Tools for archaeometry and archaeometallurgy in R
Install from GitHub
# installinstall.packages("devtools")
devtools::install_github("iramat/iRamat")Load the iRamat package
library(iRamat)Connect the database API with the default parameters, and show the first row, using the db_api_connect() function
df <- db_api_connect()The default dataset is dataset_adisser17
names(df)
# [1] "dataset_adisser17"The dataset can by accessed by its name:
head(df$dataset_adisser17, 2)| site_name | id_chips | sample_name | typology | na | mg | al | si | p | s | cl | k | ca | mn | fe | loi | ag | arsenic | ba | be | bi | cd | ce | co | cr | cs | cu | dy | er | eu | deltafe56 | deltafe57 | ga | gd | ge | hf | ho | indium | la | li | lu | mo | nb | nd | ni | os | os187_os188 | os187_os186 | pb | pd | pr | rb | ru | sb | sc | se | sm | sn | sr | sr87_sr86 | ta | tb | te | th | ti | tl | tm | u | v | w | y | yb | zn | zr | major_method | major_analytical_setup | trace_method | trace_analytical_setup | reference | url |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Aux Minières | 4967 | MINHAO108-A | NA | 0.04 | 0.03 | 3.23 | 4.45 | 0.04 | 0.00 | 0 | 0.07 | 0.09 | 0.03 | 53.17 | 7.90 | NA | 578.900 | 20.31 | 13.500 | 0.308 | 0.325 | 56.06 | 26.590 | 214.9 | 0.971 | 5.587 | 5.025 | 2.637 | 1.387 | NA | NA | 7.478 | 4.960 | 2.61 | 1.431 | 0.938 | 0.475 | 21.24 | NA | 0.372 | 5.362 | 2.808 | 24.37 | 63.140 | NA | NA | NA | 113.8444 | NA | 6.119 | 4.692 | NA | 127.50 | 1.342 | NA | 6.095 | 0.945 | 43.73 | NA | 0.227 | 0.843 | NA | 17.38 | 0.092 | NA | 0.397 | 9.352 | 857.3 | 0.563 | 22.17 | 2.776 | 112.60 | 58.04 | ICP-OES | CRPG - Thermo Fisher Scientific Icap 6500 | ICP-OES | CRPG - Thermo Fisher Scientific Icap 6500 | Alexandre Disser, Philippe Dillmann, Marc Leroy, Maxime L'Héritier, Sylvain Bauvais, Philippe Fluzin (2017), Iron Supply for the Building of Metz Cathedral: New Methodological Development for Provenance Studies and Historical Considerations, Archaeometry, 59 | https://onlinelibrary.wiley.com/doi/full/10.1111/arcm.12265 |
The chrono() function models the chronological attribution of the site by creating a timeline:
df <- db_api_connect()
chrono(d = df$dataset_adisser17)
PeriodO periods can also be displayed. The default Periodo authority (i.e. a set of different periods identified by the same author) is INRAP: Institut National de Recherches Archeologiques Preventive.
periodo(min_date = -700, max_date = 0, use_periodo = TRUE, time_match = 1)
PeriodO records the spatial extent of periods. In the periodo() function, this spatial extent is represented by the variable location. Here, the PeriodO authority ArkeoGIS authors is used for periods in France only.
periodo(periodo_authority = "http://n2t.net/ark:/99152/p09hq4n", min_date = -500, max_date = 500,
use_periodo = TRUE, time_match = 1, location = "France")
Site and PeriodO timelines can be merged into a single plot:
df <- db_api_connect()
plots <- chrono(df$dataset_adisser17, use_periodo = TRUE)
ggpubr::ggarrange(plots$sites, plots$periodo$periodo,
heights = c(1, 2), ncol = 1, align = "v")
The ppa() function performs different point pattern analysis (PPA) on raster grids or spatial data. It could be used to assess if a point distribution is regular, clustered or random.
| regular | clustered | random |
|---|---|---|
Run the function with its default parameters:
d <- ppa()d is a hash-like object (similar to a Python dictionary) that stores different test outputs: Quadrat test, K-Ripley test, G-function test. Let's call some of these results:
Check the Quadrat test of the clustered distribution
d[["clustered_distribution.png"]]$quadrat Chi-squared test of CSR using quadrat counts
data: pp
X2 = 732.01, df = 24, p-value < 2.2e-16
alternative hypothesis: two.sided
Quadrats: 5 by 5 grid of tiles
plot(d[["clustered_distribution.png"]]$ripley, main = "clustered distribution")
plot(d[['regular_distribution.png']]$gfunction, main = "regular distribution")