3 ms·

If you are running a script over and over (or using small modules to do organize things) it will be very fast and easy to work with. The compilation is just for

If you are running a script over and over (or using small modules to do organize things) it will be very fast and easy to work with. The compilation is just for the first time a script is run, not every time. It really isn't a barrier any more than, say, waiting for `library` commands in R.

bluenose69 8y ago
I don't think I've ever seen an R library() take more than about a second, and usually the action is complete as my finger is starting to raise from the 'return' key. When I tried "using Plots" in julia, it took several tens of seconds the first time, and several seconds in subsequent sessions. So, slower than R, but not terribly so. I suspect the real advantage of Julia is that it lets the analyst stick to a single language, without (as in the R case) having to write time-consuming components in C, C++, or Fortran.
- babahoyo 8y ago
  For me the appeal is 3-fold 1) I can contribute to widely used packages like DataFrames and HypothesisTests. I had never made a git commit before this and my only "real" programming was CS 101 in Java. The fact that I could get up and running so easily contributing is a testament to the language's ease of use 2) I think its tough to predict your computational needs at the start of a project. Sure everything can be done in `lme` in R at the outset, but if you need some new bootstrapping procedure that a reviewer wants you might be left connecting some high performance code to an existing, large, R-based codebase. That's tough. I think Julia makes that "refactoring" (if you can call it that) easy. 3) Hopefully Julia will open a lot of doors for me in the future in my research career. I will be able to write interesting simulation procedures that are otherwise too unweildy for the comparison R or Stata economist.

3 ms·

If you are running a script over and over (or using small modules to do organize things) it will be very fast and easy to work with. The compilation is just for

by babahoyo 8y ago

bluenose69 8y ago
I don't think I've ever seen an R library() take more than about a second, and usually the action is complete as my finger is starting to raise from the 'return' key. When I tried "using Plots" in julia, it took several tens of seconds the first time, and several seconds in subsequent sessions. So, slower than R, but not terribly so. I suspect the real advantage of Julia is that it lets the analyst stick to a single language, without (as in the R case) having to write time-consuming components in C, C++, or Fortran.
- babahoyo 8y ago
  For me the appeal is 3-fold 1) I can contribute to widely used packages like DataFrames and HypothesisTests. I had never made a git commit before this and my only "real" programming was CS 101 in Java. The fact that I could get up and running so easily contributing is a testament to the language's ease of use 2) I think its tough to predict your computational needs at the start of a project. Sure everything can be done in `lme` in R at the outset, but if you need some new bootstrapping procedure that a reviewer wants you might be left connecting some high performance code to an existing, large, R-based codebase. That's tough. I think Julia makes that "refactoring" (if you can call it that) easy. 3) Hopefully Julia will open a lot of doors for me in the future in my research career. I will be able to write interesting simulation procedures that are otherwise too unweildy for the comparison R or Stata economist.