What is Python?
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
I started with a CodeAcademy course before working on my first Rails project. The language is easy to read and Rails is an amazing framework. The online community is fantastic, the documentation is amazing, and there is no shortage of Ruby Gems which allows you to quickly "cobble" together a new project.
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
Only thing worth noting here is that we saw some major issues with memory swapping on Heroku with Ruby 2.1.2. We weren’t the only ones. So we actually reverted back to 2.0.0. Here’s what happened: . We’re planning to upgrade to 2.1.4 once we’ve had time to test thoroughly.
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.
When we are comparing Turing complete languages it's not about what compiles faster or to the cleanest machine code, it's about what the developer can think in and write faster, including the availability of libraries. A couple of times I wrote some ruby scripts for this project. I just like the language best for some things. Love the syntax.
To me, this is by far the best programming language. Why? Because it’s the only language that really got me going after trying to get into programming with Java for a while. Python is powerful, easy to learn, and gets you to unsderstand other languages more once you understand it. Did I state I love the python language? Well, I do..
Backend server for analysis of image samples from iPhone microscope lens. Chose this because of familiarity. The number one thing that I've learned at hackathons is that work exclusively with what you're 100% comfortable with. I use Python extensively at my day job at Wit.ai, so it was the obvious choice for the bulk of my coding.
been a pythoner for around 7 years, maybe longer. quite adept at it, and love using the higher constructs like decorators. was my goto scripting language until i fell in love with clojure. python's also the goto for most vfx studios and great for the machine learning. numpy and pyqt for the win.
We are primarily a Ruby shop; our main apps are running on Ruby + Rails, our Slack bot is written in Ruby and our smaller projects are on Sinatra. It makes it really easy for us to re-use code and switch between different projects since they are all on the same language.
Because happiness. Testing via RSpec (mostly model and request specs), Factory Girl for fixtures, VCR for testing API responses, timecop, Redcarpet for Markdown rendering, Intercom gem for posting events.
Most of our services are written in Ruby. We started out as a Ruby shop, but we're slowly also moving some stuff to Go. We're using Go more and more these days, actually, including some backend services.
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)