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Common Lisp
Common Lisp

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R
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Common Lisp vs R: What are the differences?

Developers describe Common Lisp as "The modern, multi-paradigm, high-performance, compiled, ANSI-standardized descendant of the long-running family of Lisp programming languages". Lisp was originally created as a practical mathematical notation for computer programs, influenced by the notation of Alonzo Church's lambda calculus. It quickly became the favored programming language for artificial intelligence (AI) research. As one of the earliest programming languages, Lisp pioneered many ideas in computer science, including tree data structures, automatic storage management, dynamic typing, conditionals, higher-order functions, recursion, and the self-hosting compiler. [source: wikipedia]. 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.

Common Lisp and R can be primarily classified as "Languages" tools.

"Flexibility" is the primary reason why developers consider Common Lisp over the competitors, whereas "Data analysis " was stated as the key factor in picking R.

According to the StackShare community, R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to Common Lisp, which is listed in 5 company stacks and 3 developer stacks.

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What is Common Lisp?

Lisp was originally created as a practical mathematical notation for computer programs, influenced by the notation of Alonzo Church's lambda calculus. It quickly became the favored programming language for artificial intelligence (AI) research. As one of the earliest programming languages, Lisp pioneered many ideas in computer science, including tree data structures, automatic storage management, dynamic typing, conditionals, higher-order functions, recursion, and the self-hosting compiler. [source: wikipedia]

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 tools integrate with Common Lisp?
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What are some alternatives to Common Lisp and R?
Clojure
Clojure is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language - it compiles directly to JVM bytecode, yet remains completely dynamic. Clojure is a dialect of Lisp, and shares with Lisp the code-as-data philosophy and a powerful macro system.
Haskell
Racket
It is a general-purpose, multi-paradigm programming language based on the Scheme dialect of Lisp. It is designed to be a platform for programming language design and implementation. It is also used for scripting, computer science education, and research.
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.
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Decisions about Common Lisp and R
Eric Colson
Eric Colson
Chief Algorithms Officer at Stitch Fix | 19 upvotes 289.9K 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
#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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Reviews of Common Lisp and R
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How developers use Common Lisp 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 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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