estimate_richness function,
and returns a ggplot plotting object.
The plot generated by this function will include every sample
in physeq, but they can be further grouped on the horizontal axis
through the argument to x,
and shaded according to the argument to color (see below).
You must use untrimmed, non-normalized count data for meaningful results,
as many of these estimates are highly dependent on the number of singletons.
You can always trim the data later on if needed,
just not before using this function.plot_richness(physeq, x = "samples", color = NULL, shape = NULL,
title = NULL, scales = "free_y", nrow = 1, shsi = NULL,
measures = NULL, sortby = NULL)phyloseq-class, or alternatively,
an otu_table-class. The data about which you want to estimate.x) can be either a character string indicating a
variable in sample_data
(among the set returned by sample_variables(physeq) );
or a custom supplied vector with length equal to the number of samples
in the dataset (nsamples(physeq)). The default value is "samples", which will map each sample's name
to a separate horizontal position in the plot.
NULL.
The sample variable to map to different colors.
Like x, this can be a single character string of the variable name in
sample_data
(among the set returned by sample_variables(physeq) );
or a custom supplied vector with length equal to the number of samples
in the dataset (nsamples(physeq)).
The color scheme is chosen automatically by link{ggplot},
but it can be modified afterward with an additional layer using
scale_color_manual.NULL. The sample variable to map
to different shapes. Like x and color,
this can be a single character string
of the variable name in
sample_data
(among the set returned by sample_variables(physeq) );
or a custom supplied vector with length equal to the number of samples
in the dataset (nsamples(physeq)).
The shape scale is chosen automatically by link{ggplot},
but it can be modified afterward with an additional layer using
scale_shape_manual.NULL. Character string.
The main title for the graphic."free_y".
Whether to let vertical axis have free scale that adjusts to
the data in each panel.
This argument is passed to facet_wrap.
If set to "fixed", a single vertical scale will
be used in all panels. This can obscure values if the
measures argument includes both
richness estimates and diversity indices, for example.1,
meaning that all plot panels will be placed in a single row,
side-by-side.
This argument is passed to facet_wrap.
If NULL, the number of rows and columns will be
chosen automatically (wrapped) based on the number of panels
and the size of the graphics device.NULL, meaning that
all available alpha-diversity measures will be included in plot panels.
Alternatively, you can specify one or more measures
as a character vector of measure names.
Values must be among those supported:
c("Observed", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson", "Fisher").measures argument.
Sort x-indices by the mean of one or more measures,
if x-axis is mapped to a discrete variable.
Default is NULL, implying that a discrete-value horizontal axis
will use default sorting, usually alphabetic.ggplot plot object summarizing
the richness estimates, and their standard error.estimate_richness, the variable names of that output should
not be used as x or color (even if it works, the resulting
plot might be kindof strange, and not the intended behavior of this function).
The following are the names you will want to avoid using in x or color:c("Observed", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson", "Fisher").
estimate_richness
estimateR
diversityThere are many more interesting examples at the
## There are many more interesting examples at the phyloseq online tutorials.
## http://joey711.github.io/phyloseq/plot_richness-examples
data("soilrep")
plot_richness(soilrep, measures=c("InvSimpson", "Fisher"))
plot_richness(soilrep, "Treatment", "warmed", measures=c("Chao1", "ACE", "InvSimpson"), nrow=3)
data("GlobalPatterns")
plot_richness(GlobalPatterns, x="SampleType", measures=c("InvSimpson"))
plot_richness(GlobalPatterns, x="SampleType", measures=c("Chao1", "ACE", "InvSimpson"), nrow=3)
plot_richness(GlobalPatterns, x="SampleType", measures=c("Chao1", "ACE", "InvSimpson"), nrow=3, sortby = "Chao1")Run the code above in your browser using DataLab