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Plotting functions in rbiom fall into five categories:

Category Functions
Box Plots adiv_boxplot() bdiv_boxplot() taxa_boxplot()
Correlation Plots adiv_corrplot() bdiv_corrplot() taxa_corrplot()
Ordination Plots bdiv_ord_plot()
Heatmaps bdiv_heatmap() taxa_heatmap() plot_heatmap()
Stacked Bar Plots taxa_stacked()

Box Plots

Box plots are useful for visualizing a numeric outcome against one or more categorical predictors. The rbiom package provides dedicated functions for three numeric outcomes:

  1. Alpha Diversity (shannon, simpson, etc) - adiv_boxplot().
  2. Beta Diversity (unifrac, bray-curtis, etc) - bdiv_boxplot().
  3. Taxa Abundance (phylum, genus, etc) - taxa_boxplot().

You can then map categorical metadata variables to the x-axis, colors, patterns, shapes, and facets. See Mapping Metadata to Aesthetics for details.

Layers

Despite being refered to as “box plots”, rbiom’s *_boxplot() functions support a wide range of graphical elements beyond box-and-whisker.

You can assign one or more of the following options to a box plot’s layers parameter.

Unambiguous abbreviations are also accepted. For instance, layers = c("box", "dot") is equivalent to layers = c("x", "d") and layers = "xd". Note that the single letter abbreviation for “box” is “x” (“bar” is “b”).

Examples

biom <- rarefy(hmp50, depth = 1000)

adiv <- adiv_boxplot(
  biom = biom, layers = "bes", x = "Sex", stat.by = "Sex")
  
bdiv <- bdiv_boxplot(
  biom = biom, layers = "xd", x = "==Body Site", stat.by = "Body Site", 
  pt.alpha = 0.2, pt.stroke = 0 )

taxa <- taxa_boxplot(
  biom = biom, layers = "p", x = "Body Site", p.label = 0, taxa = 3, 
  facet.ncol = 1, facet.strip.position = "right" )

patchwork::wrap_plots(
  lapply(list(adiv, bdiv, taxa), function (p) {
    p + 
      ggplot2::labs(x = NULL, caption = NULL) + 
      ggplot2::theme(legend.position = "none") }))

Statistics

The categorical groups defined by x, stat.by, etc are used to calculate non-parametric statistics with the Mann-Whitney or Kruskal-Wallis algorithms. See Statistics for details.

Correlation Plots

Correlation plots are designed to visualize the relationship between a numeric outcome and a numeric predictor, optionally with additional categorical predictors. The rbiom package provides dedicated functions for three numeric outcomes:

  1. Alpha Diversity (shannon, simpson, etc) - adiv_corrplot().
  2. Beta Diversity (unifrac, bray-curtis, etc) - bdiv_corrplot().
  3. Taxa Abundance (phylum, genus, etc) - taxa_corrplot().

You can then map categorical metadata variables to the x-axis, colors, and facets. See Mapping Metadata to Aesthetics for details.

Layers

You can assign one or more of the following options to a correlation plot’s layers parameter.

Unambiguous abbreviations are also accepted. For instance, layers = c("trend", "point") is equivalent to layers = c("t", "p") and layers = "tp".

Examples

biom <- rarefy(hmp50, depth = 1000)

adiv <- adiv_corrplot(biom, layers = "ts", x = "BMI")
  
bdiv <- bdiv_corrplot(
  biom = biom, layers = "tc", x = "BMI", stat.by = "==Body Site", 
  limit.by = list("Body Site" = c("Buccal mucosa", "Stool")) )

taxa <- taxa_corrplot(
  biom = biom, layers = "tsr", x = "Age", taxa = 3, stat.by = "Body Site", 
  limit.by = list("Body Site" = c("Mid vagina", "Saliva", "Stool")),
  facet.ncol = 1, facet.strip.position = "right" )

patchwork::wrap_plots(
  guides = "collect",
  lapply(list(adiv, bdiv, taxa), function (p) {
    p + ggplot2::labs(caption = NULL) }))

Ordination Plots

Layers

You can assign one or more of the following options to a ordination plot’s layers parameter.

#> Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

Unambiguous abbreviations are also accepted. For instance, layers = c("point", "ellipse") is equivalent to layers = c("p", "e") and layers = "pe".

The layers c("point", "spider", "ellipse", "name") apply to samples. The layers c("mean", "taxon", "arrow") apply to the taxa.

Examples

biom <- rarefy(hmp50)

p1 <- bdiv_ord_plot(biom, layers = "pse", stat.by = "Body Site") +
        ggplot2::theme(legend.position = "none")

p2 <- bdiv_ord_plot(biom, layers = "emt", stat.by = "Body Site")

patchwork::wrap_plots(p1, p2, guides = "collect")

Heatmaps

Visualizing a large grid of values is a job for heatmaps. The generic plot_heatmap() function accepts any matrix, while the two common use cases below operate on a biom object.

  1. Beta Diversity (unifrac, bray-curtis, etc) - bdiv_heatmap().
  2. Taxa Abundance (phylum, genus, etc) - taxa_heatmap().

Examples

biom <- rarefy(hmp50)

bdiv <- bdiv_heatmap(biom, tracks = c("Age", "Body Site"), asp = 0.4)
taxa <- taxa_heatmap(biom, tracks = c("Age", "Body Site"), asp = 0.4)

patchwork::wrap_plots(bdiv, taxa, ncol = 1, guides = "collect")

Stacked Bar Plots

Taxa stacked bar plots show the same information as a taxa heatmap. Both have their own advantages.

Examples

taxa_stacked(rarefy(hmp50), taxa = 10)