Julia is by far the favorite language I have written code in. It is extremely expressive, while also being easy to read. Most design decisions are spot on. For

Julia is by far the favorite language I have written code in. It is extremely expressive, while also being easy to read. Most design decisions are spot on. For example, the language has syntactic sugar, but not too much. Everything in the base library makes sense and seems to be there for a purpose. Other niceties are the meta-programming capabilities, which allow for things like inspecting the llvm code and printing a variable name `x` plus output by only typing `@show x`. Then there is the fact that anonymous functions actually look like how you would describe a math function! (That is, `f(x) = 2x` is a valid function, as is `f(x) = 2π`.) However, there is one thing I do not like at all. That is the loading time of packages. When starting julia and running `@time using DataFrames` it takes about 38 seconds when recompiling some stale cache. If all caches are good, the the load times for some common packages still add up to 1.1 + 4.5 + 1.1 seconds according to `@time using Test; @time using CSV; @time using Dates`. Therefore, nowadays I prefer to use R. For most of my use cases R outperforms Julia by a factor 10.