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R
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R vs SciPy: What are the differences?

What is R? 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.

What is SciPy? Scientific Computing Tools for Python. Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

R and SciPy are primarily classified as "Languages" and "Data Science" tools respectively.

SciPy is an open source tool with 6.01K GitHub stars and 2.85K GitHub forks. Here's a link to SciPy's open source repository on GitHub.

Instacart, Zalando, and Thumbtack are some of the popular companies that use R, whereas SciPy is used by Suggestic, Botimize, and Zetaops. R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to SciPy, which is listed in 12 company stacks and 4 developer stacks.

- No public GitHub repository available -

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.

What is SciPy?

Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
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Why do developers choose R?
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      What companies use R?
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      What tools integrate with R?
      What tools integrate with SciPy?
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        What are some alternatives to R and SciPy?
        MATLAB
        Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
        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.
        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.
        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!
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        Decisions about R and SciPy
        Eric Colson
        Eric Colson
        Chief Algorithms Officer at Stitch Fix | 19 upvotes 219.3K views
        atStitch FixStitch Fix
        Amazon EC2 Container Service
        Amazon EC2 Container Service
        Docker
        Docker
        PyTorch
        PyTorch
        R
        R
        Python
        Python
        Presto
        Presto
        Apache Spark
        Apache Spark
        Amazon S3
        Amazon S3
        PostgreSQL
        PostgreSQL
        Kafka
        Kafka
        #Data
        #DataStack
        #DataScience
        #ML
        #Etl
        #AWS

        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 R and SciPy
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        How developers use R and SciPy
        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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