The corrgram package provides functions for creating
corrgrams using three different graphics systems, base, grid, and
lattice.
Base R graphics + single function corrgram() for
dataframes or matrices. + Enables most features found in the paper by
@friendly2002corrgrams. - No automatic
legend. - Not easily combined with other graphics.
lattice graphics + Separate panel functions for
lattice::levelplot() for dataframes and
lattice::splom() for correlation matrices. + Enables
automatic legend. + Enables corrgrams conditioned on other variables. +
Can be combined with other lattice graphics for complex figures. - Not
feature complete compared to base R.
grid graphics + single function corrgram2()
for either dataframes or correlation matrices. + Enables automatic
legend. + Can be combined with other grid graphics for complex figures.
- Not feature complete compared to base R. + Faster than base R when
evaluated inside Positron.
This vignette demonstrates how to create corrgrams using
grid graphics with the corrgram2() function
and a variety of panel functions for visualizing correlations in
different ways.
This vignette demonstrates the use of grid-based panels in
corrgram2, which provide flexible and modern correlation
matrix visualizations.
The vote dataset contains roll call voting records for
US Senators. Here we show a grid-based correlation plot with absolute
correlations, ordering, and a legend.
The auto dataset contains various automobile attributes.
We select a subset of numeric variables and display a grid-based
correlation plot using the fill panel.
vars6 <- setdiff(colnames(auto), c("Model", "Origin"))
corrgram2(auto[, vars6],
lower.panel = grid_panel.shade, upper.panel=grid_panel.pie,
title = "auto data", legend = TRUE)