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There are many other packages for visualising correlation or similar
information. Here we show how pairwise
structures produced
by bullseye
can be displayed with these visualisations
provided by these packages.
Conversely, we show how correlation or correlation-like information
provided by other packages can be displayed using
bullseye
.
# install.packages("palmerpenguins")
library(bullseye)
library(dplyr)
library(ggplot2)
peng <-
rename(palmerpenguins::penguins,
bill_length=bill_length_mm,
bill_depth=bill_depth_mm,
flipper_length=flipper_length_mm,
body_mass=body_mass_g)
bullseye
with other
packagescorrplot
visualisationsThe package corrplot
provides correlation displays in
matrix layout. Standard usage builds a correlation matrix with
cor
and plots it with corrplot
.
To show bullseye
results:
sc <- pairwise_scores(peng) # includes factors, unlike `cor`
corrplot::corrplot(as.matrix(sc), diag=FALSE)
linkspotter
visualisationsThe linkspotter
package calculates and visualizes
association for numeric and factor variables using a network layout
plot. The nodes show the variables and the edges represent the measure
of association between pair of variables. Absolute correlation is mapped
to edge width.
bullseye
visualisations with other packages.The correlation
package offers calculation of a variety
of correlations, including partial correlations, Bayesian correlations,
multilevel correlations, polychoric correlations, biweight, percentage
bend or Sheperd’s Pi correlations, distance correlation and more. The
output data structure is a tidy dataframe with a correlation value and
correlation tests for variable pairs for which the correlation method is
defined. This is converted to pairwise
via the
as.pairwise
method.
# install.packages("correlation")
library(correlation)
sc_cor <- correlation(peng, method = "distance")
plot(as.pairwise(sc_cor))
Multiple measures from correlation
can also be used:
sc_multi<- bind_rows(
as.pairwise(correlation(peng, method = "pearson")),
as.pairwise(correlation(peng, method = "biweight")))
plot(sc_multi)
bullseye
results.In this example we compare ace and nmi measures for the penguin data
pm <- pairwise_multi(peng)
tidyr::pivot_wider(pm, names_from=score, values_from = value) |>
ggplot(aes(x=nmi, y=ace))+ geom_point()
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.