If you just look at the main chapters thing looks pretty good. Julia has packages for regression, resampling, trees, SVM, PCA, clustering and so on. But when you start looking into the details of what the packages offer you find that the Julia offerings are very spars compared to what ISLR uses in R.
On Sunday, December 14, 2014 7:10:13 PM UTC+2, [email protected] wrote: > > That's interesting. What sort of stuff was not implemented? I would have > thought (by now) the coverage would be much higher than 30-40%... > > > On Sunday, December 14, 2014 11:07:05 AM UTC-5, Johan Sigfrids wrote: >> >> There is a ISLR package for R with a bunch of example datasets used in >> the book. Those datasets are also available in RDatasets.jl >> >> Doing the ISLR example in Julia would involve a lot of writing of >> functionality. Last summer I browsed through the statistics functionality >> available in Julia and something like 60-70% of the stuff used in ISLR >> isn't yet implemented. >> >> On Sunday, December 14, 2014 5:41:02 PM UTC+2, John Myles White wrote: >>> >>> This would be a great first project for someone interested in learning >>> Julia. >>> >>> FWIW, the RDatasets.jl repo doesn't have anything to do with ISRL -- >>> except insofar as ISRL decided to use common R datasets. >>> >>> -- John >>> >>> On Dec 14, 2014, at 10:36 AM, [email protected] wrote: >>> >>> I'm going through ISRL and find the book very useful. I see that someone >>> has loaded the data from the book: >>> >>> https://github.com/johnmyleswhite/RDatasets.jl >>> >>> >>> Someone has also taken the chapters in R and implemented in numpy: >>> >>> https://github.com/TomAugspurger/StatLearning/tree/master/python >>> >>> >>> The book is great, and I would love to see the examples implemented in >>> Julia... >>> >>> >>> >>> >>>
