ggside walkthrough

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)
library(ggside)

ggside

The package ggside was designed to enable users to add metadata to their ggplots with ease. While adding metadata information is not extremely difficult to do with geom_tile or other geoms, it can be frustrating to the user positioning these geometries away from the main plot. Additionally, if the user wants to use a color guide with the fill aesthetic, then they may run into conflicts when one layer uses a discrete scale and another uses a continuous scale.

Motivation

Lets look at a very simple example set using dplyr to summarise the diamonds dataset.

summariseDiamond <- diamonds %>%
  mutate(`Cut Clarity` = paste(cut, clarity)) %>%
  group_by(`Cut Clarity`,cut, clarity, color) %>%
  summarise(n = n(),
            `mean Price` = mean(price),
            sd = sd(price))
#> `summarise()` has grouped output by 'Cut Clarity', 'cut', 'clarity'. You can override using the `.groups` argument.
ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
  geom_tile(aes(fill = `mean Price`))

p <-ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
  geom_tile(aes(fill = `mean Price`)) +
  geom_tile(aes(x=0, fill = cut))
tryCatch({
  p
  },
  error = function(e) e
)
#> Error: Discrete value supplied to continuous scale

As you can see, trying to place a colorbar causes an error because the previous geom_tile call has already mapped mean Price to fill and has deemed the scale as continuous. Thus a categorical variable is unable to map to the fill aesthetic anymore.

However, you could map another continuous variable, but this will place these to the same guide, shifting the limits and washing out all color.

summariseDiamond <- summariseDiamond %>%
  group_by(`Cut Clarity`) %>%
  mutate(`sd of means` = sd(`mean Price`))

ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
  geom_tile(aes(fill = `mean Price`)) +
  geom_tile(aes(x=0, fill = `sd of means`))

Using ggside allows for aesthetics to be mapped to a separate scale, which can also be controlled with scale_*fill_gradient functions (more on this later).

ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
  geom_tile(aes(fill = `mean Price`)) +
  geom_ysidetile(aes(x = "sd of means", yfill = `sd of means`))  +
  scale_yfill_gradient(low ="#FFFFFF", high = "#0000FF") 

ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
  geom_tile(aes(fill = `mean Price`)) +
  geom_ysidetile(aes(x = "max", yfill = after_stat(summarise),
                     domain = `mean Price`), stat = "summarise", fun = max) +
  geom_ysidetile(aes(x = "mean",yfill = after_stat(summarise),
                     domain = `mean Price`), stat = "summarise", fun = mean) +
  geom_ysidetile(aes(x = "median",yfill = after_stat(summarise),
                     domain = `mean Price`), stat = "summarise", fun = median) +
  geom_ysidetile(aes(x = "min",yfill = after_stat(summarise),
                     domain = `mean Price`), stat = "summarise", fun = min) +
  scale_yfill_gradient(low ="#FFFFFF", high = "#0000FF") 

.tmp <- summariseDiamond %>% group_by(`Cut Clarity`) %>%
  summarise_at(vars(`mean Price`),.funs = list(max,median,mean,min)) %>%
  tidyr::gather(key = key, value = value, -`Cut Clarity`)

ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
  geom_tile(aes(fill = `mean Price`)) +
  geom_ysidetile(data = .tmp, aes(x = key, yfill = value)) +
  scale_yfill_gradient(low ="#FFFFFF", high = "#0000FF") 

Unfortunately using xfill or yfill with geom_xsidetile or geom_ysidetile respectively will lock its associated scale with the first layer. So you cannot first assign yfill to a discrete scale and then add a layer with yfill maps to a continuous variable or vise a versa. For example, the following code still produces an error. This is largely due to the original motivation for making this package, but at least ggside can give some ease to plotting information to the sides of the main figure.

p <- ggplot(summariseDiamond, aes(x = color, y = `Cut Clarity`)) +
  geom_tile(aes(fill = `mean Price`)) +
  geom_ysidetile(aes(yfill = `sd of means`)) + #sets yfill to a continuous scale
  geom_ysidetile(aes(yfill = cut)) #attempting to add discrete color values
tryCatch({
  p
},
error = function(err){
  print(paste(err))
})
#> Error: Discrete value supplied to continuous scale

How to Use

Geom

geom_xside* and geom_yside* both extend the ggplot2::Geom* environments. As you may expect, geom_xside* allows you to place geometries along the x-axis and geom_yside* allows placement along the y-axis. All of the geom_*side* functions provide a variation on the color aesthetics colour/fill. The variants are named xcolour and xfill or ycolour and yfill for their respective xside or yside geoms. These aesthetics will take precedence over their more general counterpart if assigned. This allows for certain geoms to be plotted on different color scales - particularly useful when one requires a discrete scale and another requires a discrete scale.

Available Geoms

The following geoms are currently available to use right away from the ggside package. Each of the following ggproto Geom*’s are total clones to GeomXside* or GeomYside* with the only variations being the additional color aesthetics. The geom_*side* functions return a ggside_layer object. When a ggside_layer is added to a ggplot, the plot is transformed into a ggside object which has a different ggplot_build S3 method. This method is what allows for the side geoms to be plotted on a separate panel.

  • GeomBar
  • GeomBoxplot
  • GeomDensity
  • GeomFreqpoly
  • GeomHistogram
  • GeomLine
  • GeomPath
  • GeomPoint
  • GeomText
  • GeomTile
  • GeomViolin

Facets

Technically speaking ggside’s main workhorse is hacking Facet framework. Whenever a standard ggplot object is converted to a ggside object, the current Facet ggproto class is replaced to one that is compatible with ggside. All geom*side variants are plotted in a panel adjacent to the axis their name implies. All vanilla ggplot2 geometries are plotted in the main panel.

How its done

XLayer and YLayer

Each geom_*side* variants function return an XLayer or YLayer which both extends ggplot2:::Layer. Currently, only Layer$setup_layer is overwritten to add column PANEL_TYPE to the data. This column will contain "x", or "y" which will help map data to the correct panel. Data missing the PANEL_TYPE column (or containing values other than "x" or "y") is assumed to be mapped to the main panel. The values in PANEL_TYPE help predict which extra panels needed to be drawn per main panel produced by the original Facet class the ggplot holds.

Facet

Three main methods are overwritten in order to make ggside work. compute_layout, map_data, and draw_panels. The compute_layout will first call the base Facet’s method, and then will will build more panels based on the attached ggside object. map_data will take extra care to ensure data is mapped to the proper panel using PANEL_TYPE as well as any other facet variables passed. draw_panels which is responsible for rendering all panels correctly.

Extending

Currently, ggside works with ggplot2’s three base facet classes, FacetNull, FacetWrap and FacetGrid. If you wish to extend ggside to another package’s custom facet function, then you must also export a as_ggsideFacet S3 method, which will be called when an the ggplot is converted to ggside or whenever a new facet is added to the plot. This method should return a ggproto object that inherits from the Facet group. Helpful computed variables in the layout object are PANEL_TYPE which indicates if the PANEL expects a side geom or default geom, and PANEL_GROUP which helps clarify which PANEL’s are grouped together in a facet. These additional computed variables and the ggside object passed to params will have the information needed to help you draw panels for you custom facet with ggside.

Examples with Facets

i2 <- iris %>%
  mutate(Species2 = rep(c("A","B"), 75))
p <- ggplot(i2, aes(Sepal.Width, Sepal.Length, color = Species)) +
  geom_point()
p2 <- p + geom_xsidedensity(aes(y=stat(density))) +
  geom_ysidedensity(aes(x=stat(density))) +
  theme_bw()
p2 + labs(title = "FacetNull")

p2 + facet_wrap(Species~Species2) +
  labs(title = "FacetWrap") +
  guides(guide_axis(check.overlap = T))

p2 + facet_grid(Species~Species2, space = "free", scale = "free_y") 

Further control on how the sideFacets are handled may be done with the ggside function.

p2 + ggside(x.pos = "bottom", y.pos = "left") +
  labs(title = "FacetNull", subtitle = "Xside placed bottom, Yside placed left")

When using having multiple panels, it may be handy to collapse side panels to one side, which helps save space and computation time!

p2 + facet_wrap(Species~Species2) +
  labs(title = "FacetWrap", subtitle = "Collapsing X side Panels") +
  ggside(collapse = "x")