ggh4x

ggplot extension: options for tailored facets, multiple colourscales and miscellaneous

https://github.com/teunbrand/ggh4x

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ggplot extension: options for tailored facets, multiple colourscales and miscellaneous

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "80%",
  fig.align = "center",
  dev = "ragg_png",
  dpi = 176
)
```

# ggh4x 


[![CRAN status](https://www.r-pkg.org/badges/version/ggh4x)](https://CRAN.R-project.org/package=ggh4x)
[![R-CMD-check](https://github.com/teunbrand/ggh4x/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/teunbrand/ggh4x/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/teunbrand/ggh4x/graph/badge.svg)](https://app.codecov.io/gh/teunbrand/ggh4x)


The ggh4x package is a ggplot2 extension package. It provides some utility functions that don't entirely fit within the 'grammar of graphics' concept ---they can be a bit hacky--- but can nonetheless be useful in tweaking your ggplots. Examples include adjusting the sizes of facets, mapping multiple aesthetics to colours and specifying individual scales for facets. Besides this, it is also a small collection of geoms, facets, positions, guides and stats.

## Installation

You can install the most recent stable version of ggh4x from CRAN as follows:

``` r
install.packages("ggh4x")
```

Alternatively, you can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("teunbrand/ggh4x")
```

## Overview

There are a few topics explored in the package's vignettes with examples. Links to these topics are below.

* Options to tailor [facets](https://teunbrand.github.io/ggh4x/articles/Facets.html), including:
    * Additional options for axis labelling and placement in [extended facets](https://teunbrand.github.io/ggh4x/articles/Facets.html#extended-facets-1).
    * [Nested facets](https://teunbrand.github.io/ggh4x/articles/Facets.html#nested-facets) that have strips that can span multiple panels.
    * Custom layouts in [manual facets](https://teunbrand.github.io/ggh4x/articles/Facets.html#manual-facets-1).
    * More types of [strips](https://teunbrand.github.io/ggh4x/articles/Facets.html#strips-1) to use in facets.
    * Adjusting the [position scales](https://teunbrand.github.io/ggh4x/articles/Facets.html#position-scales) on a per-panel basis.
    * Varying the [size of panels](https://teunbrand.github.io/ggh4x/articles/Facets.html#sizes) without being limited to the global `aspect.ratio` or fixed coordinates.

* There are some [stat layers](https://teunbrand.github.io/ggh4x/articles/Statistics.html) that can make it easier to plot. These stat layers can:
  * overlaying the [theoretical density](https://teunbrand.github.io/ggh4x/articles/Statistics.html#theoretical-densities) of several distributions, which are computed with the **fitdistrplus** package.
  * draw a trend line of your data with a [rolling kernel](https://teunbrand.github.io/ggh4x/articles/Statistics.html#rolling-kernels).
  * plainly [transform x and y](https://teunbrand.github.io/ggh4x/articles/Statistics.html#function-x-y) position in a group-wise manner.
  * calculate [run-length encodings](https://teunbrand.github.io/ggh4x/articles/Statistics.html#run-length-encoding) of your data.

## Example

Below you'll find an example that illustrates some of the features of ggh4x.

```{r multicolour}
library(ggh4x)
library(scales)

df <- transform(
  iris, 
  Nester = ifelse(Species == "setosa", "Short Leaves", "Long Leaves")
)

# Basic plot
g <- ggplot(df, aes(Sepal.Width, Sepal.Length)) +
  theme_classic() +
  theme(strip.background = element_blank())

# For making a plot with multiple colour scales, we'd first need to make layers
# with alternative aesthetics. We'll choose a colour scale for every species.
# This will produce a few warnings, as ggplot2 doesn't know how to deal with
# the alternative aesthetics.
g <- g + 
  geom_point(aes(SW = Sepal.Width),
             data = ~ subset(., Species == "setosa")) +
  geom_point(aes(PL = Petal.Length),
             data = ~ subset(., Species == "versicolor")) +
  geom_point(aes(PW = Petal.Width),
             data = ~ subset(., Species == "virginica"))

# These alternative aesthetics don't mean a lot until we add a multi-colour
# scale to the plot. We need to specify our alternative aesthetics and colours
# for every scale. Arguments provided as lists are passed on to individual 
# scales.
g <- g +
  scale_colour_multi(
    aesthetics = c("SW", "PL", "PW"),
    name = list("Blue", "Pink", "Orange"),
    colours = list(
      brewer_pal(palette = "YlGnBu")(6),
      brewer_pal(palette = "RdPu")(6),
      brewer_pal(palette = "YlOrRd")(6)
    ),
    guide = guide_colorbar(barheight = unit(50, "pt"))
  )
g
```

```{r facets}
# We can make a facet wherein duplicated strip labels are merged into one strip
g <- g + 
  facet_nested(~ Nester + Species, scales = "free",
               nest_line = TRUE)

# Like we did for colours, we might also want to set position scales for every
# panel individually. We set these in the same order the facets appear in.
position_scales <- list(
  scale_x_reverse(guide = guide_axis(minor.ticks = TRUE)),
  scale_x_continuous(labels = dollar, guide = guide_axis(cap = "both")),
  scale_x_continuous(breaks = c(3, 4), expand = c(0,0))
)

# Adding the list of scales to the plot
g <- g + facetted_pos_scales(x = position_scales)

# Setting the sizes of panels individually
size <- 2 / (1 + sqrt(5))
g <- g + force_panelsizes(cols = c(1, size, size ^ 2), respect = TRUE)
g
```

## Dependency statement

The 'ggh4x' package largely takes on the same dependencies as 'ggplot2' to keep it on the lightweight side. There are two optional, suggested dependencies that are needed for `guide_dendro()` and `stat_theodensity()`, resp. 'ggdendro' and 'fitdistrplus', but these functions should send a prompt in interactive sessions to install the dependencies.

## Footnote

I would like to mention that there are also packages that do some similar things to what this package does. [facetscales](https://github.com/zeehio/facetscales) also has a facet function wherein scales can set per row/column. The [egg](https://github.com/jwdink/egg) package can also set panel sizes. The [lemon](https://github.com/stefanedwards/lemon) package also has options to tweak position axes. The [relayer](https://github.com/clauswilke/relayer) and [ggnewscale](https://github.com/cran/ggnewscale) packages also allow multiple colour scales in the same plot.  

Historically, many of these functions come from the [ggnomics](https://github.com/teunbrand/ggnomics) package, but have been moved here as a package independent of Bioconductor infrastructure.

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