CoffeeScript vs R: What are the differences?
CoffeeScript and R can be primarily classified as "Languages" tools.
"Easy to read" is the top reason why over 197 developers like CoffeeScript, while over 58 developers mention "Data analysis " as the leading cause for choosing R.
CoffeeScript is an open source tool with 15.2K GitHub stars and 1.99K GitHub forks. Here's a link to CoffeeScript's open source repository on GitHub.
Code School, Zaarly, and thoughtbot are some of the popular companies that use CoffeeScript, whereas R is used by AdRoll, Instacart, and Verba. CoffeeScript has a broader approval, being mentioned in 364 company stacks & 170 developers stacks; compared to R, which is listed in 128 company stacks and 97 developer stacks.
What is CoffeeScript?
What is R?
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Choosing to add TypeScript has given us one more layer to rely on to help enforce code quality, good standards, and best practices within our engineering organization. One of the biggest benefits for us as an engineering team has been how well our IDEs and editors (e.g., Visual Studio Code ) integrate with and understand TypeScript . This allows developers to catch many more errors at development time instead of relying on run time. The end result is safer (from a type perspective) code and a more efficient coding experience that helps to catch and remove errors with less developer effort.
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
We have added very little to the CoffeeScript Hubot application – just enough to allow it to talk to our Hubot workers. The Hubot workers implement our operational management functionality and expose it to Hubot so we can get chat integration for free. We’ve also tailored the authentication and authorization code of Hubot to meet the needs of roles within our team.
For larger tasks, we’ve got an internal #CLI written in Go that talks to the same #API as Hubot, giving access to the same functionality we have in Slack, with the addition of scripting, piping, and all of our favorite #Unix tools. When the Hubot worker recognizes the CLI is in use, it logs the commands to Slack to maintain visibility of operational changes.
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.
All front-end / back-end is driven by Coffeescript. For the main ReactJS functionality JSX is embedded with coffee in .cjsx files / handled by Browserify.
We like CoffeeScript because it's more readable, we use it because we have a lot of libraries and functions already (plays nicely with Rails, too)
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)