Re: [R] Learning advanced R
On 2018-03-14 11:52, Rich Shepard wrote: On Wed, 14 Mar 2018, Duncan Murdoch wrote: I'm all for learning more languages and using the one that's best for each job, but for people who don't know Python, it would be helpful to list the aspects in which it excels. When should an R user choose to write something in Python instead? Duncan, "Best" is subjective, but my view is the language most comfortable and familiar to the developer/analyst should be the one used. In my environmental consulting business I use both R and Python. While Python has support for many statistical models I'm more comfortable with the ones available in R. For spatial analyses (separate from spatial statistics) I've used GRASS for > 20 years and it heavily uses Python. I also use Python (along with emacs, awk, sed, and grep) for cleaning and organizing data. For writing, I use LaTeX (a markup language) and the LyX GUI front end. Python has a lot of support for scientific and financial analyses, as does R. Considering there are a gazillion programming languages available (and used for essential applications, such as GnuCash (written in guile, a scheme variant) which I use for business and personal bookkeeping, picking the "best" one is strictly a personal matter. I prefer emacs, my system and network admin friends prefer vi. In linux, at least, there are so many options for doing a task that sometimes it's difficult to decide which to use in a given situation. If the languages you know do all you need then learn a new one only if it's to scratch an itch. :-) My software development productivity increased by a factor of maybe 30 by using first S-Plus then R, including writing R packages, then RStudio and writing Rmarkdown vignettes. 1. I started writing Fortran in 1963. I've written assembly language for multiple machines, Cobol, Lisp, and other language. I started using S-Plus in the early 1990s and abandoned it for R when I needed "debug" for some S-Plus code. Developing R packages improved my software development productivity by a factor of 10, because the discipline of creating unit tests in "\examples" made it so much easier to debug and maintain -- AND share with others. 2. I've also written some Python, though not much. I used Emacs until I found RStudio. Vi and Emacs are not tools you can give to someone, who is only marginally computer literate and expect them to be productive in a reasonable period of time. By contrast, if someone knows enough to be able to install R and RStudio, I can give them some R code and be confident that they will get something useful from the experience in a relatively short period of time. You can't do that with vi and Emacs unless they already know those applications. 3. Recently, I've started writing RMarkdown vignettes, and that further increased my productivity. 3.1. Two years ago, I told I client I was going to prepare and Rmarkdown vignette to document what I did with their data. My sales guy said absolutely, we were NOT going to give the client an Rmarkdown vignette. I spent a week analyzing the data and 6 months answering questions from the team mostly by pointing them to certain lines in the vignette, occasionally by extending it. In the middle of that, we learned that the client required our analysis to be verifiable. After that, the vignette became a primary deliverable. 3.2. More recently, another client asked me to explain principal components. This client was moderately facile with software but not with R nor vector spaces. I gave him an Rmarkdown vignette that included a principal components on some data he gave me done both with a single command and step by step supplemented with a simple discussion of a one-dimensional subspace of two-dimensional space. He was happy. 4. I invite all to review and improve the discussion in the Wikipedia article on "Software repository". This a table with a discussion of "Selected repositories", much of which I wrote 8 years ago. It's heavily biased toward CRAN, because that's what I know the best, and I've so far been unable to find anyone with the expertise and interest in improving it. This article averaged 290 views per day over the past 90 days, over 26,000 in the past 3 months. If you can improve that article, an audience that size might be worth talking to. Spencer Graves Best regards, Rich __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/lis
Re: [R] Learning advanced R
On Wed, 14 Mar 2018, Duncan Murdoch wrote: I'm all for learning more languages and using the one that's best for each job, but for people who don't know Python, it would be helpful to list the aspects in which it excels. When should an R user choose to write something in Python instead? Duncan, "Best" is subjective, but my view is the language most comfortable and familiar to the developer/analyst should be the one used. In my environmental consulting business I use both R and Python. While Python has support for many statistical models I'm more comfortable with the ones available in R. For spatial analyses (separate from spatial statistics) I've used GRASS for > 20 years and it heavily uses Python. I also use Python (along with emacs, awk, sed, and grep) for cleaning and organizing data. For writing, I use LaTeX (a markup language) and the LyX GUI front end. Python has a lot of support for scientific and financial analyses, as does R. Considering there are a gazillion programming languages available (and used for essential applications, such as GnuCash (written in guile, a scheme variant) which I use for business and personal bookkeeping, picking the "best" one is strictly a personal matter. I prefer emacs, my system and network admin friends prefer vi. In linux, at least, there are so many options for doing a task that sometimes it's difficult to decide which to use in a given situation. If the languages you know do all you need then learn a new one only if it's to scratch an itch. :-) Best regards, Rich __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Learning advanced R
On 14/03/2018 12:07 PM, Rich Shepard wrote: On Wed, 14 Mar 2018, Barry Rowlingson wrote: Depending on your application, I'm not sure there's much point in being an "advanced R programmer" these days. Become an adequate R programmer, and learn C++ and Rcpp. Do basic data mashing in R, then do all your intensive stuff in C++ with Rcpp. Eventually you'll probably get to the point where you can express yourself in C++ as fast as you can in interpreted R, with the bonus of C++ speed, type-safety etc. Barry, Allow me to offer an alternative to C++: Python. Compiled languages are faster than interpreted ones, but unless you're doing time-critical computations it really does not matter. R and Python provide proven abilities in a broad range of applications and with today's hardware the analytical/modeling code is not likely to be the limiting factor. I'm all for learning more languages and using the one that's best for each job, but for people who don't know Python, it would be helpful to list the aspects in which it excels. When should an R user choose to write something in Python instead? Duncan Murdoch __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Learning advanced R
On Wed, 14 Mar 2018, Barry Rowlingson wrote: Depending on your application, I'm not sure there's much point in being an "advanced R programmer" these days. Become an adequate R programmer, and learn C++ and Rcpp. Do basic data mashing in R, then do all your intensive stuff in C++ with Rcpp. Eventually you'll probably get to the point where you can express yourself in C++ as fast as you can in interpreted R, with the bonus of C++ speed, type-safety etc. Barry, Allow me to offer an alternative to C++: Python. Compiled languages are faster than interpreted ones, but unless you're doing time-critical computations it really does not matter. R and Python provide proven abilities in a broad range of applications and with today's hardware the analytical/modeling code is not likely to be the limiting factor. Regards, Rich __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Learning advanced R
Depending on your application, I'm not sure there's much point in being an "advanced R programmer" these days. Become an adequate R programmer, and learn C++ and Rcpp. Do basic data mashing in R, then do all your intensive stuff in C++ with Rcpp. Eventually you'll probably get to the point where you can express yourself in C++ as fast as you can in interpreted R, with the bonus of C++ speed, type-safety etc. On Wed, Mar 14, 2018 at 8:13 AM, Eric Berger wrote: > Bert's suggestion is good as a pointer to a variety of resources. > Sticking to the book format there are two of Hadley Wickham's books, which > have the advantage that they are freely available. > You can either read them online or download the source from github and > create your own copy (which you can then print, if desired.) > 1. "R for Data Science" > online: http://r4ds.had.co.nz/ > github: https://github.com/hadley/r4ds > 2. "Advanced R" > online: https://adv-r.hadley.nz/ > github: https://github.com/hadley/adv-r > > Best, > Eric > > > > On Wed, Mar 14, 2018 at 12:13 AM, Rich Shepard > wrote: > > > On Tue, 13 Mar 2018, Mark Leeds wrote: > > > > See Hadley's advanced R > >> > > > > +1 A very well writte, highly useful book. Recommended. > > > > Rich > > > > > > __ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/posti > > ng-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Learning advanced R
Bert's suggestion is good as a pointer to a variety of resources. Sticking to the book format there are two of Hadley Wickham's books, which have the advantage that they are freely available. You can either read them online or download the source from github and create your own copy (which you can then print, if desired.) 1. "R for Data Science" online: http://r4ds.had.co.nz/ github: https://github.com/hadley/r4ds 2. "Advanced R" online: https://adv-r.hadley.nz/ github: https://github.com/hadley/adv-r Best, Eric On Wed, Mar 14, 2018 at 12:13 AM, Rich Shepard wrote: > On Tue, 13 Mar 2018, Mark Leeds wrote: > > See Hadley's advanced R >> > > +1 A very well writte, highly useful book. Recommended. > > Rich > > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posti > ng-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Learning advanced R
On Tue, 13 Mar 2018, Mark Leeds wrote: See Hadley's advanced R +1 A very well writte, highly useful book. Recommended. Rich __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Learning advanced R
See here for some suggestions: https://www.rstudio.com/online-learning/#R Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Mar 13, 2018 at 2:31 PM, Mark Leeds wrote: > See Hadley's advanced R along Thomas Mailund's books. I haven't gone > through them carefully but they both > seem (from what I've looked at ) to be the best ones for that. Mentions of > others are appreciated. > > > > > On Tue, Mar 13, 2018 at 5:26 PM, Nik Tuzov > wrote: > > > > > Hello: > > > > Could you please suggest the best way to become an "advanced" R > programmer. > > I went through "R for dummies" by de Vries and Meys and I can see two > ways > > to proceed: > > > > 1) Get a more advanced textbook. E.g. could you recommend Gentleman, > > "R for Bioinformatics"? > > > > 2) Because textbooks are limited and become obsolete fast, I can focus on > > learning state-of-the-art packages, > > but for that I need to find a list of most useful general purpose > packages > > (foreach, doParallel, etc) that is > > updated in real time. Does such list exist? > > > > Your recommendations are very welcome. > > > > Thanks, > > Nik > > > > __ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/ > > posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Learning advanced R
See Hadley's advanced R along Thomas Mailund's books. I haven't gone through them carefully but they both seem (from what I've looked at ) to be the best ones for that. Mentions of others are appreciated. On Tue, Mar 13, 2018 at 5:26 PM, Nik Tuzov wrote: > > Hello: > > Could you please suggest the best way to become an "advanced" R programmer. > I went through "R for dummies" by de Vries and Meys and I can see two ways > to proceed: > > 1) Get a more advanced textbook. E.g. could you recommend Gentleman, > "R for Bioinformatics"? > > 2) Because textbooks are limited and become obsolete fast, I can focus on > learning state-of-the-art packages, > but for that I need to find a list of most useful general purpose packages > (foreach, doParallel, etc) that is > updated in real time. Does such list exist? > > Your recommendations are very welcome. > > Thanks, > Nik > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Learning advanced R
Hello: Could you please suggest the best way to become an "advanced" R programmer. I went through "R for dummies" by de Vries and Meys and I can see two ways to proceed: 1) Get a more advanced textbook. E.g. could you recommend Gentleman, "R for Bioinformatics"? 2) Because textbooks are limited and become obsolete fast, I can focus on learning state-of-the-art packages, but for that I need to find a list of most useful general purpose packages (foreach, doParallel, etc) that is updated in real time. Does such list exist? Your recommendations are very welcome. Thanks, Nik __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.