Looks really nice. I'm a heavy Python/Stata user, but I'm seriously thinking about transitioning, given all the amazing work in the hadleyverse.
Also, RMarkdown looks incredibly well thought out
R for Data Science
21–30 of 76 posts
Re: R for Data Science
#22Looks really nice. I'm a heavy Python/Stata user, but I'm seriously thinking about transitioning, given all the amazing work in the hadleyverse. Also, RMarkdown looks incredibly well thought out
cough tidyverse cough
Re: R for Data Science
#23I'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.
Are there solutions for the exercises? Lot of the exercises, especially in the exploratory data analysis part are "why is blah?" or "is there a relationship in blah?" I think I know the answers, but it would be nice to be able to check if I see what I'm supposed to se in the data.
No, but I'll probably crowd source when the book is final.
Re: R for Data Science
#24Earlier quoted context omitted.
I don't see how there would be a need for that. The code will work in Jupyter Notebooks just as it will work in a different IDE. I came from Python using iPython. I missed them for the first few weeks, but now I can't switch from RStudio. It really is just such a great tool for data science.
The advantage of Jupyter notebooks over RStudio is having text and code in the same place. Personally, I find that being able to run+modify the code in a textbook is much more informative than simply reading the syntax (for example - https://github.com/CamDavidsonPilon/Probabilistic-Programmin... ). Sure, I could just copy and paste from the website into an IDE, but notebooks are a more natural way of communicating c…
The source is available in https://github.com/hadley/r4ds, and works exactly as you describe when using the preview edition of RStudio (and indeed that's how I write the book)
Re: R for Data Science
#25Seems interesting. Quick question: Some background on myself first. I am a financial consultant (only 1 year since graduating) and am planning to do a PhD in Accounting in the next 3 years. Currently working through the GMAT, but once that is complete, I will find myself with 2 or so years to do things that will help prepare me for research. One thing I have considered is taking a course/reading books on data science…
Yes
Re: R for Data Science
#26Earlier quoted context omitted.
There's a large number of such books, though none that are as authoritative with respect to Python (this is a statement about the size of Python's community vs. R, not necessarily about the authors): - via Wes McKinney, creator of pandas (which makes Python about as close to R as you can get): https://www.amazon.com/Python-Data-Analysis-Wrangling-IPytho... - http://joelgrus.com/2015/04/26/data-science-from-scratch-fi…
What would you recommend for visualisation?
I actually have no idea about that. I don't think there's an equivalent to R's base graphics, so that would seem to make matplotlib the closest thing to a standard -- seaborn [0], which I've seen used a lot lately for more advanced dataviz, lives atop it, but it's also relatively new.
People seem to have conflicted feelings about matplotlib, maybe because of its origin in MATLAB? Not that Matlab itself is bad, but I think the decision to make matplotlib's API comfortable for MATLAB users seems to cause confusion to contemporary users, even before the usual 2.x vs 3.x issues (matplotlib ported to 3.x a few years ago but many users still write Python in the 2.x style.)
Anecdotally, I feel like I see advice like "Just use plotly" more than I see recommendations to actually learn matplotlib. I actually gave up on matplotlib until I stumbled upon this comprehensive tutorial, which covers the basics and many elaborate use cases. If there's a book that does it better, I haven't heard about it:
http://www.labri.fr/perso/nrougier/teaching/matplotlib/
The matplotlib site itself is chockful of well-documented examples, but some of them seem to be significantly more verbose than they need to be. My impression is that the library is stable/ubiquitous enough that there isn't a big movement to overhaul things. Last time I looked at the API changes for v2.0 [1] (1.5.3 is stable), most of the changes had to do with default styles and stylesheets, which is non-trivial given the number of people who use ggplot2 because it "just works"
[0] https://stanford.edu/~mwaskom/software/seaborn/
[1] http://matplotlib.org/devdocs/users/dflt_style_changes.html
Re: R for Data Science
#27Hadley, can you share a bit more about your plans for modelr and what need(s) the package will be designed to solve? Congrats on your book btw, I've been reading it for a few weeks and it's quite simply excellent.
Re: R for Data Science
#28Seems interesting. Quick question: Some background on myself first. I am a financial consultant (only 1 year since graduating) and am planning to do a PhD in Accounting in the next 3 years. Currently working through the GMAT, but once that is complete, I will find myself with 2 or so years to do things that will help prepare me for research. One thing I have considered is taking a course/reading books on data science…
It looks like it starts at a low enough level that you should be fine without a programming background.
Though they suggest Garrett's book [1] as a companion to R for Data Science in the Prerequisites section [2].
> And is R the appropriate language to learn?
I'd think so. Possibly either R or Python, or both (if you want to get beyond Excel).
[1] https://www.amazon.com/dp/1449359019 [2] http://r4ds.had.co.nz/intro.html
Re: R for Data Science
#29Earlier quoted context omitted.
The advantage of Jupyter notebooks over RStudio is having text and code in the same place. Personally, I find that being able to run+modify the code in a textbook is much more informative than simply reading the syntax (for example - https://github.com/CamDavidsonPilon/Probabilistic-Programmin... ). Sure, I could just copy and paste from the website into an IDE, but notebooks are a more natural way of communicating c…
The source is available in https://github.com/hadley/r4ds , and works exactly as you describe when using the preview edition of RStudio (and indeed that's how I write the book)
I just saw this the other day! This wrapper around Pandoc to make life our lifes easier (tidyr)? I just went through the process of making a markdown file to an epub before I saw this, wish I saw this earlier.
Re: R for Data Science
#30I'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.
Great book, I'm getting a lot out of the site and I'm looking forward to the release. Thanks!
I understand there is always one more library or topic that could be included...
.. but with that acknowledged, what do you think of sqldf as an alternative to dplyr? You mention that dplyr is a bit easier (within the context of being specialized for data analysis). I'd have trouble weighting in because I don't use R all that much, but I do really like the python "equivalent" pandasql.
Also, I've used SQL for a long time, so I'd have trouble at this point really knowing what's "easier" for someone new to both, but I do often find it easier to use SQL than do data frame operations in pandas. dplyr seems to be a closer cousin to standard SQL, so the difference might not be quite as great.