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R Language

A language and environment for statistical computing and graphics
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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
355 companies reportedly use R Language in their tech stacks, including Instacart, Delivery Hero, and Durstexpress GmbH.

Developers
1006 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 8 tools that integrate with R Language.
Private Decisions at about R Language

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

Shared insights
on
R LanguageR 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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Shared insights
on
R LanguageR 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 5.4K views
Shared insights
on
R LanguageR 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 1.1M views

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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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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Shared insights
on
R LanguageR 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 5.4K views
Shared insights
on
R LanguageR Language

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

See more
Shared insights
on
R LanguageR 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?
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.
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.
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.
SAS
It is a command-driven software package used for statistical analysis and data visualization. It is available only for Windows operating systems. It is arguably one of the most widely used statistical software packages in both industry and academia.
Rust
Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
See all alternatives

R Language's Followers
1032 developers follow R Language to keep up with related blogs and decisions.
arenberg tran
Nghiep Tran
Josep Oriol Ayats
Arash Younesi
Devesh Tarasia
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Tarek Hassan
Lakshey Singhal
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Kyle Domingo