[babahoyo]

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.