---
title: "Feature Plots"
output:
    rmarkdown::html_vignette:
        toc: true
description: >
  Visualize how features are distributed within a cluster solution.
vignette: >
  %\VignetteIndexEntry{Feature Plots}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

Download a copy of the vignette to follow along here: [feature_plots.Rmd](https://raw.githubusercontent.com/BRANCHlab/metasnf/main/vignettes/feature_plots.Rmd)

Given a cluster solution formatted as a row of a solutions matrix (or extended solutions matrix) and a `data_list` and/or `target_list` containing features to plot, the `auto_plot()` function can automatically generate `ggplot`-based bar and jitter plots showing how that particular feature was divided across clusters.

```{r}
library(metasnf)

data_list <- generate_data_list(
    list(abcd_subc_v, "subcortical_volume", "neuroimaging", "continuous"),
    list(abcd_h_income, "household_income", "demographics", "continuous"),
    list(abcd_colour, "favourite_colour", "misc", "categorical"),
    list(abcd_pubertal, "pubertal_status", "demographics", "continuous"),
    list(abcd_anxiety, "anxiety", "behaviour", "ordinal"),
    list(abcd_depress, "depressed", "behaviour", "ordinal"),
    uid = "patient"
)

# Build space of settings to cluster over
settings_matrix <- generate_settings_matrix(
    data_list,
    nrow = 2,
    min_k = 20,
    max_k = 50,
    seed = 42
)

# Clustering
solutions_matrix <- batch_snf(data_list, settings_matrix)

sm_row <- solutions_matrix[1, ]
```

Note, the row you pick could come directly from a `solutions_matrix`, but could also come from an `extended_solutions_matrix` or from a representative solution picked after `get_representative_solutions()`.

```{r}
plot_list <- auto_plot(
    solutions_matrix_row = sm_row,
    data_list = data_list
)

names(plot_list)

plot_list$"household_income"

plot_list$"smri_vol_scs_csf"

plot_list$"colour"
```

If there's something you'd like to change about the plot, you can always tack on `ggplot2` elements to build from the skeleton provided by `auto_plot`:

```{r}
plot_list$"colour" +
    ggplot2::labs(
        fill = "Favourite Colour",
        x = "Cluster",
        title = "Patient Favourite Colour by Cluster"
    ) +
    ggplot2::scale_fill_manual(
        values = c(
            "green" = "forestgreen",
            "red" = "firebrick3",
            "yellow" = "darkgoldenrod1"
        )
    )
```