README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse   = TRUE,
  comment    = "#>",
  fig.path   = "man/figures/README-",
  fig.width  = 8,
  fig.height = 1.2,
  out.width  = "100%",
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)
```

# ggspruce

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ggspruce provides functions that make automatic adjustments to ggplot2 plots
to improve aesthetics.

## Installation

You can install the development version of ggspruce from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("sheridar/ggspruce")
```

<br>

## Optimizing color palettes

This is a color palette from the MetBrewer package.
This palette can be improved by modifying colors 2, 12, and 13 to make
them more distinct.

```{r}
library(ggspruce)
library(ggplot2)
library(MetBrewer)
library(cowplot)

clrs <- as.character(met.brewer("Austria", 15))

plot_colors(clrs)
```

The `spruce_colors()` function calculates the pairwise distance between
colors in the palette, and identifies pairs of colors that are very similar.
The difference threshold used to identify similar colors can be modified with
the `difference` parameter, with higher values resulting in colors that are more
distinct.
How colors are modified can be controlled using the `property` parameter.

By default, new colors are interpolated based on the other colors on the
palette.

```{r}
new_clrs <- clrs |>
  spruce_colors(difference = 10)

plot_colors(new_clrs)
```

<br>

One or more color properties can also be specified to limit how the colors are
adjusted.
If the example below lightness is first adjusted, if the resulting colors still
do not meet the difference threshold, the hue is adjusted.

```{r}
new_clrs <- clrs |>
  spruce_colors(
    difference = 20,
    property   = c("lightness", "hue")
  )

plot_colors(new_clrs)
```

<br>

## Integration with ggplot2

Color palettes can be automatically modified on the fly using the
`scale_color_spruce()` and `scale_fill_spruce()` functions.
These functions are similar to the `ggplot2::scale_*_manual()` functions with
some modifications.
Original colors are shown on the left and adjusted colors on the right.

```{r, fig.width = 8, fig.height = 5}
clrs <- as.character(met.brewer("Nattier", 8))

# Original colors
plt <- diamonds |>
  ggplot(aes(carat, price, color = clarity)) +
  geom_point(size = 1) +
  guides(color = guide_legend(override.aes = list(size = 3))) +
  theme_bw() +
  theme(
    aspect.ratio = 1,
    legend.position = "bottom"
  )

plt1 <- plt +
  scale_color_manual(values = clrs) +
  ggtitle("original")

# Automatically adjust colors
plt2 <- plt +
  scale_color_spruce(
    values     = clrs,
    difference = 25,
    property   = "lightness"
  ) +
  ggtitle("adjusted")

plot_grid(plt1, plt2, nrow = 1)
```

<br>

If no colors are provided to the `scale_*_spruce()` functions, they will pull
colors from the existing color scale.

```{r, fig.width = 8, fig.height = 5}
# Adjust colors from existing scale
plt1 <- plt +
  scale_color_discrete()

plt2 <- plt1 +
  scale_color_spruce(difference = 25)

plot_grid(plt1, plt2, nrow = 1)
```

<br>

## Colorblind filters

Color similarity can be assessed and adjusted using colorblind filters.
The original and adjusted palettes are shown below.

```{r, fig.width = 8, fig.height = 3}
library(colorspace)
library(RColorBrewer)

clrs <- RColorBrewer::brewer.pal(10, "RdYlGn")

new_clrs <- clrs |>
  spruce_colors(
    difference = 15,
    property   = c("lightness", "saturation"),
    filter     = "deutan"
  )

# Plot adjusted colors without filter
plt1 <- clrs |>
  plot_colors() +
  ggtitle("original")

plt2 <- new_clrs |>
  plot_colors() +
  ggtitle("adjusted")

plot_grid(plt1, plt2, ncol = 1)
```

The palettes are shown below using a filter to simulate deuteranopia.

```{r, fig.width = 8, fig.height = 3}
# Plot adjusted colors with colorblind filter
plt3 <- clrs |>
  deutan() |>
  plot_colors() +
  ggtitle("original + filter")
  
plt4 <- new_clrs |>
  deutan() |>
  plot_colors() +
  ggtitle("adjusted + filter")
  
plot_grid(plt3, plt4, ncol = 1)
```

```{r "TEST RESIZE PALETTE", eval = FALSE, include = FALSE, echo = FALSE}
test_clrs <- clrs[1:20]
n <- 10

# NEW GENSA
test_clrs |>
  collapse_colors(n = n, exact = FALSE) |>
  plot_colors()

test_clrs |>
  spruce_colors() |>
  plot_colors()

collapse_colors(test_clrs, n = n, exact = TRUE)

# OLD
res <- test_clrs |>
  collapse_colors(n = n, exact = TRUE)

res |>
  plot_colors()

x <- res |>
  farver::decode_colour(res, to = "lab") |>
  farver::compare_colour(from_space = "lab", method = "CIE2000")

min(x[upper.tri(x)])

```