Publications by John Mount
Some R Guides: tidyverse and data.table Versions
Saghir Bashir of ilustat recently shared a nice getting started with R and tidyverse guide. In addition they were generous enough to link to Dirk Eddelbuette’s later adaption of the guide to use data.table. This type of cooperation and user choice is what keeps the R community vital. Please encourage it. (Heck, please insist on it!) Related...
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Piping into ggplot2
In our wrapr pipe RJournal article we used piping into ggplot2 layers/geoms/items as an example. Being able to use the same pipe operator for data processing steps and for ggplot2 layering is a question that comes up from time to time (for example: Why can’t ggplot2 use %>%?). In fact the primary ggplot2 package author wishes that magrittr pipi...
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Quasiquotation in R via bquote()
In August of 2003 Thomas Lumley added bquote() to R 1.8.1. This gave R and R users an explicit Lisp-style quasiquotation capability. bquote() and quasiquotation are actually quite powerful. Professor Thomas Lumley should get, and should continue to receive, a lot of credit and thanks for introducing the concept into R. In fact bquote() is already...
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Designing Transforms for Data Reshaping with cdata
Authors: John Mount, and Nina Zumel 2018-10-25 As a followup to our previous post, this post goes a bit deeper into reasoning about data transforms using the cdata package. The cdata packages demonstrates the “coordinatized data” theory and includes an implementation of the “fluid data” methodology for general data re-shaping. cdata adher...
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Conway’s Game of Life in R: Or On the Importance of Vectorizing Your R Code
R is an interpreted programming language with vectorized data structures. This means a single R command can ask for very many arithmetic operations to be performed. This also means R computation can be fast. We will show an example of this using Conway’s Game of Life. Conway’s Game of Life is one of the most interesting examples of cellul...
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Use Pseudo-Aggregators to Add Safety Checks to Your Data-Wrangling Workflow
One of the concepts we teach in both Practical Data Science with R and in our theory of data shaping is the importance of identifying the roles of columns in your data. For example, to think in terms of multi-row records it helps to identify: Which columns are keys (together identify rows or records). Which columns are data/payload (are consider...
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The blocks and rows theory of data shaping
We have our latest note on the theory of data wrangling up here. It discusses the roles of “block records” and “row records” in the cdata data transform tool. With that and the theory of how to design transforms, we think we have a pretty complete description of the system. Related To leave a comment for the author, please follow the ...
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coalesce with wrapr
coalesce is a classic useful SQL operator that picks the first non-NULL value in a sequence of values. We thought we would share a nice version of it for picking non-NA R with convenient operator infix notation wrapr::coalesce(). Here is a short example of it in action: library("wrapr") NA %?% 0 # [1] 0 A more substantial application is the f...
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R tip: Make Your Results Clear with sigr
R is designed to make working with statistical models fast, succinct, and reliable. For instance building a model is a one-liner: model <- lm(Petal.Length ~ Sepal.Length, data = iris) And producing a detailed diagnostic summary of the model is also a one-liner: summary(model) # Call: # lm(formula = Petal.Length ~ Sepal.Length, data = iris) # #...
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How to de-Bias Standard Deviation Estimates
This note is about attempting to remove the bias brought in by using sample standard deviation estimates to estimate an unknown true standard deviation of a population. We establish there is a bias, concentrate on why it is not important to remove it for reasonable sized samples, and (despite that) give a very complete bias management solution. ...
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