Rethinking an analysis and four-quadrant chart by Professor Scott Galloway, at https://www.profgalloway.com/uss-university. The new visualization categorizes university's prospects in a way that is more consistent with Galloway's apparent intent.
Demonstration of data science on a small real-world problem, including handling of missing values, variance, and decision-support.
It is often useful when analyzing data to think of the type of data we have and what operations can be performed on that data. I provide a brief introduction to a framework for types of measurement, and compare that to the types of variables and data used in R.
A worked example of classification using cluster analysis, xgboost, random forest, and logistic regression.
R doesn't always play nice with networked storage. Here's a guide to installing for the non-admin.
Time series analysis, and time series decomposition, are tools that nearly every data scientist, Six Sigma practitioner, and engineer will find useful. R provides a range of tools for working with time series data; here we’ll explore the most basic: ts() and stl().
A brief introduction to R's bracket notation, using copious examples.