Plot the alpha diversity using a violin plot. alpha_diversity_plots generates plots for all alpha diversity measures.
alpha_diversity_plot(obj, measure = "Shannon", group = "TreatmentGroup", select_otu_table = NULL, title = NULL) alpha_diversity_plots(obj, measures = c("Shannon", "GiniSimpson", "InverseSimpson"), group = "TreatmentGroup")
| obj | An object to be converted to a Taxmap object with |
|---|---|
| measure | Select an alpha diversity measure such as Shannon, Fisher, Coverage, GiniSimpson, and InverseSimpson, Default: 'Shannon' |
| group | The "TreatmentGroup" or similar grouping or column from your metadata to denote sample groups, Default: 'TreatmentGroup' |
| select_otu_table | DEPRECATED. Choose an otu table to analyze, Default: 'otu_proportions' |
| title | The title of the plot, Default: NULL |
| measures | A list of alpha diversity measures such as Shannon, Fisher, Coverage, GiniSimpson, and InverseSimpson, Default: 'c("Shannon", "GiniSimpson", "InverseSimpson")' |
Returns an alpha diversity plot.
Returns a melted dataframe.
Alpha diversity helps to determine the species richness (the number of different species in a sample) or evenness (similar abundance level).
We prefer to use Shannon as it is better for data generated using the QIIME pipeline.
alpha_diversity_measures, diversity, ggviolin
Other Visualizations: correlation_data,
correlation_plots,
correlation_plot,
heat_tree_parameters,
heat_tree_plots,
ordination_plots,
ordination_plot, plot_limits,
save_alpha_diversity_plots,
save_correlation_plots,
save_heat_tree_plots,
save_ordination_plots,
save_stacked_barplots,
stacked_barplots,
stacked_barplot,
top_coefficients_barplot
Other Visualizations: correlation_data,
correlation_plots,
correlation_plot,
heat_tree_parameters,
heat_tree_plots,
ordination_plots,
ordination_plot, plot_limits,
save_alpha_diversity_plots,
save_correlation_plots,
save_heat_tree_plots,
save_ordination_plots,
save_stacked_barplots,
stacked_barplots,
stacked_barplot,
top_coefficients_barplot
# NOT RUN { if (interactive()) { library(MicrobiomeR) data <- analyzed_silva plot <- alpha_diversity_plot(obj = data, measure = "Shannon") plot } # }