Writing a book is a sacrifice. It takes a lot of time, represents a lot of missed opportunities, and does not (directly) pay very well. If you do a good job it may pay back in good-will, but producing a serious book is a great challenge.
In the end we worked very hard to organize and share a lot of good material in what we feel is a very readable manner. But I think the first-author may have been signaling and preparing a bit earlier than I was aware we were writing a book. Please read on to see some of her prefiguring work. Continue reading Did she know we were writing a book?
Recently I whined/whinged or generally complained about a few sharp edges in some powerful R systems.
In each case I was treated very politely, listened to, and actually got fixes back in a very short timeframe from volunteers. That is really great and probably one of the many reasons R is a great ecosystem.
With our recent publication of “Can you nest parallel operations in R?” we now have a nice series of “how to speed up statistical computations in R” that moves from application, to larger/cloud application, and then to details.
In our last article on the algebra of classifier measures we encouraged readers to work through Nina Zumel’s original “Statistics to English Translation” series. This series has become slightly harder to find as we have use the original category designation “statistics to English translation” for additional work.
To make things easier here are links to the original three articles which work through scores, significance, and includes a glossery.
A lot of what Nina is presenting can be summed up in the diagram below (also by her). If in the diagram the first row is truth (say red disks are infected) which classifier is the better initial screen for infection? Should you prefer the model 1 80% accurate row or the model 2 70% accurate row? This example helps break dependence on “accuracy as the only true measure” and promote discussion of additional measures.
Some readers have been having a bit of trouble using devtools to install WVPlots (announced here and used to produce some of the graphs shown here). I thought I would write a note with a few instructions to help.
These are things you should not have to do often, and things those of us already running R have stumbled through and forgotten about. These are also the kind of finicky system dependent non-repeatable interactive GUI steps you largely avoid once you have a scriptable system like fully R up and running. Continue reading Installing WVPlots and “knitting R markdown”
Our publisher Manning Publications is celebrating the release of a new data science in Python title Introducing Data Science by offering it and other Manning titles at half off until Wednesday, May 18.
As part of the promotion you can also use the supplied discount code mlcielenlt for half off some R titles including R in Action, Second Edition and our own Practical Data Science with R. Combine these with our half off code (C3) for our R video course Introduction to Data Science and you can get a lot of top quality data science material at a deep discount.
I’ve been editing a two-part three-part series Nina Zumel is writing on some of the pitfalls of improperly applied principal components analysis/regression and how to avoid them (we are using the plural spelling as used in following Everitt The Cambridge Dictionary of Statistics). The series is looking absolutely fantastic and I think it will really help people understand, properly use, and even teach the concepts.
The series includes fully worked graphical examples in R and is why we added the ScatterHistN plot to WVPlots (plot shown below, explained in the upcoming series).
Frankly the material would have worked great as an additional chapter for Practical Data Science with R (but instead everybody is going to get it for free).
Please watch here for the series.
The complete series is now up: