---
title: "Integrating bullseye with other packages."
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Integrating bullseye with other packages.}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message=FALSE, warning=FALSE,
  fig.width=4, fig.height=4, fig.align = "center"
)


```


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`.


```{r setup, message=FALSE}
# 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)
```

```{r, echo=FALSE}
library(ggiraph)
set_girafe_defaults(opts_sizing= opts_sizing(rescale=FALSE, width=.5))
```

## Using data structures from `bullseye` with other packages

###  `corrplot` visualisations

The 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:

```{r , corrplot_assoc}
sc <- pairwise_scores(peng) # includes factors, unlike `cor`
  
corrplot::corrplot(as.matrix(sc), diag=FALSE)
# corrplot::corrplot(as.matrix(sc, default=1)) # to show 1 along the diagoonal

```

###  `linkspotter` visualisations

The `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. 


```{r, fig.width=7, fig.height=7}
linkspotter::linkspotterGraphOnMatrix(as.data.frame(as.matrix(sc)),minCor=0.7)
```


## Using  `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.

```{r}
# install.packages("correlation")
library(correlation)
sc_cor <- correlation(peng, method = "distance")
plot(as.pairwise(sc_cor))
```

Multiple measures from `correlation` can also be used:
```{r}
sc_multi<- bind_rows(
  as.pairwise(correlation(peng, method = "pearson")),
  as.pairwise(correlation(peng, method = "biweight")))
plot(sc_multi)
```

## Using other visualisations with  `bullseye` results.

In this example we compare ace and nmi measures for the penguin data

```{r}
pm <- pairwise_multi(peng)
tidyr::pivot_wider(pm, names_from=score, values_from = value) |>
  ggplot(aes(x=nmi, y=ace))+ geom_point()

```