tabula
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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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