HAML vs R: What are the differences?
What is HAML? HTML Abstraction Markup Language - A Markup Haiku. Haml is a markup language that’s used to cleanly and simply describe the HTML of any web document, without the use of inline code. Haml functions as a replacement for inline page templating systems such as PHP, ERB, and ASP. However, Haml avoids the need for explicitly coding HTML into the template, because it is actually an abstract description of the HTML, with some code to generate dynamic content.
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.
HAML and R can be primarily classified as "Languages" tools.
"Clean and simple" is the top reason why over 66 developers like HAML, while over 58 developers mention "Data analysis " as the leading cause for choosing R.
HAML is an open source tool with 3.44K GitHub stars and 544 GitHub forks. Here's a link to HAML's open source repository on GitHub.
According to the StackShare community, R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to HAML, which is listed in 113 company stacks and 40 developer stacks.
What is HAML?
What is R?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
Sign up to add, upvote and see more consMake informed product decisions
Sign up to get full access to all the companiesMake informed product decisions
When we rebooted our front-end stack earlier this year, we wanted to have a consolidated and friendly developer experience. Up to that point we were using Sass and BEM. There was a mix of HAML views, React components and Angular. Since our ongoing development was going to be exclusively in React, we wanted to shift to an inline styling library so the "wall of classnames" could be eliminated. The ever-shifting landscape of inline CSS libraries for React is sometimes difficult to navigate.
We decided to go with Glamorous for a few reasons:
As you may or may not know, Glamorous has ceased active development and been mostly superseded by Emotion. We are planning to migrate to either Emotion or @styled-components in the near future, and I'll write another Stack Decision when we get there!
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
Personally, I really like HAML. Not having to use open and close tags is a huge time saver. As a result, writing markup with HAML is much more pleasant. HAML essentially forces you to be very strict about spacing, organization, and structure. It also makes the markup easier to read. Protip: I use this pretty frequently: htmltohaml.com
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.
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)