Visualize multiple distributions in a series of maps

Source: R/map.R

This function enables visualization of distributional information in a series of small multiples by combining distribution metrics and an HCL color palette.

map_multiples(
  x,
  palette,
  ncol,
  lambda_i = 0,
  labels = NULL,
  return_type = c("plot", "df")
)

Arguments

x

RasterStack of distributions processed by metrics_pull().
palette

data frame containing an HCL color palette generated using palette_timecycle(), palette_timeline(), or palette_set().
ncol

integer specifying the number of columns in the grid of plots.
lambda_i

number that allows visual tuning of intensity values via the scales::modulus_trans() function (see Details). Negative numbers increase the opacity of cells with low intensity values. Positive numbers decrease the opacity of cells with low intensity values.
labels

character vector of layer labels for each plot. The default is to not show labels.
return_type

character specifying whether the function should return a ggplot2 plot object ("plot") or data frame ("df"). The default is to return a ggplot2 object.

Value

By default, or when return_type = "plot", the function returns a map that is a ggplot2 plot object.

When return_type = "df", the function returns a data frame containing eight columns:

  • x,y: coordinates of raster cell centers.

  • cell_number: integer indicating the cell number.

  • layer_cell: a unique ID for the cell within the layer in the format "layer-cell_number".

  • intensity: maximum cell value across layers divided by the maximum value across all layers and cells; mapped to alpha level.

  • specificity: the degree to which intensity values are unevenly distributed across layers; mapped to chroma.

  • layer_id: the identity of the raster layer from which an intensity value was pulled; mapped to hue.

  • color: the hexadecimal color associated with the given layer and specificity values.

Details

The lambda_i parameter allows for visual tuning of intensity values with unusual distributions. For example, distributions often contain highly skewed intensity values because individuals spend a vast majority of their time within a relatively small area or because populations are relatively dense during some seasons and relatively dispersed during others. This can make visualizing distributions a challenge. The lambda_i parameter transforms intensity values via the scales::modulus_trans() function, allowing users to adjust the relative visual weight of high and low intensity values.

See also

Other map: map_single()

Examples

# load fisher data
data("fisher_ud")

# prepare data
r <- metrics_pull(fisher_ud)

# generate palette
pal <- palette_timeline(fisher_ud)

# produce maps, adjusting lambda_i to make areas that were used less
# intensively more conspicuous
map_multiples(r, pal, lambda_i = -5, labels = paste("night", 1:9))