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Julia vs R: What are the differences?

Developers describe Julia as "A high-level, high-performance dynamic programming language for technical computing". Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. 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.

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

"Lisp-like Macros" is the top reason why over 7 developers like Julia, while over 58 developers mention "Data analysis " as the leading cause for choosing R.

Julia is an open source tool with 22.7K GitHub stars and 3.43K GitHub forks. Here's a link to Julia's open source repository on GitHub.

Instacart, Key Location, and Custora are some of the popular companies that use R, whereas Julia is used by inFeedo, Platform Project, and N26. R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to Julia, which is listed in 5 company stacks and 5 developer stacks.

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What is Julia?

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

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 Julia and R?
    Python
    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
    PHP
    Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.
    JavaScript
    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
    Java
    Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!
    HTML5
    HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.
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    Decisions about Julia and R
    Eric Colson
    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 353.4K 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

    See more
    Interest over time
    Reviews of Julia and R
    Avatar of blegat
    Engineer
    Review ofJuliaJulia

    This language and its community are exactly how I have always dreamed them to be. The language is both easy to use/very flexible/powerful and very fast ! The community is very dynamic, open minded and ambitious.

    How developers use Julia 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 BeastyBlacksmith
    BeastyBlacksmith uses JuliaJulia

    Fast development and fast execution time. Flawless communication between packages.

    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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