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))

p

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

p

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 = after_stat(density))) +
  geom_ysidedensity(aes(x = after_stat(density))) +
  theme_bw()
p2 + labs(title = "FacetNull")

p2 + facet_wrap(Species~Species2) +
  labs(title = "FacetWrap") +
  guides(x = 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") 

p2 + facet_grid(Species~Species2, space = "free", scales = "free") +
  labs(title = "FacetGrid", subtitle = "Collapsing All Side Panels") +
  ggside(collapse = "all")

p + geom_xsidedensity(aes(y=after_stat(density)))+
  geom_ysidedensity(aes(x=after_stat(density), ycolor = Species2)) +
  theme_bw() + 
  facet_grid(Species~Species2, space = "free", scales = "free") +
  labs(title = "FacetGrid", subtitle = "Collapsing All Side Panels") +
  ggside(collapse = "all")

p + geom_xsidedensity(aes(y=after_stat(density), xfill = Species), position = "stack")+
  geom_ysidedensity(aes(x=after_stat(density), yfill = Species2), position = "stack") +
  theme_bw() + 
  facet_grid(Species~Species2, space = "free", scales = "free") +
  labs(title = "FacetGrid", subtitle = "Collapsing All Side Panels") +
  ggside(collapse = "all") +
  scale_xfill_manual(values = c("darkred","darkgreen","darkblue")) +
  scale_yfill_manual(values = c("black","gold"))

Note that when collapsing panels on FacetGrid, the panels appear under the strips whereas on FacetWrap they appear above the strips. This is because FacetWrap, collapsing panels in the same column or row may not share the same facet variable, which would be confusing since the strip would not represent the data entirely. This is not the case with FacetGrid since each row or column is dictated by the facet variable.

Collapsing on an x or y coerces all panels in that column or row to the same scale, thus scales = "free_x" is incompatible with collapse = "x".

p2 + facet_wrap(Species~Species2, scales = "free") +
  labs(title = "FacetWrap", subtitle = "Collapsing X side Panels") +
  ggside(collapse = "x") 
#> Warning: free x scales is not compatible with collapse x. Assigning new x
#> scales.

You may also change the size of the side panels with the theme elements ggside.panel.scale, ggside.panel.scale.x and ggside.panel.scale.y. These theme elements take a positive numeric value as input and indicate how large the side panel’s heights or widths are relative to the main plot’s height or width. For example, setting ggside.panel.scale.x = 1 will mean the x side panels height will be equal in size to the main panel’s heights (or if x is collapsed, is equal to the sum of the heights).

p2 + facet_grid(Species~Species2, space = "free", scales = "free") +
  labs(title = "FacetGrid", subtitle = "Collapsing X Side Panels and \nAdjusted Side Panel Relative Size") +
  ggside(collapse = "all", x.pos = "bottom", scales = "free_x",
         respect_side_labels = "y") +
  theme(ggside.panel.scale.x = .4,
        ggside.panel.scale.y = .25)

Specifying ggside Axes

As of ggside (>= 0.1.0) you can now have further control over how a side axis will render. For example, when making a xside geometry, the x-axis was shared with the main panel so you can specify how the x-axis is rendered via the scale_x_* functions. Prior to ggside (>= 0.1.0) you had no control over the y-axis of the xside panel. Now, you can use scale_xsidey_(continuous|discrete) functions to further specify this scale. Similarly, you can do this for the x-axis of a yside panel with scale_ysidex_(continuous|discrete) functions. For all intents and purposes, these are identical to the scale_(x|y)_* functions but they only affect their xside or yside panel’s non-shared axis.

Additionally, this allows you to mix continuous and discrete scales on the same y or x axis. For example the main panel y axis may be continuous and the side panel y axis may be discrete. Take the following example that was not possible prior to this version:

ggplot(mpg, aes(displ, hwy, colour = class)) + 
  geom_point(size = 2) +
  geom_xsideboxplot(aes(y =class), orientation = "y") +
  geom_ysidedensity(aes(x = after_stat(density)), position = "stack") +
  theme(ggside.panel.scale = .3)

Now we can provide the plot with the proper scale the panel will expect. You can use the guide argument of these new scale functions to further customize how the text is rendered, the breaks argument to control the location or visibility of the tick marks.

ggplot(mpg, aes(displ, hwy, colour = class)) + 
  geom_point(size = 2) +
  geom_xsideboxplot(aes(y =class), orientation = "y") +
  geom_ysidedensity(aes(x = after_stat(density)), position = "stack") +
  theme(ggside.panel.scale = .3) +
  scale_xsidey_discrete() +
  scale_ysidex_continuous(guide = guide_axis(angle = 90), minor_breaks = NULL)