Visualize alpha diversity with boxplots.

Visualize alpha diversity with boxplots.

Usage

adiv_boxplot(
  biom,
  x = NULL,
  adiv = "Shannon",
  layers = "x",
  stat.by = x,
  facet.by = NULL,
  colors = TRUE,
  shapes = TRUE,
  patterns = FALSE,
  flip = FALSE,
  stripe = NULL,
  ci = "ci",
  level = 0.95,
  p.adj = "fdr",
  outliers = NULL,
  xlab.angle = "auto",
  p.label = 0.05,
  trans = "none",
  caption = TRUE,
  ...
)

Arguments

biom

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

x

A categorical metadata column name to use for the x-axis. Or NULL, which groups all samples into a single category.

adiv

Alpha diversity metric(s) to use. Options are: "OTUs", "Shannon", "Chao1", "Simpson", and/or "InvSimpson". Set adiv=".all" to use all metrics. Default: "Shannon"

Multiple/abbreviated values allowed.

layers

One or more of c("bar", "box" ("x"), "violin", "dot", "strip", "crossbar", "errorbar", "linerange", "pointrange"). Single letter abbreviations are also accepted. For instance, c("box", "dot") is equivalent to c("x", "d") and "xd". Default: "x"

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

patterns

Patterns for each group. Options are similar to colors's: TRUE, FALSE, NULL, pattern names ("brick", "chevron", "fish", "grid", etc), or a named vector mapping groups to specific pattern names. See "Aesthetics" section below for additional information. Default: FALSE

flip

Transpose the axes, so that taxa are present as rows instead of columns. Default: FALSE

stripe

Shade every other x position. Default: same as flip

ci

How to calculate min/max of the crossbar, errorbar, linerange, and pointrange layers. Options are: "ci" (confidence interval), "range", "sd" (standard deviation), "se" (standard error), and "mad" (median absolute deviation). The center mark of crossbar and pointrange represents the mean, except for "mad" in which case it represents the median. Default: "ci"

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"

outliers

Show boxplot outliers? TRUE to always show. FALSE to always hide. NULL to only hide them when overlaying a dot or strip chart. Default: NULL

xlab.angle

Angle of the labels at the bottom of the plot. Options are "auto", '0', '30', and '90'. Default: "auto".

p.label

Minimum adjusted p-value to display on the plot with a bracket.

• p.label = 0.05 - Show p-values that are <= 0.05.

• p.label = 0 - Don't show any p-values on the plot.

• p.label = 1 - Show all p-values on the plot.

If a numeric vector with more than one value is provided, they will be used as breaks for asterisk notation. Default: 0.05

trans

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

caption

Add methodology caption beneath the plot. Default: TRUE

...

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, d.size = 2 ensures only the points on the dot layer 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".

Patterns are added using the fillpattern R package. Options are "brick", "chevron", "fish", "grid", "herringbone", "hexagon", "octagon", "rain", "saw", "shingle", "rshingle", "stripe", and "wave", optionally abbreviated and/or suffixed with modifiers. For example, "hex10_sm" for the hexagon pattern rotated 10 degrees and shrunk by 2x. See fillpattern::fill_pattern() for complete documentation of options.

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 alpha_diversity: adiv_corrplot(), adiv_stats(), adiv_table()

Other visualization: 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_corrplot(), taxa_heatmap(), taxa_stacked()

Examples

    library(rbiom)
    
    biom <- rarefy(hmp50)
    
    adiv_boxplot(biom, x="Body Site", stat.by="Body Site")

    
    adiv_boxplot(biom, x="Sex", stat.by="Body Site", adiv=c("otu", "shan"), layers = "bld")

    
    adiv_boxplot(biom, x="body", stat.by="sex", adiv=".all", flip=TRUE, layers="p")
#> Error in fun(...): Problem while computing aesthetics.
#> ℹ Error occurred in the 1st layer.
#> Caused by error:
#> ! object 'Body Site' not found
    
    
    # Each plot object includes additional information.
    fig <- adiv_boxplot(biom, x="Body Site")
    
    ## Computed Data Points -------------------
    fig$data
#> # A tibble: 49 × 5
#>    .sample .depth .adiv   .diversity `Body Site`   
#>  * <chr>    <dbl> <fct>        <dbl> <fct>         
#>  1 HMP01     1183 Shannon       1.76 Buccal mucosa 
#>  2 HMP02     1183 Shannon       2.60 Buccal mucosa 
#>  3 HMP03     1183 Shannon       2.92 Saliva        
#>  4 HMP04     1183 Shannon       3.26 Saliva        
#>  5 HMP05     1183 Shannon       1.40 Buccal mucosa 
#>  6 HMP06     1183 Shannon       3.03 Saliva        
#>  7 HMP07     1183 Shannon       1.24 Buccal mucosa 
#>  8 HMP08     1183 Shannon       2.49 Saliva        
#>  9 HMP09     1183 Shannon       3.56 Saliva        
#> 10 HMP10     1183 Shannon       1.72 Anterior nares
#> # ℹ 39 more rows
    
    ## Statistics Table -----------------------
    fig$stats
#> # Model:    wilcox.test(.diversity ~ `Body Site`)
#> # A tibble: 10 × 9
#>    `Body Site`        .mean.diff .h1    .p.val  .adj.p .lower .upper    .n .stat
#>    <fct>                   <dbl> <fct>   <dbl>   <dbl>  <dbl>  <dbl> <int> <dbl>
#>  1 Anterior nares - …    -1.54   != 0  1.83e-4 5.59e-4 -1.87  -1.31     20     0
#>  2 Mid vagina - Sali…    -2.71   != 0  1.83e-4 5.59e-4 -3.03  -2.41     20     0
#>  3 Buccal mucosa - S…    -1.68   != 0  2.46e-4 5.59e-4 -2.19  -0.982    20     1
#>  4 Anterior nares - …    -1.09   != 0  2.80e-4 5.59e-4 -1.35  -0.783    19     0
#>  5 Mid vagina - Stool    -2.23   != 0  2.80e-4 5.59e-4 -2.52  -1.93     19     0
#>  6 Anterior nares - …     1.16   != 0  2.83e-3 3.60e-3  0.771  1.56     20    90
#>  7 Buccal mucosa - M…     1.07   != 0  2.83e-3 3.60e-3  0.381  1.69     20    90
#>  8 Buccal mucosa - S…    -1.16   != 0  2.88e-3 3.60e-3 -1.70  -0.447    19     8
#>  9 Saliva - Stool         0.520  != 0  4.85e-3 5.39e-3  0.186  0.824    19    80
#> 10 Anterior nares - …     0.0170 != 0  9.70e-1 9.70e-1 -0.515  0.584    20    51
    
    ## ggplot2 Command ------------------------
    fig$code
#> ggplot(data, aes(x = `Body Site`, y = .diversity)) +
#>   geom_rect(
#>     mapping = aes(xmin = -Inf, xmax = Inf, ymin = 4, ymax = Inf), 
#>     color   = NA, 
#>     fill    = "white" ) +
#>   geom_boxplot(
#>     mapping  = aes(color = `Body Site`, fill = `Body Site`), 
#>     position = position_dodge(width = 0.8), 
#>     alpha    = 0.6, 
#>     width    = 0.7 ) +
#>   geom_segment(
#>     mapping = aes(x = .x, xend = .xend, y = .y, yend = .yend), 
#>     data    = ~attr(., "stat_brackets") ) +
#>   geom_text(
#>     mapping = aes(x = .x, y = .y, label = .label), 
#>     data    = ~attr(., "stat_labels"), 
#>     parse   = TRUE, 
#>     size    = 3, 
#>     vjust   = 0 ) +
#>   labs(
#>     caption = "Mann-Whitney p-values, with Benjamini & Hochberg FDR correction.", 
#>     y       = "Shannon Diversity" ) +
#>   scale_color_manual(values = c("#1F77B4", "#FF7F0E", "#2CA02C", "#D62728", "#9467BD")) +
#>   scale_fill_manual(values = c("#1F77B4", "#FF7F0E", "#2CA02C", "#D62728", "#9467BD")) +
#>   scale_x_discrete() +
#>   scale_y_continuous(
#>     breaks       = c(0, 1, 2, 3), 
#>     minor_breaks = c(0.5, 1.5, 2.5, 3.5), 
#>     expand       = c(0.02, 0, 0.08, 0) ) +
#>   theme_bw() +
#>   theme(
#>     text               = element_text(size = 14), 
#>     panel.grid.major.x = element_blank(), 
#>     plot.caption       = element_text(face = "italic", size = 9) )