Gist: ggplot2 is used for a layered composition of static 1- and 2D graphics. We create functions to facilitate layered composition of dynamic animations of linear projections, tours. We parallel the composition of static graphics and try to abstract most of the complexity of replicating data and aesthetic mappings across many and changing number of interpolation frames.
We are moving to a new ‘
ggproto’ API for constructing animated tours from ggplot2 objects. This interface should feel more comfortable to people already familiar with ggplot2. Ggproto is the parent class of geoms, layers, and some other plotting elements.
Proto objects (or lists of them) can be assigned to a variable without
ggplot(), they store unevaluated code that will be used to plot. Let’s see what this looks like in ggplot2 before we delve into tour animations.
library("ggplot2") library("magrittr") ## A ggproto: gp <- geom_point() class(gp) #>  "LayerInstance" "Layer" "ggproto" "gg" ## A list of ggplot elements, a 'head-less' ggplot call gg_ls <- list( gp, geom_smooth(method = "loess", formula = y ~ x), ggtitle("proto_* functions return lists of geoms_* functions.", "These lists can be stored, extended, and added to ggplot(). \n We use this to include the animation of ggplots."), facet_grid(cols = vars(Tree)) ) lapply(gg_ls, class) #> [] #>  "LayerInstance" "Layer" "ggproto" "gg" #> #> [] #>  "LayerInstance" "Layer" "ggproto" "gg" #> #> [] #>  "labels" #> #> [] #>  "FacetGrid" "Facet" "ggproto" "gg" ## ggplot call, without geoms, a 'body-less' ggplot call gghead <- ggplot(Orange, aes(age, circumference, color = Tree)) ## Evaluate together gghead + gg_ls
ggproto API we use this feature to create
proto_\*() functions, the counter-parts of
geom_\*() functions. These proto functions are used in composition with
ggtour(), which replaces
ggplot(), to create animated tours. In adopting this additive, composition approach we aim to maximize the flexibility for the end-users to customize tours while keeping the door open for extension of the development of further
play_manual_tour() abstracted away complexity, but it was becoming too big, and consuming workflow which a function should venue too far into. This made it too bloated to allow for some flexibility, but hard to understand all of the arguments. By paring back to
proto_\* can keep details where they are more relevant without becoming overbearing in one monster function.
proto_\* functions return a list of several
geom_\* functions, that were designed to facilitate animation across many projection bases. We abstract away of work and complexity that comes with creating and animating tours, but this comes at the price of flexibility. For instance, protos do not know how to deal with facets and hard-code the number and type of geoms which would otherwise become very burdensome to specify all the specifics of.
The manual tour varies the contribution of a selected variable. This can be used to explore the sensitivity of the variables contribution to the structure in a projection, such as cluster separation.
library(tourr) library(spinifex) ## Scale our numeric data dat <- scale_sd(penguins_na.rm[, 1:4]) ## Use species as a class to set color and shape with clas <- penguins_na.rm$species ## Manual tour, manipulating the contribution of a selected variable bas <- basis_pca(dat) ## Start basis mv <- 1 ## Number of the variable to manipulate mt_path <- manual_tour(bas, manip_var = mv) ## Tour path ## Create a static ggplot2 plot with all frames of the tour ggt <- ggtour(mt_path, dat, angle = .2) + proto_basis() + proto_point(aes_args = list(color = clas, shape = clas), identity_args = list(size = 1.5)) ## Animate animate_gganimate(ggt, height = 3, width = 4.5, units = "in", res = 150) ## Or as a plotly html widget #animate_plotly(ggt)
This tour display composition works with other tours, such as those created with tourr.
## Save a grand tour basis path, projecting through randomly selected bases gt_path <- save_history(dat, grand_tour(), max_bases = 3) ## Static ggplot of all frames in the tour ggt <- ggtour(gt_path, dat, angle = .2) + ## angle is the distance between (geodesically) interpolated frames. proto_basis(position = "right") + proto_point(list(color = clas, shape = clas)) ## Animate animate_gganimate(ggt, height = 2, width = 4.5, units = "in", res = 150) ## Or as a plotly html widget #animate_plotly(ggt)
We can also projection down to 1D with density curve and a unit rectangle basis instead.
## (Quietly create) a 1d guided tour, optimizing the projection space for the holes() function guided_path <- save_history(dat, guided_tour(holes(), d = 1)) ## Static ggplot of all frames in the tour ggt <- ggtour(guided_path, dat, angle = .2) + proto_basis1d() + proto_density(list(fill = clas, color = clas), rug_shape = 3) ## Animate animate_gganimate(ggt, height = 2, width = 4.5, units = "in", res = 150) ## Or as a plotly html widget #animate_plotly(ggt)
Because the output of
ggtour() + proto_\* is a ggplot, themes and setting functions are interoperable.
ggt <- ggt + theme_bw() + ggtitle("My Tour Animation") + labs(x = "Y1", y = "Density") animate_gganimate(ggt, height = 2, width = 3, units = "in", res = 150) ## Or as a plotly html widget #animate_plotly(ggt)
facet_wrap is used to create panels wrapping on levels from 1 or more categorical levels. We call
facet_wrap_tour (before proto functions) to apply similar faceting to the
gt_path <- save_history(dat, max = 7) ggt <- ggtour(gt_path, dat, angle = .3) + facet_wrap_tour(facet_var = clas, nrow = 1) + proto_point(list(color = clas, shape = clas)) + proto_basis(position = "center") + proto_origin() animate_gganimate(ggt, height = 2, width = 6, units = "in", res = 150) ## Or as a plotly html widget #animate_plotly(ggt)
More protos will be added, especially as we find a use-case for them. Check the documentation for
ggtour, all ggtour and proto related functions are linked in the See Also.
|proto functions||related ggplot2 function||detail|
|ggtour||ggplot||Also perfroms setup for the tour.|
|proto_hex||geom_hex||Heatmap hexegons, for high observation density|
|proto_origin/1d||NA||Line segments for the origin, the space where 0 values project to|
|proto_density||geom- _density & _rect||1D density with run hash marks underneath,
|proto_basis/1d||geom- _segment & _text||html widget, row numbers added as tooltip on hover. plotly doesn’t presicly map all ggplot2 settings; legends, point size and opacity may vary.|
|proto_default/1d||several protos||Direction and magnetude of variables to the projection disp~|
|animate_plotly||plotly::ggplotly (with animation)||Default protos for 2/1D tours|
|animate_gganimate||gganimate::animate||gif, mp4 and other video animation. gganimate consumes native ggplots and aestheics should be consistant.|