tabula

Overview

An easy way to examine archaeological count data. tabula provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). The package make it easy to visualize count data and statistical thresholds: rank vs. abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams.

kairos is a companion package to tabula that provides functions for chronological modeling and dating of archaeological assemblages from count data.

Prior to version 2.0.0, tabula included various matrix seriation methods. These methods have been moved to kairos, so that we no longer have chronological tools in two separate packages.

To cite tabula in publications use:

  Frerebeau N (2019). "tabula: An R Package for Analysis, Seriation,
  and Visualization of Archaeological Count Data." _Journal of Open
  Source Software_, *4*(44). doi:10.21105/joss.01821
  <https://doi.org/10.21105/joss.01821>.

  Frerebeau N (2022). _tabula: Analysis and Visualization of
  Archaeological Count Data_. Université Bordeaux Montaigne, Pessac,
  France. doi:10.5281/zenodo.1489944
  <https://doi.org/10.5281/zenodo.1489944>, R package version 2.0.0,
  <https://packages.tesselle.org/tabula/>.

This package is a part of the tesselle project
<https://www.tesselle.org>.

Installation

You can install the released version of tabula from CRAN with:

install.packages("tabula")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("tesselle/tabula")

Usage

## Install extra packages (if needed)
# install.packages("khroma")

## Load packages
library(folio) # Datasets
library(tabula)

It assumes that you keep your data tidy: each variable (type/taxa) must be saved in its own column and each observation (sample/case) must be saved in its own row.

Visualization

Several types of graphs are available in tabula which uses ggplot2 for plotting informations. This makes it easy to customize diagrams (e.g. using themes and scales).

Bertin or Ford (battleship curve) diagrams can be plotted, with statistic threshold (including B. Desachy’s sériographe).

## Bertin matrix with variables scaled to 0-1 and the variable mean as threshold
scale_01 <- function(x) (x - min(x)) / (max(x) - min(x))

## Data from Lipo et al. 2015
plot_bertin(mississippi, threshold = mean, scale = scale_01) +
  khroma::scale_fill_vibrant(name = "Mean")

## Ford diagram
plot_ford(mississippi)

Spot matrix allows direct examination of data:

## Plot co-occurrence of types
## (i.e. how many times (percent) each pairs of taxa occur together 
## in at least one sample.)
mississippi |> 
  occurrence() |> 
  plot_spot() +
  khroma::scale_colour_YlOrBr()

Diversity

Diversity can be measured according to several indices (referred to as indices of heterogeneity – see vignette("diversity")). Corresponding evenness (i.e. a measure of how evenly individuals are distributed across the sample) can also be computed, as well as richness and rarefaction.

heterogeneity(mississippi, method = "shannon")
#>  [1] 1.2027955 0.7646565 0.9293974 0.8228576 0.7901428 0.9998430 1.2051989
#>  [8] 1.1776226 1.1533432 1.2884172 1.1725355 1.5296294 1.7952443 1.1627477
#> [15] 1.0718463 0.9205717 1.1751002 0.7307620 1.1270126 1.0270291

Measure diversity by comparing to simulated assemblages:

set.seed(12345)

## Data from Conkey 1980, Kintigh 1989, p. 28
chevelon |>
  heterogeneity(method = "shannon") |>
  simulate() |>
  plot()

chevelon |>
  richness(method = "count") |>
  simulate() |>
  plot()

Several methods can be used to ascertain the degree of turnover in taxa composition along a gradient on qualitative (presence/absence) data. It assumes that the order of the matrix rows (from 1 to n) follows the progression along the gradient/transect.

Diversity can also be measured by addressing similarity between pairs of sites:

## Calculate the Brainerd-Robinson index
## Plot the similarity matrix
s <- similarity(mississippi, method = "brainerd")

plot_spot(s) +
  khroma::scale_colour_iridescent(name = "brainerd")

Contributing

Please note that the tabula project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

References

Bertin, Jacques. 1977. La graphique et le traitement graphique de l’information. Nouvelle bibliothèque scientifique. Paris: Flammarion.

Ford, J. A. 1962. A Quantitative Method for Deriving Cultural Chronology. Technical Manual 1. Washington, DC: Pan American Union.