Simple tasks with the SignBase dataset

prepare-radiocarbon-data-for-analyses-signbase.R

library(tidyverse)
library(rcarbon)

signbase <- read_csv("signBase_Version1.0.csv")

# clean up and separate the data in the radiocarbon age columns
# so we can use it for plotting and analyses. This will generate some
# warnings, that's ok, nothing to worry about
signbase_years <- 
  signbase %>% 
  drop_na(date_bp_max_min) %>% 
  mutate(date_bp_max_min = str_replace_all(date_bp_max_min, "\\+\\/\\-", "±")) %>% 
  mutate(date_bp_max_min = str_replace_all(date_bp_max_min, "\\+", "±")) %>% 
  separate(date_bp_max_min,
           sep = " - ",
           c("date_bp_max", "date_bp_min"),
           remove = FALSE) %>% 
  mutate(date_bp_max = ifelse(str_detect(date_bp_max, "\\/"),
                              str_extract(date_bp_max, ".*?(?=\\/)"),
                              date_bp_max)) %>% 
  mutate(date_bp_min = ifelse(str_detect(date_bp_min, "\\/"),
                              str_extract(date_bp_min, ".*?(?=\\/)"),
                              date_bp_min)) %>% 
  separate(date_bp_max,
           sep = "±",
           c("date_bp_max_age",
             "date_bp_max_error")) %>% 
  separate(date_bp_min,
           sep = "±",
           c("date_bp_min_age",
             "date_bp_min_error")) %>% 
  drop_na(date_bp_max_age,
          date_bp_max_error) %>% 
  mutate(date_bp_max_age = parse_number(date_bp_max_age),
         date_bp_max_error = parse_number(date_bp_max_error))

# calibrate the radiocarbon ages and join the calibration results back
# to the main data frame of all the object, location, and sign data.
# Use this signbase_years_cal data frame for all your anlyses now.
# Use the column "MedianBP" for visualising and analysing ages. That
# is calendar years Before the Present
signbase_years_cal <- 
  calibrate(signbase_years$date_bp_max_age,
            signbase_years$date_bp_max_error,
            ids = signbase_years$object_id,
            verbose = FALSE) %>% 
  summary() %>% 
  tibble() %>% 
  left_join(signbase,
            join_by("DateID" == "object_id"))

# for example, we can make a histogram to see which time periods are most
# frequently represented in the data
ggplot(signbase_years_cal) +
  aes(MedianBP) +
  geom_histogram()

simple-map-of-the-sites-in-SignBase.R

library(tidyverse)
library(rnaturalearth)
library(ggrepel)

# import the data from the paper https://www.nature.com/articles/s41597-020-00704-x
signbase <- read_csv("signBase_Version1.0.csv")

# prepare simple features objects for a base map of the coastlines
# and country boundaries of Europe
world <- ne_countries(scale = "medium", returnclass = "sf")
Europe <- world[which(world$continent == "Europe"),]

# make a map of the signbase sites!
ggplot() +
  # plot the base map of European countries and coastlines
  geom_sf(data = Europe) +
  # crop the map to zoom in on the parts of Europe where the sites are
  coord_sf(xlim = c(-10,30), 
           ylim = c(35,53), 
           expand = FALSE) +
  # add the layer of signbase sites, here I'm using the raw data, but you can 
  # also use a data frame that you have done some calculations on. You just need
  # the longitude and latitude columns to be present for this to work
  geom_point(data = signbase,
          # point colour indicates 'country', feel free to explore and 
          # change this to other columns in the signbase_sf data frame
          aes(x = longitude,
              y = latitude,
              # point colour indicates 'country', feel free to explore and 
              # change this to other columns in the signbase data frame
              colour = country)) +
  # add labels for the site names
  geom_text_repel(data = signbase %>% 
                    # only one label per site, otherwise it's too croweded to read
                    distinct(site_name,
                             .keep_all = TRUE),
                  aes(x = longitude ,
                      y = latitude,
                      label = site_name),
                  max.overlaps = 100,
                  size = 2) +
  theme_minimal() 

Here's the map that this code produces

Here's the histogram of the radiocarbon data: