Using data to dynamically build your Résumé or CV makes many powerful integrations possible. By using data to populate entries in the document, it becomes easy to manipulate and select relevant experiences for a particular application. There are many sources of data which can be used to populate a CV with vitae, some commonly sources are summarised in this vignette.
The main purpose of sourcing your CV entries from common data sources is to extend the “do not repeat yourself” programming philosophy to maintaining a CV. If you maintain publications on ORCID you shouldn’t need to repeat these entries in your CV. If a list of talks you’ve made can be found on your website, avoid repeating the list in multiple locations to ensure that they both contain the same content.
This vignette is far from comprehensive, and there are no doubt many other interesting ways to populate your CV with data. If you’re using a data source that you think others should know about, consider making a pull request that adds your method to this vignette.
The simplest source of entries for vitae are maintained dataset(s) of
past experiences and achievements. Just like any dataset, these entries
can be loaded into the document as a
tibble using functions from base R or the
Google Scholar does not require authentication to extract
publications. Using the
package, it is easy to extract a user’s publications from their
Google Scholar ID. To obtain publications for an individual, you would
first find your ID which is accessible from your profile URL. For
example, Rob Hyndman’s ID would be
The vitae package directly supports loading
bibliography_entries() function, which formats
the entries in a bibliography style.
It is also possible to display the contents of your bibliography using template specific entries formats.
A list of R packages that you have helped develop can be obtained