I am a proponent of open science and sharing data and code to improve the reproducibility of archaeological analyses. I share data associated with many of my publications on the Digital Archaeological Repository (tDAR) which can be accessed via the link on the right. Data generated through the Southwest Social Networks and Chaco Social Networks projects are available to researchers via a user agreement with the University of Arizona, Archaeology Southwest, and Arizona State University which can be found here. Also, I am co-PI on the cyberSW project which has made data available from millions of objects from tens of thousands of sites via our web interface. For data associated with specific publications, see the publications page.

The links below represent R-scripts and R Markdown documents associated with my research and teaching. I am in the process of migrating old scripts to Markdown and putting several new analytical tools on GitHub complete with improved documentation, sample datasets, and error checking tools. For code associated with specific publications, see the publications page.

Useful R-Scripts

Binomial Co-occurrence Assessment

This R-script implements a means for statistically assessing the degree of co-occurrence between classes within a tabular dataset based on an idea origially suggested by James Allison and published by Keith Kintigh in 2006. The script produces a square matrix comparing each class to every other class with values representing the number of standard deviations more or less than expected (based on relative frequencies of occurrence) two classes co-occur. This method was used and explained in greater detail in Giomi and Peeples 2019.

Bootstrapped Correspondence Analysis

Additional information for conducting bootstrapped correspondence analysis following the Peeples and Schachner (2012) Journal of Archaeological Science article. This approach uses a bootstrap procedure to estimate sampling error and assess the stability of CA solutions. GitHub link.

Brainerd-Robinson Similarity Coefficient and Error Assessment

Script for calculating Brainerd-Robinson similarity coefficients based on tabular count or percent data. This script also estimates the probability that a given difference between two samples is the product of sampling error when count data are given. GitHub link.

Ceramic Apportioning Method

This is an R implementation of the procedure described by Roberts et al. (2012) “A method for chronologically apportioning of ceramic assemblages” published in the Journal of Archaeological Science. Click here to see a worked example.

Ceramic Frequency Date Plot

This R script produces a exploratory visual for assessing site date ranges originally suggested by Wesley Bernardini. The plot consits of horizontal bars on an x-axis reprenting time with the width determined based on the production span for that type and with the height of each box determined based on the relative frequency of that type with types sorted vertically by start date. The script reads in csv files in the formatted of the example dataset here and produces a pdf containing results for each site.

Ford Diagrams with Error Bars

This R Function creates a simple Ford diagram for archaeological seriation with error bars determined based on sample size where count data are given.

K-Means Cluster Analysis with Tools for “Elbow Method” Assessment of Clustering

Script for conducting K-means cluster analysis and selecting appropriate cluster levels using R. This script provides a series of heuristic approaches to selecting cluster solutions using a Monte Carlo simulation approach. GitHub link.

Mean Ceramic Dates and Sampling Error Assessment

Script for calculating mean ceramic dates (following South 1977) for sites with dated ceramic materials. This script is designed to run for a large number of sites in one csv file and produces MCD estimates as well as estimates of confidence intervals based on Monte Carlo procedure. GitHub link.

Measuring Archaeological Diversity by Comparing to Simulated Assemblages

This R script replicates the analyses presented in : Kintigh, K. 1984. Measuring Archaeological Diversity by Comparison with Simulated Assemblages. American Antiquity 49: 44-54.

Uniform Probability Density Analysis

This is an R implementation of the Uniform Probability Density Analysis approach published by Scott Ortman (2016) “Uniform Probability Density Analysis and Population History in the Northern Rio Grande” published in the Journal of Archaeological Method and Theory. This procedure was also implemented into the cyberSW web platform. See the cyberSW.org for more information. Note that this analysis can also be conducted in your browser directly in the cyberSW website for sites selected from that online database. Click here to see the worked example.

R Markdown Tutorials and Teaching Resources

A Brief Introduction to Networks in R

R Markdown document associated with the workshop at the “Big Data in Archaeology” conference hosted by the McDonald Institute of Archaeological Research in Cambridge, UK (March 2019).

Network science and statistical techniques for dealing with uncertainties in archaeological datasets

R Markdown document associated with the workshop at the Computer Applications and Quantitative Methods in Archaeology Meeting in Atlanta, GA (2017) hosted by Matt Peeples and Tom Brughmans. Network Science and Statistical Techniques for Dealing with Uncertainties in Archaeological Datasets.

Online Companion to Network Science in Archaeology

R Bookdown project that serves as an extensive and detailed companion to the book Network Science in Archaeology by Brughmans and Peeples (2023). This set of tutorials also includes many examples that go beyond the book and will be updated periodically.

Basic Mapping in R

R Markdown document walking through the basic use of map data in R. This document was originally designed to accompany an assignment in my graduate course at ASU: ASB 568: Intrasite Research Strategies in Archaeology.

Point Pattern Analysis in R

This R Markdown document walks through a series of examples of basic point pattern analysis in R including Ripley’s K and related measures, quadrat and spatial tests of inhomogeniety, Poisson point process models, the Pair Correlation Function, and G-hat measures. This document was originally designed to accompany an assignment in my graduate course at ASU: ASB 568: Intrasite Research Strategies in Archaeology.

Local Indicators of Spatial Association (LISA)

This R Markdown document walks through a series of LISA analyses  in R including Moran’s I and Getis-Ord G* analysis. This document was originally designed to accompany an assignment in my graduate course at ASU: ASB 568: Intrasite Research Strategies in Archaeology.

Pure Locational Clustering

This R Markdown document walks through an example of the pure locational clustering approach to spatial analysis of point patterns in R. This includes K-means cluster analysis, DBSCAN density based clustering, fuzzy K-means analysis, as well as additional analyses designed to help parameterize cluster models.  This document was originally designed to accompany an assignment in my graduate course at ASU: ASB 568: Intrasite Research Strategies in Archaeology.

Unconstrained Clustering

This R Markdown document walks through an example of the unconstrained clustering approach to spatial analysis of point or grid data in R (see Whallon 1984; Kintigh 1990). This includes procedures for obtaining grid counts from point located data, K-means cluster analysis, and a procedure for evaluating the statistical significance of homogeneity of artifact types by cluster assignment.  This document was originally designed to accompany an assignment in my graduate course at ASU: ASB 568: Intrasite Research Strategies in Archaeology.