I think Julia is better designed than R. But it lacks R's ecosystem, so Julia is a tougher sell.
I haven't done a lot with Julia, so I don't know if it's easier to teach Julia to novices than Python or R.
I haven't done a lot with Julia, so I don't know if it's easier to teach Julia to novices than Python or R.
For teaching, I think that Julia indexing (for example: vector[2:end-1]) is easier to explain than numpy (vector[1:-1]). On the other hand, I like python's plain English operators and/or versus && / || in Julia.
Also loops tends to be more readable than vertorised code in some circumstances (e.g. computing the Laplacian by finite difference). In Julia, loop and vectorized code are both quite efficient, while in python and R, one has to vectorize the code.
The community is great. But small. For a lot of situations, I'd be hesitant to invest in Julia, because I don't know if the community will stay that way or if it fades away.
While in absolute numbers, we may be at 20% of R or Python communities, I am always curious to understand what people mean when they say the community is too small. What would be a signal that a particular community is big enough?
Essentially the base ecosystem of a language is driven by a core group of contributors and the derivation an ability of that group matters more than most other factors. When doing scientific and or data science I personally care more about the core quality and what the platform enables. Lately I’ve considered learning R as it has a lot of well done says which simply aren’t available in Python, and aren’t ready yet in Julia. Last time I tried to calculate a confidence interval in Python for an obscure probability function I ended up wanting to pull out my air in frustration. There’s libraries that kind of handle it in Python but they are (we’re?) nigh impossible to modify or re-use for a generalized case. Much less getting a proper covariance matrix with enough documentation to know what to do with it. I used R examples to figure out the correct maths. R’s NSE seems appealing in allowing generalized re-use. I’ve had similar ability to re-use library features in Julia for solving problems outside that libraries initial scope.
1: https://en.wikipedia.org/wiki/Steven_G._Johnson 2: https://discourse.julialang.org/t/steven-johnson-as-a-juliac... 3: https://github.com/STOR-i/GaussianProcesses.jl
This enables you to also select libraries: a standard float type will use blas for matrix ops; a gpu float type will use cuda.
In the areas I work in (scientific computing and scientific machine learning), you can really only find the packages in Julia while R and Python's ecosystems are quite lacking. R has stats and Python has ML, but the rest of the scientific ecosystems there just aren't as complete.