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Visualize beta diversity with scatterplots and trendlines.

Source: R/corrplot.r
bdiv_corrplot.Rd

Visualize beta diversity with scatterplots and trendlines.

Usage

bdiv_corrplot(
  biom,
  x,
  bdiv = "Bray-Curtis",
  layers = "tc",
  weighted = TRUE,
  tree = NULL,
  within = NULL,
  between = NULL,
  stat.by = NULL,
  facet.by = NULL,
  colors = TRUE,
  shapes = TRUE,
  test = "emmeans",
  fit = "gam",
  at = NULL,
  level = 0.95,
  p.adj = "fdr",
  transform = "none",
  ties = "random",
  seed = 0,
  alt = "!=",
  mu = 0,
  caption = TRUE,
  check = FALSE,
  ...
)

Arguments

biom

An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.

x

Dataset field with the x-axis values. Equivalent to the regr argument in stats_table(). Required.

bdiv

Beta diversity distance algorithm(s) to use. Options are: "Bray-Curtis", "Manhattan", "Euclidean", "Jaccard", and "UniFrac". For "UniFrac", a phylogenetic tree must be present in biom or explicitly provided via tree=. Multiple/abbreviated values allowed. Default: "Bray-Curtis"

layers

One or more of c("trend", "confidence", "point", "name", "residual"). Single letter abbreviations are also accepted. For instance, c("trend", "point") is equivalent to c("t", "p") and "tp". Default: "tc"

weighted

Take relative abundances into account. When weighted=FALSE, only presence/absence is considered. Multiple values allowed. Default: TRUE

tree

A phylo object representing the phylogenetic relationships of the taxa in biom. Only required when computing UniFrac distances. Default: biom$tree

within, between

Dataset field(s) for intra- or inter- sample comparisons. Alternatively, dataset field names given elsewhere can be prefixed with '==' or '!=' to assign them to within or between, respectively. Default: NULL

stat.by

Dataset field with the statistical groups. Must be categorical. Default: NULL

facet.by

Dataset field(s) to use for faceting. Must be categorical. Default: NULL

colors

How to color the groups. Options are:

TRUE -

Automatically select colorblind-friendly colors.

FALSE or NULL -

Don't use colors.

a palette name -

Auto-select colors from this set. E.g. "okabe"

character vector -

Custom colors to use. E.g. c("red", "#00FF00")

named character vector -

Explicit mapping. E.g. c(Male = "blue", Female = "red")

See "Aesthetics" section below for additional information. Default: TRUE

shapes

Shapes for each group. Options are similar to colors's: TRUE, FALSE, NULL, shape names (typically integers 0 - 17), or a named vector mapping groups to specific shape names. See "Aesthetics" section below for additional information. Default: TRUE

test

Method for computing p-values: 'none', 'emmeans', or 'emtrends'. Default: 'emmeans'

fit

How to fit the trendline. 'lm', 'log', or 'gam'. Default: 'gam'

at

Position(s) along the x-axis where the means or slopes should be evaluated. Default: NULL, which samples 100 evenly spaced positions and selects the position where the p-value is most significant.

level

The confidence level for calculating a confidence interval. Default: 0.95

p.adj

Method to use for multiple comparisons adjustment of p-values. Run p.adjust.methods for a list of available options. Default: "fdr"

transform

Transformation to apply. Options are: c("none", "rank", "log", "log1p", "sqrt", "percent"). "rank" is useful for correcting for non-normally distributions before applying regression statistics. Default: "none"

ties

When transform="rank", how to rank identical values. Options are: c("average", "first", "last", "random", "max", "min"). See rank() for details. Default: "random"

seed

Random seed for permutations. Must be a non-negative integer. Default: 0

alt

Alternative hypothesis direction. Options are '!=' (two-sided; not equal to mu), '<' (less than mu), or '>' (greater than mu). Default: '!='

mu

Reference value to test against. Default: 0

caption

Add methodology caption beneath the plot. Default: TRUE

check

Generate additional plots to aid in assessing data normality. Default: FALSE

...

Additional parameters to pass along to ggplot2 functions. Prefix a parameter name with a layer name to pass it to only that layer. For instance, p.size = 2 ensures only the points have their size set to 2.

Value

A ggplot2 plot. The computed data points, ggplot2 command, stats table, and stats table commands are available as $data, $code, $stats, and $stats$code, respectively.

Aesthetics

All built-in color palettes are colorblind-friendly. The available categorical palette names are: "okabe", "carto", "r4", "polychrome", "tol", "bright", "light", "muted", "vibrant", "tableau", "classic", "alphabet", "tableau20", "kelly", and "fishy".

Shapes can be given as per base R - numbers 0 through 17 for various shapes, or the decimal value of an ascii character, e.g. a-z = 65:90; A-Z = 97:122 to use letters instead of shapes on the plot. Character strings may used as well.

See also

Other beta_diversity: bdiv_boxplot(), bdiv_clusters(), bdiv_heatmap(), bdiv_ord_plot(), bdiv_ord_table(), bdiv_stats(), bdiv_table(), distmat_stats()

Other visualization: adiv_boxplot(), adiv_corrplot(), bdiv_boxplot(), bdiv_heatmap(), bdiv_ord_plot(), plot_heatmap(), rare_corrplot(), rare_multiplot(), rare_stacked(), stats_boxplot(), stats_corrplot(), taxa_boxplot(), taxa_corrplot(), taxa_heatmap(), taxa_stacked()

Examples

    library(rbiom)
    
    biom <- rarefy(hmp50)
    bdiv_corrplot(biom, "Age", stat.by = "Sex", layers = "tcp")