I have a couple of public appearances coming up soon.
R has a very powerful array slicing ability that allows for some very slick data processing.
The R package cdata now has version 0.7.0 available from CRAN.
cdata is a data manipulation package that subsumes many higher order data manipulation operations including pivot/un-pivot, spread/gather, or cast/melt. The record to record transforms are specified by drawing a table that expresses the record structure (called the “control table” and also the link between the key concepts of row-records and block-records).
What can be quickly specified and achieved using these concepts and notations is amazing and quite teachable. These transforms can be run in-memory or in remote database or big-data systems (such as Spark).
The concepts are taught in Nina Zumel’s excellent tutorial.
And in John Mount’s quick screencast/lecture.
The 0.7.0 update adds local versions of the operators in addition to the Spark and database implementations. These methods should now be a bit safer for in-memory complex/annotated types such as dates and times.
R has a lot of under-appreciated super powerful functions. I list a few of our favorites below.
Atlas, carrying the sky. Royal Palace (Paleis op de Dam), Amsterdam.
Photo: Dominik Bartsch, CC some rights reserved.
match_order() to Align Datamatch_order() to Align DataR tip. Use wrapr::match_order() to align data.
Let’s consider piping in R both using the magrittr package and using the wrapr package.
Continue reading magrittr and wrapr Pipes in R, an Examination
Four years ago today authors Nina Zumel and John Mount received our author’s copies of Practical Data Science with R!
Continue reading Four Years of Practical Data Science with R
R tip: first organize your tasks in terms of data, values, and desired transformation of values, not initially in terms of concrete functions or code.
I know I write a lot about coding in R. But it is in the service of supporting statistics, analysis, predictive analytics, and data science.
Rwithout data is like going to the theater to watch the curtain go up and down.(Adapted from Ben Katchor’s Julius Knipl, Real Estate Photographer: Stories, Little, Brown, and Company, 1996, page 72, “Excursionist Drama 2”.)
Usually you come to R to work with data. If you think and plan in terms of data and values (including introducing more data to control processing) you will usually work in much faster, explainable, and maintainable fashion.
Excited to see our new Hangul/Korean edition of “Practical Data Science with R” by Nina Zumel, John Mount, translated by Daekyoung Lim.
Continue reading Hangul/Korean edition of Practical Data Science with R!