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Julia vs R: What are the differences?
Developers describe Julia as "A high-level, high-performance dynamic programming language for technical computing". Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. 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.
Julia and R can be primarily classified as "Languages" tools.
"Lisp-like Macros" is the top reason why over 7 developers like Julia, while over 58 developers mention "Data analysis " as the leading cause for choosing R.
Julia is an open source tool with 22.7K GitHub stars and 3.43K GitHub forks. Here's a link to Julia's open source repository on GitHub.
Instacart, Key Location, and Custora are some of the popular companies that use R, whereas Julia is used by inFeedo, Platform Project, and N26. R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to Julia, which is listed in 5 company stacks and 5 developer stacks.
After writing a project in Julia we decided to stick with Kotlin. Julia is a nice language and has superb REPL support, but poor tooling and the lack of reproducibility of the program runs makes it too expensive to work with. Kotlin on the other hand now has nice Jupyter support, which mostly covers REPL requirements.
Pros of Julia
- Fast Performance and Easy Experimentation22
- Designed for parallelism and distributed computation21
- Free and Open Source17
- Dynamic Type System16
- Lisp-like Macros16
- Calling C functions directly16
- Multiple Dispatch15
- Powerful Shell-like Capabilities9
- Jupyter notebook integration8
- REPL7
- String handling4
- Emojis as variable names4
- Interoperability3
Pros of R Language
- Data analysis83
- Graphics and data visualization62
- Free53
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Preferred Medium6
- Shiny interactive plots6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
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Cons of Julia
- Immature library management system5
- Slow program start4
- JIT compiler is very slow3
- Poor backwards compatibility3
- Bad tooling2
- No static compilation2
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