flametree_plot: Create a plot from a flametree data frame

Description Usage Arguments Details Value Examples

View source: R/plot.R

Description

Create a plot from a flametree data frame

Usage

1
2
3
4
5
6
flametree_plot(
  data,
  background = "black",
  palette = c("#1E2640", "#F3EAC0", "#DC9750", "#922C40"),
  style = "plain"
)

Arguments

data

The data frame specifying the flametree

background

The background colour of the image

palette

A vector of colours

style

Style of tree to draw

Details

The flametree_plot() function provides several ways to visualise the data created by the generative system implemented by flametree_grow(). The background argument sets the background colour of the image, and should either be a string specifying an RGB hex colour (e.g., "#000000") or the of a colour recognised by R (see the colours() function for details). Analogously, the palette argument should be a vector of colours. However, the palette argument is interpreted slightly differently depending on which style of plot is created, discussed below. To set the style of the resulting plot, pass one of the following style names: "plain" (the default), "voronoi", "wisp", "nativeflora", "minimal", or "themegray".

Plots in the "plain" style have the following properties. Branches of the trees vary in width using the seg_wid data column. Each branch is shown as a curved segment created using geom_bezier2(), and the colour of the segments is mapped to the seg_col column in the data. No leaves are drawn. In this style, the elements of the palette are used to create a continuous n-colour gradient using scale_colour_gradientn().

Plots in the "voronoi" style draw the shape of the tree the same way as the plain style, except that the segments do not vary in colour and are rendered using geom_bezier() instead of geom_bezier2(). Unlike the plain style, stylised "leaves" are drawn by constructing a Voronoi tesselation of the terminal nodes in the tree. Note that computing the tesselation is computationally expensive and this will likely produce errors if there are too many nodes (typically when the time parameter to flametree_grow() is large). The interpretation of the palette argument is slightly different: the first element of the palette is used to set the colour of the trees, and the rest of the palette colours are used to create the gradient palette used to colour the tiles depicted in the Voronoi tesselation.

The style = "nativeflora" style creates a plot in which tree branches are rendered as thin segments, with a proportion of those segments removed, and small points are drawn at the end of each terminal segment. The width of the branches does not vary (i.e., seg_wid is ignored) and the colour of the branches is constant within tree, but does vary across trees, ignoring the continuous valued seg_col variable and using only the id_tree variable to do so. As with the plain style, the palette colours are used to define an n-colour gradient.

The "wisp" style is similar to nativeflora, but no segments are removed, and the wdith of the branches is mapped to seg_wid. It only uses the first two elements of palette: the first element specifies the colour of the branches, and the second element specifies the colour of the leaf dots.

The final two styles are simplifications of other styles. The "minimal" style is similar to the plain style but does not use curved segments, relying on geom_path() to draw the branches. The "themegray" style does this too, but it ignores the palette argument entirely, rendering the trees in black, set against the default gray background specified by the ggplot2 theme_gray() function.

Value

A ggplot object.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
# the default tree in the plain style
flametree_grow() %>% flametree_plot()

# 10 trees drawn in the nativeflora style
flametree_grow(trees = 10, shift_x = spark_nothing()) %>%
  flametree_plot(style = "nativeflora")

# changing the palette
shades <- c("#A06AB4", "#FFD743", "#07BB9C", "#D773A2")
flametree_grow() %>% flametree_plot(palette = shades)

flametree documentation built on Nov. 29, 2021, 9:12 a.m.