# Examples

To execute the examples shown in this vignette, load the package:

    library(chronochrt)

## 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.londonmuseum.org.uk/collections/research/bioarchaeological-research/) 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", "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"
    )