Concerning "Data Science", the problem is that collecting data and conjecturing about what is going on, is not exactly science. You would need to experimentally test things, and look at that kind of data instead.
R for Data Science
71–76 of 76 posts
Re: R for Data Science
#72I'm the author, and I'm happy to answer any questions. The book should be in print by (hopefully) the end of this year, or definitely by Jan 2017. The content will not change significantly, but there is will be minor fixes and a lot of proof reading.
Trivial, self-serving question: is there a library for generating the diagram of table relationships here (13.2 nycflights13) http://r4ds.had.co.nz/relational-data.html And of course, thanks for another great book, it's helpful for learning R but I'm always enlightened by how thoroughly you explain the general concepts (e.g. Relational data and joins). Have heard a few people on faculty speak enthusiastically about t…
As far as I recall, the MySQL Workbench has a pretty useful ER generation tool builtin.
Re: R for Data Science
#73I'm the author, and I'm happy to answer any questions. The book should be in print by (hopefully) the end of this year, or definitely by Jan 2017. The content will not change significantly, but there is will be minor fixes and a lot of proof reading.
Hey Hadley. Huge fan of your work! Many of the libraries you have authored or co-authored have had a big influence on how I think about building tools. I looks forward to getting a hard copy of the book! I have a bit of a nitpick about chapter 13 on "relational data", in which I believe you are consistently misusing the technical term "relation" to refer to the relationship between two data sets. In the context of re…
That is technically correct (the best kind of correct ;) but I think for newcomers it gives a better sense of the spirit of relational data.
Re: R for Data Science
#74Earlier quoted context omitted.
I don't think modelr is going to change significantly in the future. It solved a pressing problem (fitting models as part of a pipeline) so I could teach modelling using the same interface as everything else in the book. However, the modelling infrastructure in R is generally showing it's age, and thinking about how to make modelling easier is something that I will be working on in the coming months.
I saw the vctrs package repo the other day on your GitHub. What's your plan with that? I guess your covering all of R's base data types (dplyr:data frames, purrr:lists, forcats:factors, vctrs:vectors)? Also do you plan on developing further functional programming packages?
Yes. You can read the issues for what the package will start with.
No plans for more FP packages in the near future, although I do want to add multicore and progress bars to purrr.
Re: R for Data Science
#75Earlier quoted context omitted.
Just curious, but why crowd source? You're the author, I assume you wrote the questions, didn't you solve them when you wrote them?
I love your sweet naivety about the process of writing exercises :P
The proof is trivial and is left as an exercise for the publisher ;)
Re: R for Data Science
#76Earlier quoted context omitted.
Personally I work in macro and fixed income market analysis (strategist), and I can heartily recommend R as your first language. Indeed, coming from a CS background, I first applied Python to many problems, and resisted R which was not a "grown up" programming language, in my opinion (some would make the same accusation on Python). However I dipped my toe in the water one day because R had a Bloomberg terminal add in…
Thanks for the replies everyone. I'll definitely save this link and pick up the book when it comes out!
If you want to attain a deeper understanding of coding, I suggest you try another language once you get the working basics of R. Maybe some basic MOOC on Python or C. You'll understand concepts of CS that are hard to grasp by learning only R.