Data Science Desktop Survival Guide by Graham Williams
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Dealing with Correlations

20180726 From the final result we can identify pairs of variables where we might want to keep one but not the other variable because they are highly correlated. We will select them manually since it is a judgement call. Normally we might limit the removals to those correlations that are 0.90 or more. In our case here the three pairs of highly correlated variables make intuitive sense.

# Note the correlated variables that are redundant.

correlated <- c("temp_3pm", "pressure_3pm", "temp_9am")

# Add them to the variables to be ignored for modelling.

ignore <- union(ignore, correlated) %T>% print()

## [1] "date"         "location"     "risk_mm"      "temp_3pm"     "pressur...
## [6] "temp_9am"