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_richnessThere are many more interesting examples at the phyloseq online tutorials.
## 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