Markdown vs R: What are the differences?
What is Markdown? Text-to-HTML conversion tool/syntax for web writers, by John Gruber. Markdown is two things: (1) a plain text formatting syntax; and (2) a software tool, written in Perl, that converts the plain text formatting to HTML.
What is R? 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.
Markdown and R belong to "Languages" category of the tech stack.
"Easy formatting" is the top reason why over 345 developers like Markdown, while over 58 developers mention "Data analysis " as the leading cause for choosing R.
According to the StackShare community, Markdown has a broader approval, being mentioned in 756 company stacks & 718 developers stacks; compared to R, which is listed in 128 company stacks and 97 developer stacks.
What is Markdown?
What is R?
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Jekyll is an open source static site generator (SSG) with a Ruby at its core which transform your plain text into static websites and blogs.
It is simple means no more databases, comment moderation, or pesky updates to install—just your content. As said earlier SSG uses Markdown, Liquid, HTML & CSS go in and come out ready for deployment. Lastly it's blog-aware permalinks, categories, pages, posts, and custom layouts are all first-class citizens here.
For Stack Decisions I needed to add Markdown in the decision composer to give our users access to some general styling when writing their decisions. We used React & GraphQL on the #Frontend and Ruby & GraphQL on the backend.
Instead of using Showdown or another tool, We decided to parse the Markdown on the backend so we had more control over what we wanted to render in Markdown because we didn't want to enable all Markdown options, we also wanted to limit any malicious code or images to be embedded into the decisions and Markdown was a fairly large to import into our component so it was going to add a lot of kilobytes that we didn't need.
We also needed to style how the markdown looked, we are currently using Glamorous so I used that but we are planning to update this to Emotion at some stage as it has a fairly easy upgrade path rather than switching over to styled-components or one of the other cssInJs alternatives.
Also we used React-Mentions for tagging tools and topics in the decisions. Typing
@ will let you tag a tool, and typing
# will allow you to tag a topic.
The Markdown options that we chose to support are tags:
If there are anymore tags you'd love to see added in the composer leave me a comment below and we will look into adding them.
I needed to make stack decisions accept a subset of Markdown, similarly to sites like Reddit or Stack Overflow.
Problem solved! #StackDecisionsLaunch
More than year ago I was looking for the best editor of Angular 2 application and I've tried Visual Studio Code and Atom. Atom had performance issues that put me off completely to use it again. Visual Studio Code became my main editor #Typescript files (and partly editor of #Java files). I'm happy with Visual Studio Code and I've never look back on Atom. There wasn't any reason to try Atom again, because Visual Studio Code fulfills my requirements very well. I use it for editing of TypeScript, #HTML, #Sass, JSON, Docker and Markdown.
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 E