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Scatterplot with marginal distributions and statistical results against Hill diversity of phyloseq object

Source: R/plot_functions.R
ggscatt_pq.Rd

lifecycle-experimental

Basically a wrapper of function ggstatsplot::ggscatterstats() for object of class phyloseq and Hill number.

Usage

ggscatt_pq(
  physeq,
  num_modality,
  hill_scales = c(0, 1, 2),
  rarefy_by_sample = FALSE,
  one_plot = TRUE,
  ...
)

Arguments

physeq

(required): a phyloseq-class object obtained using the phyloseq package.

num_modality

(required) Name of the numeric column in physeq@sam_data to plot and test against hill number

hill_scales

(a vector of integer) The list of q values to compute the hill number H^q. If Null, no hill number are computed. Default value compute the Hill number 0 (Species richness), the Hill number 1 (exponential of Shannon Index) and the Hill number 2 (inverse of Simpson Index).

rarefy_by_sample

(logical, default FALSE) If TRUE, rarefy samples using phyloseq::rarefy_even_depth() function.

one_plot

(logical, default FALSE) If TRUE, return a unique plot with the three plot inside using the patchwork package.

...

Other arguments passed on to ggstatsplot::ggscatterstats() function.

Value

Either an unique ggplot2 object (if one_plot is TRUE) or a list of ggplot2 plot for each hill_scales.

Details

This function is mainly a wrapper of the work of others. Please make a reference to ggstatsplot::ggscatterstats() if you use this function.

See also

ggbetween_pq()

Author

Adrien Taudière

Examples

if (requireNamespace("ggstatsplot")) {
  ggscatt_pq(data_fungi_mini, "Time", type = "non-parametric")
  ggscatt_pq(data_fungi_mini, "Time", hill_scales = 1:4, type = "parametric")
  ggscatt_pq(data_fungi_mini, "Sample_id",
    hill_scales = c(0, 0.5),
    one_plot = FALSE
  )
}
#> Taxa are now in columns.
#> Cleaning suppress 0 taxa and 0 samples.
#> Taxa are now in rows.
#> Joining with `by = join_by(Sample)`
#> Registered S3 method overwritten by 'ggside':
#>   method from   
#>   +.gg   ggplot2
#> Taxa are now in columns.
#> Cleaning suppress 0 taxa and 0 samples.
#> Taxa are now in rows.
#> Joining with `by = join_by(Sample)`
#> Taxa are now in columns.
#> Cleaning suppress 0 taxa and 0 samples.
#> Taxa are now in rows.
#> Joining with `by = join_by(Sample)`
#> [[1]]
#> `stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

#> 
#> [[2]]
#> `stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

#>