MPAcolors/README.Rmd at main · vrognas/MPAcolors · GitHub

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---
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-",
  out.width = "100%"
)
```

# MPAcolors

<!-- badges: start -->
<!-- badges: end -->

The goal of MPAcolors is to provide colors used by the Swedish Medical Products Agency (MPA) in their publications and reports.

The package also provides functions to generate color palettes for use in ggplot2 plots.

## Installation

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

``` r
# install.packages("devtools")
devtools::install_github("vrognas/MPAcolors")
```

## Using these colors in plots

Let's look at some examples to see the various ways these functions can be used to customize colors with ggplot2.
Below is a very simple bar chart using the `palmerpenguins::penguins` data set.

```{r setup}
library(MPAcolors)
library(ggplot2)
library(dplyr)
```

The new mpa_color function can be used to specifically define a color.
In the plot below, `mpa_color("mpa_blue")` is the fill argument in the geom_bar layer to make all of the bars that specific shade of `mpa_blue`.

```{r}
palmerpenguins::penguins |>
  count(species) |>
  ggplot(aes(x = species, y = n)) +
  geom_bar(stat = "identity", fill = mpa_color("mpa_blue")) +
  labs(title = "The count of each species in the palmerpenguins data set") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
  theme_linedraw() +
  theme(
    axis.ticks = element_blank(),
    axis.title = element_blank(),
    panel.grid.major.x = element_blank()
  )
```

We could also add `fill = species` to the aes layer (meaning that each species will have its own color) and then use `scale_fill_mpa(palette = "complementary")` to automatically apply our "complementary" color palette.

```{r fill}
palmerpenguins::penguins |>
  count(species) |>
  ggplot(aes(x = species, y = n, fill = species)) +
  geom_bar(stat = "identity") +
  scale_fill_mpa(palette = "complementary") +
  labs(title = "The count of each species in the palmerpenguins data set") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
  theme_linedraw() +
  theme(
    axis.ticks = element_blank(),
    axis.title = element_blank(),
    legend.position = "none",
    panel.grid.major.x = element_blank()
  )
```


Or if you want to use the custom colors but not a specific palette, add a `scale_fill_manual()` layer and specify the values using the `mpa_color()` function.

```{r custom-colors}
palmerpenguins::penguins |>
  count(species) |>
  ggplot(aes(x = species, y = n, fill = species)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(
    values = unname(c(mpa_color(
      "pink_complement_20",
      "yellow_complement",
      "mpa_blue_60"
    )))
  ) +
  labs(title = "The count of each species in the palmerpenguins data set") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
  theme_linedraw() +
  theme(
    axis.ticks = element_blank(),
    axis.title = element_blank(),
    legend.position = "none",
    panel.grid.major.x = element_blank()
  )
```

Lastly, below is an example of a continuous color scale, with our `scale_color_mpa_c()` function specifying the palette to be used.

```{r continuous}
palmerpenguins::penguins |>
  filter(!is.na(sex)) |>
  ggplot(aes(x = sex, y = body_mass_g, color = bill_depth_mm)) +
  geom_jitter(size = 3, width = 0.3) +
  scale_color_mpa_c(palette = "highlight", direction = -1) +
  labs(
    title = "Bill depth and body mass by sex",
    y = "Body mass (g)",
    color = "Bill depth (mm)"
  ) +
  theme_linedraw() +
  theme(
    axis.ticks = element_blank(),
    axis.title.x = element_blank(),
    panel.grid.major.x = element_blank()
  )
```

### Complementary palette

```{r complementary}
scales::show_col(mpa_palette("complementary"), cex_label = 2)
```

### Highlight palette

```{r highlight}
scales::show_col(mpa_palette("highlight"), cex_label = 2)
```