    #ML
    #DataScience
    #DataStack
    #Data

    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

    At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

    For more info:

    #DataScience #DataStack #Data

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    Interest over time
    Reviews of JRuby and R
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    How developers use JRuby and R
    Avatar of benyomin
    benyomin uses RR

    What are my other choices for a vectorized statistics language. Professor was pushing SAS Jump (or was that SPSS) with a menu-driven point and click approach. (Reproducibility can still be accomplished, you publish the script generated by all your clicks.) But I want to type everything, great online tutorials for R. I think I made the right pick.

    Avatar of Ralic Lo
    Ralic Lo uses RR

    Connect to database, data analytics, draw diagram. Machine Learning application, and also used Spark-R for big data processing.

    Avatar of Tino Gehlert
    Tino Gehlert uses RR

    Visualisation of air quality in various rooms by RShiny (hosted free on shinyapps.io)

    Avatar of Sesync
    Sesync uses RR

    R is primarily used by SESYNC's researchers

    Avatar of STILLWATER SUPERCOMPUTING INC
    STILLWATER SUPERCOMPUTING INC uses RR

    Offline deep analytics and modeling

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