Author: Erwin Lares

# Why curriculr: The Case for a Data-Driven CV

#### Erwin Lares

#### Created 2026-04-30 | Last updated 2026-05-08

## The problem with maintaining a CV

Most people maintain their CV the same way: open a Word document or a PDF, find the right section, type a new line, fix the formatting that broke, adjust the spacing, export, and hope it looks right. Then do it again six months later when they need a different version for a different audience.

This approach has a name in software development: it is called manual data entry into a presentation layer, and it is exactly the kind of work that software exists to eliminate.

The fundamental problem is that a CV mixes two things that should be kept separate: content (what you have done, where you have been, what you have published) and presentation (fonts, margins, section order, color). When these are bundled together in a single Word document, changing one risks breaking the other. Updating your job title can knock a section onto the wrong page. Changing the font size to fit more content can cascade through the entire document.

curriculr separates them.

## The curriculr model

In curriculr, content lives in an Excel workbook. Each sheet corresponds to one section of the CV. When you get a new publication, you add a row to the `publications` sheet. When you give a talk, you add a row to `presentations`. You never touch the layout.

Presentation lives in `CV.qmd` – a Quarto document that reads the workbook and renders a PDF using Typst. The document handles fonts, margins, colors, section headings, date formatting, and the two-column entry layout. You do not touch it unless you want to change the visual design.

The pipeline is:

    Excel workbook  ->  R (curriculr)  ->  Quarto + Typst  ->  CV.pdf

When you want an updated PDF, you call:

    create_cv(
      data  = "cv-data.xlsx",
      photo = "me.jpeg"
    )

That is the entire workflow.

## How curriculr compares to other approaches

### Word or Pages documents

The most common approach. The content and formatting are inseparable, which makes consistent updates difficult. Producing multiple versions for different audiences – a full CV, a short resume, a teaching-focused version – means maintaining multiple documents that quickly drift out of sync.

curriculr keeps one source of truth. Different versions will be supported in a future release via parameters that filter sections and entries at render time.

### LaTeX CVs

LaTeX separates content from presentation better than Word, and produces beautiful PDFs. The barrier is real: LaTeX requires a working installation, knowledge of the language, and tolerance for cryptic error messages. Custom CV templates like Awesome CV add their own layer of complexity through `.cls` files and `.sty` dependencies.

curriculr uses Typst instead of LaTeX. Typst is faster, has cleaner error messages, and requires no installation beyond Quarto. The Typst layout in `CV.qmd` is transparent R-adjacent code rather than a black-box class file.

### vitae

The `vitae` R package is the closest prior art to curriculr and deserves direct acknowledgment – curriculr was inspired by it. vitae provides a family of CV templates built on R Markdown, including an Awesome CV template and several others. It handles the rendering pipeline and provides helper functions like `detailed_entries()` that map data frames to CV entries.

Where curriculr differs:

- curriculr uses Quarto and Typst rather than R Markdown and LaTeX. This removes the LaTeX dependency entirely and produces faster renders with cleaner error messages.
- curriculr uses Excel as the data source rather than R data frames or CSV files. For many researchers, a spreadsheet is a more natural editing environment than code.
- curriculr puts section control in the workbook. Adding, removing, or reordering sections requires no R code changes – only editing the `sections` sheet.
- curriculr is a single-function workflow. `create_cv()` handles scaffolding, template injection, and rendering in one call.

### datadrivencv

Nick Strayer’s `datadrivencv` package shares curriculr’s philosophy of treating the CV as structured data. It uses Google Sheets as the data source and `pagedown` for rendering. It remains an influential design and is worth examining for anyone interested in the data-driven CV space.

curriculr’s differences: local Excel instead of Google Sheets (no authentication required, works offline), Typst instead of pagedown (PDF-first rather than HTML-first), and active maintenance.

## The Excel advantage

Using Excel as the data source is a deliberate choice that some R users will find surprising. A few reasons it works well in practice:

It is already where the data lives. Most academics and professionals already track their accomplishments in some kind of spreadsheet. Asking them to maintain their CV data in the same tool they already use lowers the barrier to keeping it current.

It is accessible to collaborators. A department administrator helping maintain a faculty member’s CV can edit an Excel file without knowing R. The schema is documented in the workbook’s own `readme` sheet.

It supports structured data naturally. Each sheet is a table. Dates are dates. Text is text. The schema is explicit and consistent. Compared to editing a Word document, adding a new publication to a spreadsheet row is a precise, reversible, auditable operation.

The workbook is the documentation. The `readme` sheet explains the column schema, the date conventions, and the sections control system. A new user can open the workbook and understand what to do without reading the package documentation.

## A note on reproducibility

A curriculr CV is reproducible in the same sense that an R analysis script is reproducible: given the same workbook and the same version of the package, the same PDF is produced every time. The workbook can be version-controlled alongside the rendered output. Changes to CV content are visible in the workbook’s edit history. The entire history of a career can be preserved in a single Excel file and rendered on demand.

This matters for researchers who care about reproducibility in their own work. It is consistent to apply the same principles to a CV.

## Getting started

    # Install
    pak::pak("erwinlares/curriculr")

    # Scaffold -- copies template workbook and placeholder photo to getwd()
    library(curriculr)
    create_cv()

    # Edit cv-data-template.xlsx, then render
    create_cv(
      data  = "cv-data-template.xlsx",
      photo = "your-photo.png"
    )

The result is `CV.pdf` in the same directory as your workbook.