FAVA (version 1.0.9)

plot_relabund: Visualize a relative abundance matrix as a stacked bar plot.

Description

This function enables graphical visualization of a matrix of compostional data. In the output plot, each vertical bar represents a single vector; the height of each color in the bar corresponds to the abundance of each category in that vector. Because this function produces a ggplot object, its output can be modified using standard ggplot2 syntax.

Usage

plot_relabund(
  relab_matrix,
  group = NULL,
  time = NULL,
  w = NULL,
  K = NULL,
  arrange = FALSE
)

Value

A ggplot object containing a bar plot visualization of the relative abundance matrix.

Arguments

relab_matrix

A matrix or data frame with rows containing non-negative entries that sum to 1. Each row represents a sample, each column represents a category, and each entry represents the abundance of that category in the sample. If relab_matrix contains any metadata, it must be on the left-hand side of the matrix, the right K entries of each row must sum to 1, and K must be specified. Otherwise, all entries of each row must sum to 1.

group

Optional; a string specifying the name of the column that describes which group each row (sample) belongs to. Use if matrices is a single matrix containing multiple groups of samples you wish to compare.

time

Optional; a string specifying the name of the column that describes the sampling time for each row. Include if you wish to weight FAVA by the distance between samples.

w

Optional; a vector of length I with non-negative entries that sum to 1. Entry w[i] represents the weight placed on row i in the computation of the mean abundance of each category across rows. The default value is w = rep(1/nrow(relab_matrix), nrow(relab_matrix)).

K

Optional; an integer specifying the number of categories in the data. Default is K=ncol(relab_matrix).

arrange

Optional; controls horizontal ordering of samples and vertical ordering of categories. If arrange = TRUE or arrange = "both", samples are ordered by the categories of greatest abundance and categories are ordered in decreasing abundance. If arrange = "vertical", sample order is unchanged but categories are ordered in decreasing abundance. If arrange = "horizontal", samples are ordered by the most abundant categories, but category order is unchanged. If arrange is missing or arrange = FALSE, neither order is changed.

Examples

# Make an example matrix of compositional data
# Each row is an individual. Rows sum to 1.
population_A = matrix(c(
    .5, .3, .2,
    .4, .2, .4,
    .5, .4, .1,
    .6, .1, .3,
    .2, 0, .8
  ),
  nrow = 5,
  byrow = TRUE
  )

  plot_relabund(relab_matrix = population_A,
              K = 3, # How many categories per vector?
              arrange = FALSE
              )
  plot_relabund(relab_matrix = population_A,
              K = 3, # How many categories per vector?
              arrange = "horizontal"
              )
  plot_relabund(relab_matrix = population_A,
              K = 3, # How many categories per vector?
              arrange = "vertical"
              )
   plot_relabund(relab_matrix = population_A,
              K = 3, # How many categories per vector?
              arrange = TRUE  # could also be "both"
              )


# You can modify the plot as you would any ggplot2 object
plot_relabund(relab_matrix = population_A,
              K = 3, # How many categories per vector?
              arrange = TRUE
              ) +
  # Below are example, optional modifications to the default plot
  ggplot2::ggtitle("Population A") +
  ggplot2::scale_fill_brewer("Blues") +
  ggplot2::scale_color_brewer("Blues") +
  ggplot2::xlab("Individuals")
  # Note that both scale_fill and scale_color are needed to change the color of the bars.


  # Plot a dataset which has 2 populations

  population_B = matrix(c(
    .9, 0, .1,
    .6, .4, 0,
    .7, 0, .3,
    .3, .4, .3,
    .5, .3, .2
  ),
  nrow = 5,
  byrow = TRUE
  )


  populations_AB = cbind(data.frame(c("A", "A", "A", "A", "A",
                                     "B", "B", "B", "B", "B")),
                         rbind(population_A, population_B))
  colnames(populations_AB) = c("population", "category_1", "category_2", "category_3")


 plot_relabund(relab_matrix = populations_AB, group = "population")
 plot_relabund(relab_matrix = populations_AB, group = "population", arrange = "vertical")
 plot_relabund(relab_matrix = populations_AB, group = "population", arrange = "horizontal")
 plot_relabund(relab_matrix = populations_AB, group = "population", arrange = "both")