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

# ggh4x <img src="man/figures/logo_300px.png" align = "right" width = "150" />

<!-- badges: start -->

[![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)
<!-- badges: end -->

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
library(ggh4x)
#> Loading required package: ggplot2
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"))
#> Warning in geom_point(aes(SW = Sepal.Width), data = ~subset(., Species == :
#> Ignoring unknown aesthetics: SW
#> Warning in geom_point(aes(PL = Petal.Length), data = ~subset(., Species == :
#> Ignoring unknown aesthetics: PL
#> Warning in geom_point(aes(PW = Petal.Width), data = ~subset(., Species == :
#> Ignoring unknown aesthetics: PW

# 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
```

<img src="man/figures/README-multicolour-1.png" width="80%" style="display: block; margin: auto;" />

``` r
# 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
```

<img src="man/figures/README-facets-1.png" width="80%" style="display: block; margin: auto;" />

## 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.
