---
title: "Supported Plot Types"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteEncoding{UTF-8}
  %\VignetteIndexEntry{Supported Plot Types}
  %\VignetteEngine{knitr::rmarkdown}
toc: true
editor_options: 
  chunk_output_type: console
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.height = 6,
  fig.width = 7
)
library(plot2)
diamonds <- ggplot2::diamonds
movies <- ggplot2movies::movies
options(plot2.colour = "viridis")
```

The following plot types are supported.

Any `ggplot2` geom is supported, but we added support for some other, more advanced plots.

# Column / Bar

Used for comparing discrete categories through rectangular bars.

```{r}
diamonds |> # from the ggplot2 package
  plot2(x = cut,
        y = n())
```

In `plot2`, bar types are horizontal alternatives for column types (just like MS Excel):
```{r}
diamonds |>
  plot2(x = cut,
        y = n(),
        type = "bar")
```

# Line

Used for visualising trends over ordered intervals.

```{r}
pressure |> # from base R
  plot2(x = temperature,
        y = pressure,
        type = "line")
pressure |>
  plot2(x = temperature,
        y = pressure,
        type = "line-point")
```

# Point

Used for displaying individual observations in a two dimensional space.

```{r}
iris |> # from base R
  plot2()
diamonds |>
  plot2(x = carat,
        y = price,
        category = cut,
        type = "point")
```

# Area

Used for emphasising cumulative magnitudes across continuous domains.

```{r}
pressure |>
  plot2(x = temperature,
        y = pressure,
        type = "area")
airquality |>
  plot2(x = Day, 
        y = Wind, 
        category = Month,
        category.character = TRUE,
        stacked_fill= TRUE,
        type = "area")
```

# Boxplot / Violin

Used for summarising and comparing distributions with focus on spread and density.

```{r}
iris |>
  plot2(x = Species,
        type = "violin")
iris |>
  plot2(x = Species,
        y = where(is.double),
        type = "boxplot")
```

# Histogram

Used for visualising the frequency distribution of continuous variables.

```{r}
diamonds |>
  plot2(x = price,
        type = "hist")
```

# Geo (sf)

Used for mapping spatial data encoded as simple features.

```{r}
netherlands |> # from this plot2 package
  plot2()
```

# Beeswarm

Used for showing distributions of individual observations without overlap.

```{r}
iris |>
  plot2(x = Species,
        y = Sepal.Length,
        type = "beeswarm")
```

# Back-to-back

Used for contrasting two mirrored groups across shared categories.

```{r}
admitted_patients |> # from this plot2 package
  plot2(x = age_group,
        y = n(),
        facet = ward,
        type = "back-to-back")
admitted_patients |> # from this plot2 package
  plot2(x = age_group,
        y = n(),
        y.limits = c(0, 60),
        category = gender,
        facet = ward,
        type = "back-to-back")
```

# Sankey

Used for depicting flows or transitions between connected stages.

```{r}
Titanic |> # from base R
  plot2(x = c(Age, Class, Survived),
        category = Sex,
        type = "sankey")
```

# Spider

Used for comparing multivariate values across categorical axes arranged radially, enabling pattern recognition and relative magnitude assessment between groups.

```{r}
diamonds |>
    plot2(x = cut,
          y = mean(price),
          category = color,
          type = "spider",
          y.labels = dollars)
# spider plots can have a filling colour, but it's hardly ever useful
diamonds |>
    plot2(x = cut,
          y = mean(price),
          category = color,
          type = "spider",
          y.labels = dollars,
          colour_fill = "viridis")
```

# UpSet

Used for analysing intersections among multiple sets with scalable clarity.

```{r}
movies |> # from the ggplot2movies package
  plot2(x = c(Action, Animation, Comedy, Drama, Romance),
        type = "upset")
movies |>
  plot2(x = c(Action, Animation, Comedy, Drama, Romance),
        y = median(rating),
        y.title = "Median Rating",
        x.sort = TRUE,
        type = "upset")
```

# Dumbbell

Used for highlighting changes or differences between paired values.

```{r}
diamonds |>
  dplyr::filter(cut %in% c("Fair", "Very Good")) |>
  plot2(x = cut(carat, 6),
        y = median(price),
        category = cut,
        type = "dumbbell")
```