Analysis and Visualization of Archaeological Count Data • tabula

Overview

An easy way to examine archaeological count data. This package 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.). It allows to easily visualize count data and statistical thresholds: rank vs. abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc. tabula provides methods for:

Diversity measurement: heterogeneity(), evenness(), richness(), rarefaction(), turnover().
Similarity measurement and co-occurrence: similarity(), occurrence().
Assessing sample size and significance: bootstrap(), jackknife(), simulate().
Bertin (1977) or Ford (1962) (battleship curve) diagrams: plot_bertin(), plot_ford().
Seriograph (Desachy 2004): seriograph(), matrigraph().
Heatmaps: plot_heatmap(), plot_spot().

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

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 (2024). 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 3.1.1, 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("folio")

## Load the package
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.

## Data from Lipo et al. 2015
data("mississippi", package = "folio")

## Ford diagram
plot_ford(mississippi)

## Co-occurrence of ceramic types
mississippi |> 
  occurrence() |> 
  plot_spot()

## Data from Conkey 1980, Kintigh 1989, p. 28
data("chevelon", package = "folio")

## Measure diversity by comparing to simulated assemblages
set.seed(12345)

chevelon |>
  heterogeneity(method = "shannon") |>
  simulate() |>
  plot()

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

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.

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