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R Language

A language and environment for statistical computing and graphics

What is R Language?

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.
R Language is a tool in the Languages category of a tech stack.

Who uses R Language?

Companies
353 companies reportedly use R Language in their tech stacks, including Instacart, Zalando, and Delivery Hero.

Developers
892 developers on StackShare have stated that they use R Language.

R Language Integrations

Jupyter, Apache Zeppelin, Stan, RapidMiner, and Quilt are some of the popular tools that integrate with R Language. Here's a list of all 6 tools that integrate with R Language.

Why developers like R Language?

Here鈥檚 a list of reasons why companies and developers use R Language
Private Decisions at about R Language
Private to your company

Here are some stack decisions, common use cases and reviews by members of with R Language in their tech stack.

R Language
R Language

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

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R Language
R Language

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. R

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Tino Gehlert
Tino Gehlert
Data Scientist at Viessmann | 1 upvotes 4.8K views
R Language
R Language

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

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Public Decisions about R Language

Here are some stack decisions, common use cases and reviews by companies and developers who chose R Language in their tech stack.

Eric Colson
Eric Colson
Chief Algorithms Officer at Stitch Fix | 19 upvotes 898.6K views
atStitch FixStitch Fix
Kafka
Kafka
PostgreSQL
PostgreSQL
Amazon S3
Amazon S3
Apache Spark
Apache Spark
Presto
Presto
Python
Python
R Language
R Language
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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vivekseq
vivekseq
| 2 upvotes 6.9K views
atSequoia Consulting GroupSequoia Consulting Group
R Language
R Language
Shiny
Shiny

We have decided to make use of R for ML and Shiny for UI. We are debating usage of self hosted shiny server v/s shinyapp . Our Decision to go with R was to do with Sizes of data and availability of tools. R Shiny

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R Language
R Language

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. R

See more
Tino Gehlert
Tino Gehlert
Data Scientist at Viessmann | 1 upvotes 4.8K views
R Language
R Language

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

See more
R Language
R Language

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

See more

R Language Alternatives & Comparisons

What are some alternatives to R Language?
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.
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.
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.
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.
PHP
Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.
See all alternatives

R Language's Followers
898 developers follow R Language to keep up with related blogs and decisions.
Michael Topf
ankitgrover8
Hannah Shumway
Celestin Okoroji
vijaya vanipenta
Vajresh Balaji
Gabriel Dias
D Marques
Rafael Fabian Navarro Madrid
Nick Siska