Examples

This vignette demonstrates examples of chronological datasets which have been compiled from the literature. They are included in the package (see first example) or alternatively can be downloaded here.

Examples 1: Chonological table

This example illustrates the main purpose of the package: facilitating the hassle free drawing of chronological tables. Many archaeological cultures have competing chronological systems or temporal shifts in their sub-groups and/or spatial distributions. This example highlights regional chronological differences of the Urnfield Culture and the phases are an extract of a table presented by St. Knöpke (2009, p. 15).

First, loading of the data: If the file is already in your current working directory, use

# Data from St. Knöpke, Der urnenfelderzeitliche Männerfriedhof von Neckarsulm. 
# Konrad Theiss Verlag (Stuttgart 2009), p. 15.
UC_Chronology <- import_chron(path = "ex_urnfield_periods.xlsx",
                               "Region", "Name", "Start", "End", "Level")

To access it directly from the package, use

UC_Chronology <- import_chron(path = system.file("extdata/ex_urnfield_periods.xlsx", package = 'chronochrt'),
                               "Region", "Name", "Start", "End", "Level")

Then, create the chronological chart by:

 plot_chronochrt(UC_Chronology, 
                 axis_title = "BCE", 
                 size_text = 4, 
                 line_break = 22, 
                 filename = "UC-Chronology.png", plot_dim = c(16, 10, "cm"))

And that’s it! Because a file name as well as physical dimensions were provided, the chart would be saved right away as “UC-Chronology.png” in your working directory, with the specified size of 16x10 cm when running the code. It would look like this:

 plot_chronochrt(UC_Chronology, 
                 axis_title = "BCE", 
                 size_text = 4, 
                 line_break = 22)

Example 2: Occupation phases and other data

Additionally, the package can be used to display any kind of temporal information. The following example ‒ whilst being very circumstantial in connections of the data ‒ highlights how different types of temporal data can be merged. This dataset is partially based on the cemetery data of the Wellcome Osteological Research Database (https://www.museumoflondon.org.uk/collections/other-collection-databases-and-libraries/centre-human-bioarchaeology/osteological-database) as well as general information for the labels. According to place of burial - during this time a partial indicator of socio-economic status - some cemeteries were placed in groups (the region). Their occupation phases were entered through the start and end arguments. Further, the death numbers of major plague events were added as a separate region.

Again, the first step is to load the chronological dataset:

London_cemeteries <- import_chron(path = "ex_London_cem.xlsx", package = 'ChronochRt'),
                                  "Region", "Name", "Start", "End", "Level")

Then add some labels, e.g. ‘12.04.1665 - The “Great Plague of London” begins’ as well as some numbers from London’s plague mortality bills and other interesting facts via the following code in different parts of the plot:

London_labels <-add_label_text(region = "low socio-economic status",
                               year = 1665,
                               label = "12.04.1665:\n The \"Great Plague of London\"\n begins",
                               position = 1.98, 
                               new = TRUE) %>%
               add_label_text(region = "urban",
                              year = c(1559, 1660, 1670),
                              label = c("1559: Coronation of Elizabeth I ", "1664: Sighting of a bright comet", "1666: Great Fire of London"),
                              position = 1.98,
                              new =  FALSE) %>% 
              add_label_text(region = "plague death toll",
                              year = c(1350, 1563, 1593, 1603, 1625, 1636, 1647, 1665),
                              label = c( "1346-1353: ~62,000","1563-1564: 20 136" , "1593: 15 003","1603: 33 347", "1625: 41,313", "1636: 10 000", "1647: 3,597" ,"1665: 68 596"),
                              position = 0.75,
                              new =  FALSE)

And now, create the graph:

 plot_chronochrt(data = London_cemeteries, 
                 labels_text = London_labels, 
                 size_text = 3, 
                 line_break = 25, 
                 color_line = "grey55")