Perl vs R: What are the differences?
Developers describe Perl as "Highly capable, feature-rich programming language with over 26 years of development". Perl is a general-purpose programming language originally developed for text manipulation and now used for a wide range of tasks including system administration, web development, network programming, GUI development, and more. 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.
Perl and R belong to "Languages" category of the tech stack.
"Lots of libraries" is the primary reason why developers consider Perl over the competitors, whereas "Data analysis " was stated as the key factor in picking R.
Perl is an open source tool with 428 GitHub stars and 150 GitHub forks. Here's a link to Perl's open source repository on GitHub.
According to the StackShare community, R has a broader approval, being mentioned in 128 company stacks & 95 developers stacks; compared to Perl, which is listed in 132 company stacks and 61 developer stacks.
What is Perl?
What is R?
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In addition to our fancy Docker setup, we have captured and sanitized production logs for the behavior of our legacy Perl MTA, and we can test that the log output from the new Go version behaves the same way as the old version. These tests are set up to allow us to switch between the legacy and new version of the MTA and ensure that both systems behave in a legacy-compatible way. Not only can we ensure that we operate against a variety of issues we've seen over time from inboxes, but we know that the newest version of our MTA continues to cover all the same expected behaviors of the legacy version. #CodeCollaborationVersionControl #ContinuousIntegration
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:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
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.
The whole backend part (deployment and other scripts, business logic, web interface) is written in Perl.
Весь бэкенд (скрипты деплоя и прочие, бизнес-логика, веб-интерфейс) написан на Perl.
I use Perl to rip through log files and compare them to some signature files I have created. When I get a match, it adds the bad guy to the list of shame in MySQL.
Connect to database, data analytics, draw diagram. Machine Learning application, and also used Spark-R for big data processing.
Visualisation of air quality in various rooms by RShiny (hosted free on shinyapps.io)
A very expressive language, lets you say the same thing in many different ways