Win-Vector LLC has recently been teaching how to use
R with big data through
sparklyr. We have also been helping clients become productive on
R/Spark infrastructure through direct consulting and bespoke training. I thought this would be a good time to talk about the power of working with big-data using
R, share some hints, and even admit to some of the warts found in this combination of systems.
The ability to perform sophisticated analyses and modeling on “big data” with
R is rapidly improving, and this is the time for businesses to invest in the technology. Win-Vector can be your key partner in methodology development and training (through our consulting and training practices).
J. Howard Miller’s, 1943.
The field is exciting, rapidly evolving, and even a touch dangerous. We invite you to start using
R and are starting a new series of articles tagged “R and big data” to help you produce production quality solutions quickly.
Please read on for a brief description of our new articles series: “R and big data.” Continue reading New series: R and big data (concentrating on Spark and sparklyr)
In this article I will discuss array indexing, operators, and composition in depth. If you work through this article you should end up with a very deep understanding of array indexing and the deep interpretation available when we realize indexing is an instance of function composition (or an example of permutation groups or semigroups: some very deep yet accessible pure mathematics).
A permutation of indices
In this article I will be working hard to convince you a very fundamental true statement is in fact true: array indexing is associative; and to simultaneously convince you that you should still consider this amazing (as it is a very strong claim with very many consequences). Array indexing respecting associative transformations should not be a-priori intuitive to the general programmer, as array indexing code is rarely re-factored or transformed, so programmers tend to have little experience with the effect. Consider this article an exercise to build the experience to make this statement a posteriori obvious, and hence something you are more comfortable using and relying on.
R‘s array indexing notation is really powerful, so we will use it for our examples. This is going to be long (because I am trying to slow the exposition down enough to see all the steps and relations) and hard to follow without working examples (say with
R), and working through the logic with pencil and a printout (math is not a spectator sport). I can’t keep all the steps in my head without paper, so I don’t really expect readers to keep all the steps in their heads without paper (though I have tried to organize the flow of this article and signal intent often enough to make this readable). Continue reading On indexing operators and composition
Continue reading dplyr in Context
R users often come to the false impression that the popular packages
tidyr are both all of
R and sui generis inventions (in that they might be unprecedented and there might no other reasonable way to get the same effects in
R). These packages and their conventions are high-value, but they are results of evolution and implement a style of programming that has been available in
R for some time. They evolved in a context, and did not burst on the scene fully armored with spear in hand.
I have written about referential transparency before. In this article I would like to discuss “leaky abstractions” and why
wrapr::let() supplies a useful (but leaky) abstraction for
Continue reading Why to use wrapr::let()