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c14bazAAR is an R package to query different openly accessible radiocarbon date databases. It allows basic data cleaning, calibration and merging. If you’re not familiar with R other tools (such as GoGet) to search for radiocarbon dates might be better suited for your needs.

If you want to use data downloaded with c14bazAAR for your research, you have to cite the source databases. Most databases have a preferred way of citation that also may change over time with new versions and publications. Please check the respective homepages to find out more. The output of c14bazAAR does not contain the full citations of the individual dates, but only a short reference tag. For further information you have to consult the source databases.


c14bazAAR is on CRAN and you can install it directly from your R console. To do so, run the following line:


You can also get the development version from github:

if(!require('devtools')) install.packages('devtools')

The package needs a lot of other packages – many of them only necessary for specific tasks. Functions that require certain packages you don’t have installed yet will stop and ask you to enable them. Please do so with install.packages() to download and install the respective packages from CRAN.

How to use

The package contains a set of getter functions (see below) to query the databases. Thereby not every available variable from every archive is downloaded. Instead c14bazAAR focuses on a selection of the most important and most common variables to achieve a certain degree of standardization. The downloaded dates are stored in the custom S3 class c14_date_list which acts as a wrapper around the tibble class and provides specific class methods.

One (almost) complete workflow to download and prepare all dates can be triggered like this:


get_c14data("all") %>%
  calibrate() %>%
  mark_duplicates() %>%
  classify_material() %>%
  finalize_country_name() %>%

It takes quite some time to run all of this and it’s probably not necessary for your use case. Here’s a list of the main tasks c14bazAAR can handle. That allows you to pick what you need:


c14bazAAR contains a growing selection of getter functions to download radiocarbon date databases. Here’s a list of all available databases. You can download all dates at once with get_c14data("all"). The getters download the data, adjust the variable selection according to a defined variable key and transform the resulting list into a c14_date_list.

See ?get_c14data for more information.

x <- get_c14data("all")


The calibrate() function calibrates all valid dates in a c14_date_list individually with Bchron::BchronCalibrate(). It provides two different types of output: calprobdistr and calrange.

See ?calibrate for more information.

x %>% calibrate()

Material classification

Most 14C databases provide some information about the material sampled for the individual date. Unfortunately this information is often very specific and makes filtering operations difficult for large datasets. The function classify_material() relies on a custom made classification to simplify this data.

See ?classify_material for more information and look here for a change log of the thesaurus.

x %>% classify_material()

Country attribution

Filtering 14C dates by country is useful for a first spatial limitation and especially important, if no coordinates are documented. Most databases provide the variable country, but they don’t rely on a unified naming convention and therefore use various terms to represent the same entity. The function standardize_country_name() tries to unify the semantically equal terms by string comparison with the curated country name list countrycode::codelist and a custom made thesaurus. Beyond that it turned out to be much more reliable to look at the coordinates to determine the country.

That’s what the function determine_country_by_coordinate() does. It joins the position with country polygons from rworldxtra::countriesHigh to get reliable country attribution.

The function finalize_country_name() finally combines the initial country information in the database and the results of the two previous functions to forge a single column country_final. If the necessary columns are missing, it calls the previous functions automatically.

See ?country_attribution for more information.

x %>%
  standardize_country_name() %>%
  determine_country_by_coordinate() %>%


Some of the source databases already contain duplicated dates and for sure you’ll have some if you combine different databases. As a result of the long history of these archives, which includes even mutual absorption, duplicates make up a significant proportion of combined datasets. The function mark_duplicates() adds a column duplicate group to the c14_date_list, that assigns duplicates found by lab code comparison a common group number. This should allow you to make an educated decision, which dates to discard.

For an automatic removal there’s the function remove_duplicates(). This functions offers several options how exactly duplicates should be treated.

See ?duplicates for more information.

x %>%
  mark_duplicates() %>%

Coordinate precision

The function coordinate_precision() allows to calculate the precision of the coordinate information. It relies on the number of digits in the columns lat and lon. The mean of the inaccuracy on the x and y axis in meters is stored in the additional column coord_precision.

See ?coordinate_precision for more information.

x %>% coordinate_precision()


A c14_date_list can be directly converted to other R data structures. So far only as.sf() is implemented. The sf package provides great tools to manipulate and plot spatial vector data. This simplifies certain spatial operations with the date point cloud.

See ?as.sf for more information.

x %>% as.sf()

Technical functions

c14_date_lists are constructed with as.c14_date_list. This function takes data.frames or tibbles and adds the c14_date_list class tag. It also calls order_variables() to establish a certain variable order and enforce_types() which converts all variables to the correct data type. There are custom print(), format() and plot() methods for c14_date_lists.

The fuse() function allows to rowbind multiple c14_date_lists.

See ?as.c14_date_list and ?fuse.

x1 <- data.frame(
  c14age = 2000,
  c14std = 30
) %>% as.c14_date_list()

x2 <- fuse(x1, x1)

Plotting radiocarbon data

c14bazAAR only provides a very basic plot function for c14_date_lists. The simple plotting vignette introduces some techniques to help you get started with more sophisticated visualization.


To suggest other archives to be queried you can join the discussion here.


If you would like to contribute to this project, please start by reading our Guide to Contributing. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Adding database getter functions

If you want to add another radiocarbon database to c14bazAAR (maybe from the list here) you can follow this checklist to apply all the necessary changes to the package:

  1. Add your database to the variable_reference table and map the database variables to the variables of c14bazAAR and other databases.
  2. Write the getter function get_[The Database Name] in an own script file: get_[the database name].R. For the script file names we used a lowercase version of the database name. The function name on the other hand can contain upper case letters. The getter functions have a standardized layout and always yield an object of the class c14_date_list. Please look at some of the available functions to get an idea how it is supposed to look like and which checks it has to include.
  3. Add the following roxygen2 tags above the function definition to include it in the package documentation.
#' @rdname db_getter_backend
#' @export
  1. Update the package documentation with roxygen2.
  2. Add the database url(s) to the url_reference table to make sure that get_db_url("[the database name]") works. get_db_url() relies on the file version on the master branch, so maybe you have to find a temporary solution for this as long as you are working in another branch.
  3. Add your function to the database list in the README file here.
  4. Update the material_thesaurus table with all the new material names in the database you want to add and document the changes here. You can test this with classify_material().
  5. Do the same for the country thesaurus table if necessary (standardize_country_name()).
  6. Add the function to the functions vector in get_all_parser_functions().
  7. Document the addition of the new function in the file.


Schmid et al., (2019). c14bazAAR: An R package for downloading and preparing C14 dates from different source databases. Journal of Open Source Software, 4(43), 1914,

  title    = "{c14bazAAR}: An {R} package for downloading and preparing {C14} dates from different source databases",
  author   = "Schmid, Clemens and Seidensticker, Dirk and Hinz, Martin",
  journal  = "Journal of Open Source Software",
  volume   =  4,
  number   =  43,
  pages    = "1914",
  month    =  nov,
  year     =  2019


For the code in this project apply the terms and conditions of GNU GENERAL PUBLIC LICENSE Version 2. The source databases are published under different licenses.