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JRuby vs R: What are the differences?
Developers describe JRuby as "A high performance, stable, fully threaded Java implementation of the Ruby programming language". JRuby is the effort to recreate the Ruby (http://www.ruby-lang.org) interpreter in Java. The Java version is tightly integrated with Java to allow both to script any Java class and to embed the interpreter into any Java application. See the docs directory for more information. On the other hand, R is detailed as "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.
JRuby and R can be primarily classified as "Languages" tools.
"Java" is the primary reason why developers consider JRuby over the competitors, whereas "Data analysis " was stated as the key factor in picking R.
JRuby is an open source tool with 3.32K GitHub stars and 830 GitHub forks. Here's a link to JRuby'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 JRuby, which is listed in 13 company stacks and 4 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
What is R Language?
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What tools integrate with R Language?
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