# Getting started with toolero

#### Erwin Lares

#### Created 2026-04-30 | Last updated 2026-07-16

## Background and motivation

`toolero` grew out of a recurring observation made while teaching and supporting researchers at UW-Madison: the habits that make a project reproducible, shareable, and maintainable are easiest to adopt at the very beginning — and hardest to retrofit once a project is already underway.

The package is heavily influenced by the workflows taught in workshops run by The Carpentries and the UW-Madison Libraries. Those workshops emphasize consistent project organization, version control, and reproducible data practices as foundational skills — not advanced topics. `toolero` tries to operationalize those principles into a small set of functions that reduce the friction of doing the right thing from the start.

The theming and branding support in `toolero` is specifically tailored to UW-Madison’s. These Quarto-based reporting templates are baked into the package as defaults. If you are not at UW-Madison, the branding files are optional – and you can replace with your own. the rest of the package works independently of them.

## Who is this for?

`toolero` is designed for researchers and analysts who:

- Work primarily in R and use RStudio as their IDE
- Write reports or analyses in Quarto
- Want consistent, reproducible project structure without having to think about it every time
- May need to publish content to the UW-Madison Knowledge Base

The package is intentionally small. It does not try to be comprehensive. It tries to make the right defaults easy to reach for from the first line of code.

## Installation

You can install `toolero` from CRAN:

    install.packages("toolero")

Or install the development version from GitHub:

    pak::pak("erwinlares/toolero")

## Project setup: `init_project()` and `create_qmd()`

These two functions are designed to be used together, in order. `init_project()` creates the scaffold; `create_qmd()` populates it with a working Quarto document.

### Starting with `init_project()`

Starting a new R project usually means the same manual steps every time: create a folder, set up an RStudio project, create subdirectories for data and scripts, initialize `renv`, initialize `git`. None of these steps is hard on its own, but skipping any of them — especially early on — tends to create friction later.

`init_project()` handles all of this in a single call:

    library(toolero)

    init_project(path = "~/Documents/my-project")

This creates a new RStudio project at the specified path with the following folder structure already in place:

    my-project/
    ├── data/         # input data
    ├── data-raw/     # original, unprocessed data
    ├── R/            # reusable functions
    ├── scripts/      # analysis scripts
    ├── plots/        # generated visualizations
    ├── images/       # static images and assets
    ├── results/      # processed outputs and tables
    └── docs/         # notes, manuscripts, Quarto documents

> Why this structure? The folder layout is opinionated but not arbitrary. Separating `data/` from `data-raw/` makes it clear which files are original and which have been processed. Keeping `R/` distinct from `scripts/` encourages moving reusable logic into functions over time, which is a natural step toward more maintainable code.

By default, `init_project()` also initializes `renv` and `git`. This means the project is reproducible and version-controlled from the first commit.

> Why `renv` and `git` by default? `renv` ensures that the packages your project depends on are recorded and reproducible. `git` provides a full history of changes. Both are much easier to set up at the start than to retrofit later.

If your project needs folders beyond the defaults:

    init_project(
      path          = "~/Documents/my-project",
      extra_folders = c("notebooks", "presentations")
    )

To apply UW-Madison branding assets to the project:

    init_project(
      path        = "~/Documents/my-project",
      uw_branding = TRUE
    )

This creates an `assets/` folder and populates it with `styles.css`, `header.html`, and `rci-banner.png` — the same assets used in the Quarto template scaffolded by `create_qmd()`.

### Adding a Quarto document with `create_qmd()`

Once the project exists, `create_qmd()` adds a working Quarto document to it. The function has two modes controlled by `include_examples`, and several optional features that can be mixed and matched.

#### With examples (the default)

When `include_examples = TRUE` (the default), `create_qmd()` scaffolds a complete, runnable analysis project:

    create_qmd(path = "~/Documents/my-project", filename = "analysis.qmd")

This creates:

- `analysis.qmd` – a Quarto document with a fully populated YAML header, three-context input resolution via `detect_execution_context()`, a grouped summary, a scatterplot, and a results-saving section. The document is ready to render immediately.
- `data-raw/sample.csv` – a subset of the Palmer Penguins dataset to develop against. The template references this file in the `params` block of the YAML header.
- `assets/logo.png` – a placeholder logo that reads “your logo goes here.” Replace it with your own branding when you’re ready.
- `_quarto.yml` – a project file with a post-render hook that runs `purl.R`
- `R/purl.R` – extracts R code from the rendered document into a companion `.R` file automatically on every render

The idea is that you can render the document as-is, see results, and then progressively replace the sample analysis with your own. The sample data, the analysis blocks, and the results-saving pattern are all working examples you can study before modifying.

#### Without examples

When `include_examples = FALSE`, `create_qmd()` creates a minimal skeleton with no sample data and no pre-filled analysis:

    create_qmd(
      path             = "~/Documents/my-project",
      filename         = "analysis.qmd",
      include_examples = FALSE
    )

This creates:

- `analysis.qmd` – a Quarto document with the YAML header (title, author, format settings) and a setup chunk that loads `library(toolero)`. The body has a single `## Introduction` heading and an HTML comment prompting you to add your content. No `params` block, no analysis code, no references to sample data.
- `_quarto.yml` and `R/purl.R` – the purl hook is included by default regardless of `include_examples`

No `data-raw/` folder is created. No `sample.csv` is copied. No placeholder logo is placed in `assets/`. The document is a blank canvas with just enough structure to render.

Use this mode when you already know what your analysis looks like and don’t need the worked example as a starting point.

#### Custom styling

The `use_style` argument controls whether CSS and header assets are wired into the YAML. It works independently of `include_examples`:

    # Blank document with UW branding (assumes init_project(uw_branding = TRUE) was called)
    create_qmd(
      path             = "~/Documents/my-project",
      filename         = "report.qmd",
      include_examples = FALSE,
      use_style        = TRUE
    )

    # Blank document with custom branding from a different directory
    create_qmd(
      path             = "~/Documents/my-project",
      filename         = "report.qmd",
      include_examples = FALSE,
      use_style        = "my-branding/"
    )

When `use_style = TRUE`, the function scans `assets/` for `.css` and `.html` files and adds them to the YAML (`css:` and `include-before-body:` respectively). When `use_style` is a directory path, it scans that directory instead. If the directory contains multiple `.css` or `.html` files, the function errors and asks you to specify which one via `yaml_data`.

Styling is managed by `init_project(uw_branding = TRUE)`, which copies the UW-Madison branding files into `assets/`. The `create_qmd()` function does not copy style assets itself – it only wires up what’s already there.

#### The purl hook

By default, `create_qmd()` scaffolds a post-render hook that extracts R code from the rendered document into a companion `.R` file:

- `_quarto.yml` – contains the `post-render: ["Rscript R/purl.R"]` directive
- `R/purl.R` – scans the project root for `.qmd` files and runs `knitr::purl()` on each one

The hook runs automatically on every `quarto render`, so the `.R` file always reflects the current state of the `.qmd`. This is useful for sharing the analysis as a script, running it on a remote cluster via `submitr`, or archiving the code independently of the document.

Set `use_purl = FALSE` to skip the hook if you don’t need the `.R` companion:

    create_qmd(
      path     = "~/Documents/my-project",
      filename = "notes.qmd",
      use_purl = FALSE,
      include_examples = FALSE
    )

#### Pre-populating the YAML header

The `yaml_data` argument accepts a path to a YAML file whose keys overwrite the corresponding placeholders in the template. Keys not present in the file are left as-is:

    create_qmd(
      path      = "~/Documents/my-project",
      filename  = "analysis.qmd",
      yaml_data = "~/my-metadata.yml"
    )

Where `my-metadata.yml` might look like:

    title: "My Analysis"
    author:
      - name: "Your Name"
        affiliation: "UW-Madison"
        email: "you@wisc.edu"

This works with both `include_examples = TRUE` and `FALSE`. When combined with `use_style`, the YAML substitution runs after the style injection, so `yaml_data` can override any auto-generated keys if needed.

#### Summary of what gets created

File | `include_examples = TRUE` | `include_examples = FALSE`
--- | --- | ---
`analysis.qmd` | Full example with analysis blocks and `params` | Skeleton with YAML and empty body
`data-raw/sample.csv` | Yes | No
`assets/logo.png` | Yes | No
`_quarto.yml` | Yes (when `use_purl = TRUE`) | Yes (when `use_purl = TRUE`)
`R/purl.R` | Yes (when `use_purl = TRUE`) | Yes (when `use_purl = TRUE`)
CSS/header in YAML | Only when `use_style` is set | Only when `use_style` is set

## Working with data: `read_clean_csv()` and `write_by_group()`

These two functions address common friction points in day-to-day data work. They are general-purpose utilities — useful in any R project, not just ones set up with `toolero`.

### Reading data with `read_clean_csv()`

`read_clean_csv()` combines `readr::read_csv()`, `janitor::clean_names()`, and optionally `tidyr::drop_na()` into a single call. The goal is to get from a raw CSV to a clean, analysis-ready tibble in one step.

The simplest call reads the file and standardizes column names:

    data <- read_clean_csv("data/my-file.csv")

Column names are automatically converted to lowercase with underscores – consistent, predictable, and tidyverse-friendly. A column called `First Name` becomes `first_name`. `Q1 Revenue ($)` becomes `q1_revenue`.

#### Handling missing values

By default, `read_clean_csv()` treats empty strings and `"NA"` as missing – the same behavior as `readr::read_csv()`. If your data uses other conventions for missing values, pass them via the `na` argument:

    # Treat dots, dashes, and -999 as missing in addition to blanks and "NA"
    data <- read_clean_csv("data/my-file.csv", na = c("", "NA", "N/A", ".", "-999"))

#### Dropping rows with missing values

The `drop_na` argument controls whether incomplete rows are removed after reading. It accepts three forms:

    # Keep all rows, including those with missing values (default)
    data <- read_clean_csv("data/my-file.csv", drop_na = FALSE)

    # Drop any row that has a missing value in any column
    data <- read_clean_csv("data/my-file.csv", drop_na = TRUE)

    # Drop rows only where specific columns are missing
    data <- read_clean_csv("data/my-file.csv", drop_na = c("bill_length_mm", "sex"))

When rows are dropped, a message reports how many were removed and how many remain. This makes the data cleaning step visible in your console output rather than happening silently.

#### Ingest summary

Set `summary = TRUE` to print a brief report after reading: row and column counts, how many column names were cleaned, and the total number of missing values. The summary reflects the final state of the tibble after any `drop_na` action:

    data <- read_clean_csv("data/my-file.csv", summary = TRUE)

#### Seeing column type messages

By default, the column type messages from `readr` are suppressed to keep the console clean. Set `verbose = TRUE` to see them – useful when debugging unexpected column types:

    data <- read_clean_csv("data/my-file.csv", verbose = TRUE)

#### Combining arguments

The arguments compose naturally. A common pattern for a first look at a new dataset combines custom missing value codes, row dropping, and the ingest summary:

    data <- read_clean_csv(
      "data/my-file.csv",
      na      = c("", "NA", "N/A", "."),
      drop_na = TRUE,
      summary = TRUE
    )

#### Passing arguments through to `readr`

Any additional arguments are forwarded to `readr::read_csv()` via `...`. This means you can use `col_types` to force column types, `skip` to skip header rows, or `locale` to handle non-standard decimal separators:

    # Force a column to character instead of letting readr guess
    data <- read_clean_csv("data/my-file.csv", col_types = cols(zip_code = col_character()))

    # Skip the first two rows (e.g. metadata rows before the header)
    data <- read_clean_csv("data/my-file.csv", skip = 2)

    # Handle European-style decimals (comma as decimal separator)
    data <- read_clean_csv("data/my-file.csv", locale = locale(decimal_mark = ","))

### Splitting data by group with `write_by_group()`

When a data frame contains multiple groups that need to be written to separate files, `write_by_group()` handles the split and the write in a single call:

    write_by_group(
      data       = penguins,
      group_col  = "species",
      output_dir = "results/by-species"
    )

Output filenames are derived from the group values and sanitized for use as file names — converted to lowercase with spaces and special characters replaced by dashes. A group called `Chinstrap` becomes `chinstrap.csv`. `Palmer Penguins` would become `palmer-penguins.csv`.

To also write a manifest listing the output files, group values, and row counts:

    write_by_group(
      data       = penguins,
      group_col  = "species",
      output_dir = "results/by-species",
      manifest   = TRUE
    )

## Execution context: `detect_execution_context()`

R code often needs to behave differently depending on where it is running — interactively in RStudio, during a `quarto render`, or as a batch `Rscript` job on a remote cluster. `detect_execution_context()` identifies which of these three environments is active and returns one of `"interactive"`, `"quarto"`, or `"rscript"`.

The canonical use case is resolving input file paths portably:

    context <- detect_execution_context()

    input_file <- switch(context,
      interactive = "data/sample.csv",
      quarto      = params$input_file,
      rscript     = commandArgs(trailingOnly = TRUE)[1]
    )

This pattern is built into the template scaffolded by `create_qmd()`, so you get it for free without having to write it yourself.

## Knowledge Base export: `generate_kb_xml()`

> This section is relevant only if you publish content to the UW-Madison Knowledge Base. If you do not, you can safely skip it.

The UW-Madison Knowledge Base requires content to be submitted as XML with all visual assets embedded in the HTML body. `generate_kb_xml()` automates this process entirely.

    generate_kb_xml(
      html_path  = "docs/analysis.html",
      output_dir = "exports"
    )

The function:

1. Infers the source `.qmd` from the HTML path (or accepts it explicitly via `qmd_path`)
2. Re-renders the document with `embed-resources: true` so all CSS, images, and JavaScript are self-contained
3. Extracts metadata from the `.qmd` YAML header — `title` → `kb_title`, `description` → `kb_summary`, `categories` → `kb_keywords`
4. Produces a `.xml` file ready for direct KB import

This is why the `description` and `categories` fields in the `create_qmd()` template matter — they flow through automatically into the KB article metadata without any extra work.

> When importing into the KB, check the Decode HTML entity in body content option.

## Quick reference

Function | Brief description
--- | ---
`init_project()` | Creates a new RStudio project with a reproducible folder structure. Can optionally initialize `renv`, initialize `git`, add extra folders, and copy UW-Madison branding assets.
`create_qmd()` | Creates a Quarto document scaffold. Can generate a full worked example or a minimal skeleton, pre-fill YAML metadata, add styling, and set up the purl post-render hook.
`read_clean_csv()` | Reads a CSV file with `readr::read_csv()`, cleans column names with `janitor::clean_names()`, and can optionally drop rows with missing values or print an ingest summary.
`write_by_group()` | Splits a data frame by a grouping column and writes one CSV file per group. Can also write a manifest listing output files, group values, and row counts.
`detect_execution_context()` | Detects whether code is running interactively, during `quarto render`, or as an `Rscript` job. Useful for writing one codebase that works across local, rendered, and batch execution contexts.
`generate_kb_xml()` | Converts a rendered Quarto HTML document into UW-Madison Knowledge Base-ready XML with embedded resources and metadata extracted from the source `.qmd`.

## Citation

If you use `toolero` in your work, please cite it:

    citation("toolero")