Data and R scripts for the paper 'Regional variations in the demographic response to the arrival of rice farming in prehistoric Japan'
This repository contains data and scripts used in the following paper:
Crema, E. R., Carrignon, S., Shoda, S., & Stevens, C. J., & Shoda, S. (In press). Regional variations in the demographic response to the arrival of rice farming in prehistoric Japan. Antiquity.
The repository is organised into five main directories: data, analyses, results, figures, tables, and src. The data folder contains all relevant radiocarbon and settlement data, analyses contains core R scripts for executing all analyses and estimates, results contains R image files of all outputs, figures and tables contain all figures and tables for the manuscript and the supplementary materials as well as R scripts required to generate them, and src contains additional custom utility R functions.
Settlement data were obtained from the 「縄文・弥生集落データベース」("Jomon-Yayoi settlement database") of the National Museum of Japanese History, and stored as a CSV file (data/site_raw.csv
). The file data/data_prep_sites.R
contains data wrangling R scripts that convert relevant data into an R data.frame stored in the R image file data/sitedata.RData
. Scripts for the composite Kernel Density estimates (see manuscript and ESM for details) are stored in the R file figures/figure2.R
. Pipeline: data/site_raw.csv
→ data/sitedata.RData
.
14C data were obtained from radiocarbon database of the National Museum of Japanese History. We joined the cleaned and translated version of the database (c14db_1.1.0.csv
, obtained here) to the full Japanese version of the database (.xslx
files in /data/rekihaku_downloads
, obtained here) to extract relevant field for identifying anthropogenic contexts. The file data/data_prep_c14.R
contains pre-processing R scripts for generating the core dataset used in this paper, stored in the R image file data/c14data.RData
. Bayesian analyses were carried out using the nimbleCarbon R package, which contains custom probability distributions and utility functions for the NIMBLE probabilistic programming language. The files analyses/icar500.R
and analyses/icar750.R
contain R scripts for fitting the Bayesian models for the 500 and 750 yrs time intervals. Posterior samples are stored in the R image files results/icar_c14doubleRes500.RData
and results/icar_c14doubleRes750.RData
.
Pipelines:
data/rekihaku_downloads/1200_100_T.xlsx
&data/rekihaku_downloads/2000_1201_T.xlsx
&data/rekihaku_downloads/3000_2001_T.xlsx
&data/rekihaku_downloads/5000_3001_T.xlsx
→data/rekihaku_downloads/bindCSV.R
→data/rekihaku_downloads/binded.csv
.data/rekihaku_downloads/binded.csv
&data/rekihaku_downloads/c14db_1.1.0.csv
→data/data_prep_c14.R
→data/c14data.RData
.data/c14data.RData
→analyses/icar500.R
→results/icar_c14doubleRes500.RData
data/c14data.RData
→analyses/icar750.R
→results/icar_c14doubleRes750.RData
Absolute population estimates were calculated using a modified version of the equation introduced by Koyama (Koyama, S.1978. Jomon Subsistence and Population. Senri Ethnological Studies, 2, 1–65.) with an updated dataset. The core calculations are included as an R script in the file analyses/pop_dens_est.R
, with the outputs stored in the CSV files results/pop_estimate_compare.csv
(comparison of the different estimates discussed in the supplementary materials) and results/pop_estimate_region.csv
(estimates used in table 1 of the manuscript). Raw input data required for the calculations are Koyama's original data (data/koyama_popestimate_1984.csv
), the number of archaeological sites published by the Japanese Agency of Cultural Affairs (data/maizobunkazai_2017.csv
), and a lookup table for matching administrative units (prefectures) to the regions used in this paper (data/prefecture_data.csv
). Pipeline: data/maizobunkazai_2017.csv
& data/prefecture_data.csv
& data/koyama_popestimate_1984.csv
→
analyses/pop_dens_est.R
→ results/pop_estimate_compare.csv
& results/pop_estimate_region.csv
.
Main (figures/figure1.pdf
~ figures/figure6.pdf
) and supplementary (figures/figureS1.pdf
~ figures/figureS3.pdf
) are generated using the Rscript in figures/figures_main.R
and figures/figures_esm.R
.
Pipelines:
- Main Figures:
data/c14data.RData
&data/sitedata.RData
&results/icar_c14doubleRes500.RData
&results/icar_c14doubleRes750.RData
→figures/figures_main.R
→figures/figure1.pdf
~figures/figure6.pdf
- Supplementary Figures:
results/pop_estimate_compare.csv
→figures/figures_esm.R
→figures/figureS1.pdf
~figures/figureS3.pdf
c14data.RData
data_prep_c14.R
data_prep_sites.R
data_summary.R
koyama_popestimate_1984.csv
maizobunkazai_2017.csv
prefecture_data.csv
sitedata.RData
site_raw.csv
1200_100_T.xlsx
2000_1201_T.xlsx
3000_2001_T.xlsx
5000_3001_T.xlsx
c14db_1.1.0.csv
binded.csv
bindCSV.R
icar500.R
icar750.R
pop_dens_est.R
icar_c14doubleRes500.RData
icar_c14doubleRes750.RData
pop_estimate_compare.csv
pop_estimate_region.csv
figures_main.R
figures_esm.R
figure1.pdf
~figure6.pdf
figureS1.pdf
~figureS3.pdf
tables_main.R
tables_esm.R
table1.csv
tableS1.csv
tableS2.csv
dbscanID.R
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] dbscan_1.1-11 spdep_1.2-8 spData_2.2.2 coda_0.19-4
[5] sf_1.0-13 rnaturalearth_0.3.2 latex2exp_0.9.6 RColorBrewer_1.1-3
[9] here_1.0.1 rcarbon_1.5.1 nimbleCarbon_0.2.4 nimble_1.0.1
[13] gridExtra_2.3 dplyr_1.1.2 ggplot2_3.4.2
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 fastmap_1.1.1 spatstat.geom_3.2-2
[4] pracma_2.4.2 spatstat.explore_3.2-1 digest_0.6.31
[7] rpart_4.1.19 lifecycle_1.0.3 spatstat.data_3.0-1
[10] magrittr_2.0.3 compiler_4.3.0 rlang_1.1.1
[13] doSNOW_1.0.20 tools_4.3.0 igraph_1.5.0
[16] utf8_1.2.3 yaml_2.3.7 knitr_1.43
[19] sp_1.6-1 classInt_0.4-9 abind_1.4-5
[22] KernSmooth_2.23-20 withr_2.5.0 purrr_1.0.1
[25] numDeriv_2016.8-1.1 grid_4.3.0 polyclip_1.10-4
[28] fansi_1.0.4 e1071_1.7-13 colorspace_2.1-0
[31] progressr_0.13.0 scales_1.2.1 iterators_1.0.14
[34] spatstat.utils_3.0-3 spatstat_3.0-6 cli_3.6.1
[37] rmarkdown_2.21 generics_0.1.3 rstudioapi_0.14
[40] httr_1.4.6 DBI_1.1.3 proxy_0.4-27
[43] stringr_1.5.0 splines_4.3.0 spatstat.model_3.2-4
[46] s2_1.1.4 vctrs_0.6.3 boot_1.3-28.1
[49] Matrix_1.5-4 jsonlite_1.8.4 tensor_1.5
[52] elevatr_0.4.5 foreach_1.5.2 units_0.8-2
[55] snow_0.4-4 goftest_1.2-3 glue_1.6.2
[58] spatstat.random_3.1-5 codetools_0.2-19 stringi_1.7.12
[61] gtable_0.3.3 deldir_1.0-9 munsell_0.5.0
[64] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.5
[67] R6_2.5.1 wk_0.7.3 rprojroot_2.0.3
[70] evaluate_0.21 lattice_0.21-8 class_7.3-21
[73] Rcpp_1.0.11 spatstat.linnet_3.1-1 nlme_3.1-162
[76] spatstat.sparse_3.0-2 mgcv_1.8-42 xfun_0.39
[79] pkgconfig_2.0.3
This research was funded by the ERC grant Demography, Cultural Change, and the Diffusion of Rice and Millets during the Jomon-Yayoi transition in prehistoric Japan (ENCOUNTER) (Project N. 801953, PI: Enrico Crema) and by a Philip Leverhulme Prize (PLP-2019-304) in archaeology awarded to Enrico Crema.
CC-BY 3.0