Introduction to vitae

The vitae package makes creating and maintaining a Résumé or CV with R Markdown simple. It provides a collection of LaTeX templates, with helpful functions to add content to the documents. These functions allow you to dynamically include CV entries from any data source, which is particularly useful when this data is obtained/prepared by other R packages. Some examples of what this allows you to do includes:

Creating a CV

If using RStudio, a new CV document can be easily produced from one of the templates provided in the package. This using the RStudio R Markdown template selector, accessible via File > New File > R Markdown..., and lastly From Template. This will show a list of R Markdown templates provided by all installed packages, and you should be able to find some templates from the vitae package to use.

If not using RStudio, you can create a new *.Rmd document and use an output format that is provided by the package. A list of output formats provided by the package can be found with ?cv_formats. Examples of a YAML header that use one of these output formats is shown below.

Like other R Markdown documents, the file is split into two sections: the YAML header, and the main body.

The YAML header

The YAML header contains general entries that are common across many templates, such as your name, address and social media profiles. This is also where the output format (the CV template used) is specified, along with any options that the template supports. An example of what this header may look like is shown below:

---
name: Mitchell O'Hara-Wild
date: "`r format(Sys.time(), '%B, %Y')`"
profilepic: pic.jpg
www: mitchelloharawild.com
github: mitchelloharawild
linkedin: mitchelloharawild
twitter: mitchoharawild
headcolor: 414141
docname: CV/Resume
output: vitae::awesomecv

You can also see that the output is set to vitae::awesomecv, which indicates that this CV uses the Awesome CV template.

Currently, the YAML header allows these fields to be specified:

The document body

Like other R Markdown documents, the body allows you to mix R code with markdown to create the main content for your document. Below is an example of the start for a typical CV:


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
library(vitae)
```

# Professional Summary

This is a good opportunity to introduce yourself professionally and summarise

The setup chunk is useful to load in any packages that you may use, and also prevent R code and warnings/notes from appearing in your CV. The above code also includes a professional summary using markdown syntax, which will appear in the final CV.

Unlike other R markdown formats, the vitae package and its templates support functions to generate CV entries from data: detailed_entries() and brief_entries(). Both functions provide sections for what, when, and with, and the detailed_entries() additionally supports where and why. They use an interface similar to dplyr, in that the data can be piped (%>%) into these functions, and the arguments can involve some calculations.

Detailed entries

Let’s add to the main body with some education history. These details are available on ORCID with my ID of 0000-0001-6729-7695, which can be dynamically accessed using the rorcid package.


# Education

```{r education}
library(vitae)
edu <- do.call("rbind",
  rorcid::orcid_educations("0000-0001-6729-7695")$`0000-0001-6729-7695`$`affiliation-group`$summaries
)
edu %>%
  detailed_entries(
    what = `education-summary.role-title`,
    when = glue::glue("{`education-summary.start-date.year.value`} - {`education-summary.end-date.year.value`}"),
    with = `education-summary.organization.name`,

In the example above, the glue package has been used to combine the start and end years for our when input. Excluding any arguments is also okay (as is done for why), it will just be left blank in the CV.

Brief entries

Brief entries can be included with the same interface as detailed_entries(), and is appropriate for entries that do not need as much detail (such as skills). Another application of this is to include a list of R packages that you have published to CRAN using the pkgsearch package.

  )
```

# R Packages

```{r rpkgs}
pkgsearch::ps("O'Hara-Wild",size = 100) %>%
  filter(purrr::map_lgl(package_data, ~ grepl("Mitchell O'Hara-Wild", .x$Author, fixed = TRUE))) %>%
  arrange(desc(downloads_last_month)) %>%
  brief_entries(
    what = title,
    when = lubridate::year(date),

This example also uses several other packages to prepare the data: - dplyr to filter() my contributed packages, and arrange() the data by downloads - purrr to map over package_data column to find packages I’ve contributed to - lubridate to display only the year from the date column

Bibliography entries

The package also supports bibliography entries from a *.bib file using the bibliography_entries() function. This outputs the contents of a bibliography using a citation style, and is suitable for CVs containing publications.

  )
```