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Pro Nice regular syntax
Julia code is easy to read and avoid a lot of unnecessary special symbols and fluff. It uses newline to end statements and "end" to end blocks so there is no need for lots of semicolons and curly braces. It is regular in that unless it is a variable assignment, function name always comes...
Pro Fast computation
Certain benchmarks suggest it is capable of outperforming Python and even C (in certain situations where FORTRAN libraries can be utilized).
Con Young language with limited support
Julia was released in 2012. Due to its short existence, there is a limited amount of support for the language. Very few libraries exist as of yet, and the community is still quite small (though growing quickly).
Con Tooling & libs not yet up to par with others like R or Python
Lacks tools like R's Shiny for easy web applications/dashboards. The library ecosystem also doesn't have the breadth yet of R or Python (which is normal). The dataframe/datatable in Julia & data wrangling isn't also up to par yet with R data.frame/data.table/tibble & dplyr.
Pro Full programming language, unlike R
Allows for more rapid deployment of solutions.
Pro Rich choice of high quality libraries
Be it data manipulation or plotting there is always a wide variety at CRAN available.
Pro RStudio IDE
free lightweight easy to use clean UI
Con The library ecosystem (mostly CRAN but also github, BioConductor & others) is rather fragmented
There are over 10000 R packages and rising and there are many packages with overlapping functionality. It's not always clear which package is "best" for your needs. The situation is getting better thanks to Hadley Wickham who has rewritten a lot of core functionality from R base &
Pro Maximum performance
Fortran compilers are very mature--not even C is faster.
Con No library ecosystem for modern data science: exploratory data analysis, statistics, machine learning
Fortran is good for pure number crunching on supercomputers. The available open source libraries are mostly in domains like physics or climate science and they are mostly about things like simulations or partial differential equations solving. So it's more suitable for going from models to dat...
Pro Matlab compatibility
The languages are so similar that it's easy to port Matlab libraries.
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