Julia or R for Analytical April

I already have experience in Python so for Analytical April which language i choose Julia or R

It is always controversial to talk about a “best” language, but for what it’s worth:

  • If you are interested in physical sciences and/or machine learning: Julia
  • if social sciences and statistics: R

Honestly, the best scientific language is often the one your colleagues are using. Better package support, easier to get advice.

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What are your goals, @suyash-1234? What would you like to gain from getting to know Julia or R a bit?

Above, @colinleach considers possible applications. However, other possible goals exist, such as broadening your horizons on what programming languages can be, or learning how they work internally, or gaining a different perspective on programming generally.

Strong agree with Colin - it is controversial to talk about ‘best’, and much better to talk about ‘use case’.

R is beloved by many - especially those who work in epidemiology, biostatistics, social science, and virology. As an example, Johns Hopkins University is a really big booster of R.

Julia is growing very very fast, and it getting lots of love from the likes of MIT’s Applied math department, NASA, Disney, and Capital One.

Learning-wise, Julias syntax feels closer to Python’s at the start - but Julia allows for much more elegant handling of matrices and mathematical formulas within the core language. To do those things in Python, you need third-partly libraries like Numpy and SymPy, among others.

Learning-wise, R is further away from what I’d call a general-purpose programming language. But it has some really really nice tools for working with statistical distributions and for visualizing and telling stories with data. For what R gives you (even without the Tidyverse), you have to install Numpy, Matplotlib, Pandas, and Seaborn in Python.

RStudio is a really nice IDE that also supports using Python and Markdown from within the main interface.

What’s really fun is that all three languages are interoperable.

From R:

From Julia:

From Python:

Which means you can explore things by starting with what you know in Python and ‘porting’ to or ‘borrowing from’ the others. :smile:


I would think that at the level of solving Exercism exercises all three languages would be roughly equivalent. So choice would depend on what you want to do with them in the future, if anything.

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Julia has a pretty awesome type system, if you are choosing between the two I’d go with Julia just because of that.

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@suyash-1234 What might be useful is to watch the introduction videos I did for both tracks:

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