If you’ve read our previous R Tip on using sigr with linear models, you might have noticed that the lm() summary object does in fact carry the R-squared and F statistics, both in the printed form: model_lm

If you’ve read our previous R Tip on using sigr with linear models, you might have noticed that the lm() summary object does in fact carry the R-squared and F statistics, both in the printed form: model_lm

In my previous post, I showed how to use cdata package along with ggplot2‘s faceting facility to compactly plot two related graphs from the same data. This got me thinking: can I use cdata to produce a ggplot2 version of a scatterplot matrix, or pairs plot? A pairs plot compactly plots every (numeric) variable in … Continue reading Scatterplot...

In between client work, John and I have been busy working on our book, Practical Data Science with R, 2nd Edition. To demonstrate a toy example for the section I’m working on, I needed scatter plots of the petal and sepal dimensions of the iris data, like so: I wanted a plot for petal dimensions … Continue reading Faceted...

We are pleased and excited to announce that we are working on a second edition of Practical Data Science with R! Manning Publications has just announced the launching of the MEAP (Manning Early Access Program) for the second edition. The MEAP allows you to subscribe to drafts of chapters as they become available, and give … Continue reading Announcing...

Banaue rice terraces. Photo: Jon Rawlinson In a previous article, we showed the use of partial pooling, or hierarchical/multilevel models, for level coding high-cardinality categorical variables in vtreat. In this article, we will discuss a little more about the how and why of partial pooling in R. We will use the lme4 package to fit … Continue reading Partial...

One of the services that the R package vtreat provides is level coding (what we sometimes call impact coding): converting the levels of a categorical variable to a meaningful and concise single numeric variable, rather than coding them as indicator variables (AKA "one-hot encoding"). Level coding can be computationally and statistically preferable to one-hot encoding … Continue reading Custom...

Authors: John Mount and Nina Zumel Introduction In teaching thinking in terms of coordinatized data we find the hardest operations to teach are joins and pivot. One thing we commented on is that moving data values into columns, or into a “thin” or entity/attribute/value form (often called “un-pivoting”, “stacking”, “melting” or “gathering“) is easy to … Continue reading Teaching...

It’s a common situation to have data from multiple processes in a “long” data format, for example a table with columns measurement and process_that_produced_measurement. It’s also natural to split that data apart to analyze or transform it, per-process — and then to bring the results of that data processing together, for comparison. Such a work … Continue reading A...

Imagine that in the course of your analysis, you regularly require summaries of numerical values. For some applications you want the mean of that quantity, plus/minus a standard deviation; for other applications you want the median, and perhaps an interval around the median based on the interquartile range (IQR). In either case, you may want … Continue reading Using...

In our previous note we demonstrated Y-Aware PCA and other y-aware approaches to dimensionality reduction in a predictive modeling context, specifically Principal Components Regression (PCR). For our examples, we selected the appropriate number of principal components by eye. In this note, we will look at ways to select the appropriate number of principal components in … Continue reading Principal...

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