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Groovy
Groovy

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176
R
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1.1K
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303
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Groovy vs R: What are the differences?

What is Groovy? A dynamic language for the Java platform. 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.

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.

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

"Java platform" is the top reason why over 38 developers like Groovy, while over 58 developers mention "Data analysis " as the leading cause for choosing R.

Groovy is an open source tool with 1.49K GitHub stars and 414 GitHub forks. Here's a link to Groovy'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 Groovy, which is listed in 79 company stacks and 73 developer stacks.

- No public GitHub repository available -

What is 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.

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 Groovy and R?
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!
Scala
Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.
Kotlin
Kotlin is a statically typed programming language for the JVM, Android and the browser, 100% interoperable with Java
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.
Gradle
Gradle is a build tool with a focus on build automation and support for multi-language development. If you are building, testing, publishing, and deploying software on any platform, Gradle offers a flexible model that can support the entire development lifecycle from compiling and packaging code to publishing web sites.
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Decisions about Groovy and R
Alex A
Alex A
Founder at PRIZ Guru · | 3 upvotes · 62K views
atPRIZ GuruPRIZ Guru
Grails
Grails
Play
Play
Scala
Scala
Groovy
Groovy
Gradle
Gradle

Some may wonder why did we choose Grails ? Really good question :) We spent quite some time to evaluate what framework to go with and the battle was between Play Scala and Grails ( Groovy ). We have enough experience with both and, to be honest, I absolutely in love with Scala; however, the tipping point for us was the potential speed of development. Grails allows much faster development pace than Play , and as of right now this is the most important parameter. We might convert later though. Also, worth mentioning, by default Grails comes with Gradle as a build tool, so why change?

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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 Groovy and R
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How developers use Groovy 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 Valdomiro Bilharvas
Valdomiro Bilharvas uses GroovyGroovy

Used as language to describe and interpret as tools with jenkinsfile

Avatar of Sodep
Sodep uses GroovyGroovy

NPL solutions and statistical machine learning.

Avatar of Tongliang Liu
Tongliang Liu uses GroovyGroovy

As long as you don't abuse def and Closure

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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