Visualize taxa abundance with scatterplots and trendlines.
Usage
taxa_corrplot(
biom,
x,
rank = -1,
layers = "tc",
taxa = 6,
lineage = FALSE,
unc = "singly",
other = FALSE,
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 byas_rbiom()
can also be given here.- x
Dataset field with the x-axis values. Equivalent to the
regr
argument instats_table()
. Required.- rank
What rank(s) of taxa to display. E.g.
"Phylum"
,"Genus"
,".otu"
, etc. An integer vector can also be given, where1
is the highest rank,2
is the second highest,-1
is the lowest rank,-2
is the second lowest, and0
is the OTU "rank". Runbiom$ranks
to see all options for a given rbiom object. Default:-1
.- layers
One or more of
c("trend", "confidence", "point", "name", "residual")
. Single letter abbreviations are also accepted. For instance,c("trend", "point")
is equivalent toc("t", "p")
and"tp"
. Default:"tc"
- taxa
Which taxa to display. An integer value will show the top n most abundant taxa. A value 0 <= n < 1 will show any taxa with that mean abundance or greater (e.g.
0.1
implies >= 10%). A character vector of taxa names will show only those named taxa. Default:6
.- lineage
Include all ranks in the name of the taxa. For instance, setting to
TRUE
will produceBacteria; Actinobacteria; Coriobacteriia; Coriobacteriales
. Otherwise the taxa name will simply beCoriobacteriales
. You want to set this to TRUE whenunc = "asis"
and you have taxa names (such as Incertae_Sedis) that map to multiple higher level ranks. Default:FALSE
- unc
How to handle unclassified, uncultured, and similarly ambiguous taxa names. Options are:
"singly"
-Replaces them with the OTU name.
"grouped"
-Replaces them with a higher rank's name.
"drop"
-Excludes them from the result.
"asis"
-To not check/modify any taxa names.
Default:
"singly"
Abbreviations are allowed.- other
Sum all non-itemized taxa into an "Other" taxa. When
FALSE
, only returns taxa matched by thetaxa
argument. SpecifyingTRUE
adds "Other" to the returned set. A string can also be given to implyTRUE
, but with that value as the name to use instead of "Other". Default:FALSE
- 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
orNULL
-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")
. Seerank()
for details. Default:"random"
- seed
Random seed for permutations. Default:
0
- alt
Alternative hypothesis direction. Options are
'!='
(two-sided; not equal tomu
),'<'
(less thanmu
), or'>'
(greater thanmu
). Default:'!='
- mu
Reference value to test against. Default:
0
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 to2
.
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 taxa_abundance:
sample_sums()
,
taxa_boxplot()
,
taxa_clusters()
,
taxa_heatmap()
,
taxa_stacked()
,
taxa_stats()
,
taxa_sums()
,
taxa_table()
Other visualization:
adiv_boxplot()
,
adiv_corrplot()
,
bdiv_boxplot()
,
bdiv_corrplot()
,
bdiv_heatmap()
,
bdiv_ord_plot()
,
plot_heatmap()
,
rare_corrplot()
,
rare_multiplot()
,
rare_stacked()
,
stats_boxplot()
,
stats_corrplot()
,
taxa_boxplot()
,
taxa_heatmap()
,
taxa_stacked()