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CoffeeScript vs R: What are the differences?
CoffeeScript: Unfancy JavaScript. CoffeeScript is a little language that compiles into JavaScript. Underneath that awkward Java-esque patina, JavaScript has always had a gorgeous heart. CoffeeScript is an attempt to expose the good parts of JavaScript in a simple way; 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.
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.
MACHINE LEARNING
Python is the default go-to for machine learning. It has a wide variety of useful packages such as pandas and numpy to aid with ML, as well as deep-learning frameworks. Furthermore, it is more production-friendly compared to other ML languages such as R.
Pytorch is a deep-learning framework that is both flexible and fast compared to Tensorflow + Keras. It is also well documented and has a large community to answer lingering questions.
Pros of R Language
- Data analysis86
- Graphics and data visualization64
- Free55
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Shiny interactive plots6
- Preferred Medium6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
Cons of R Language
- Very messy syntax6
- Tables must fit in RAM4
- Arrays indices start with 13
- Messy syntax for string concatenation2
- No push command for vectors/lists2
- Messy character encoding1
- Poor syntax for classes0
- Messy syntax for array/vector combination0