Build Parameterized Report

Create reports that accept parameters to generate multiple customized variations from a single template.

When to Use

•Generating the same report for different departments, regions, or time periods
•Creating client-specific reports from a template
•Building dashboards that filter to specific subsets
•Automating recurring reports with different inputs

Inputs

•Required: Report template (Quarto or R Markdown)
•Required: Parameter definitions (names, types, defaults)
•Optional: List of parameter values for batch generation
•Optional: Output directory for generated reports

Procedure

Step 1: Define Parameters in YAML

For Quarto (report.qmd):

yaml

---
title: "Sales Report: `r params$region`"
params:
  region: "North America"
  year: 2025
  include_forecast: true
format:
  html:
    toc: true
---

For R Markdown (report.Rmd):

yaml

---
title: "Sales Report"
params:
  region: "North America"
  year: 2025
  include_forecast: true
output: html_document
---

Step 2: Use Parameters in Code

markdown

```{r}
#| label: filter-data

data <- full_dataset |>
  filter(region == params$region, year == params$year)

nrow(data)
```

## Overview for `r params$region`

This report covers the `r params$region` region for `r params$year`.

```{r}
#| label: forecast
#| eval: !expr params$include_forecast

# This chunk only runs when include_forecast is TRUE
forecast_model <- forecast::auto.arima(data$sales)
forecast::autoplot(forecast_model)
```

Step 3: Render with Custom Parameters

Single render:

# Quarto
quarto::quarto_render(
  "report.qmd",
  execute_params = list(region = "Europe", year = 2025)
)

# R Markdown
rmarkdown::render(
  "report.Rmd",
  params = list(region = "Europe", year = 2025),
  output_file = "report-europe-2025.html"
)

Step 4: Batch Render Multiple Reports

regions <- c("North America", "Europe", "Asia Pacific", "Latin America")
years <- c(2024, 2025)

# Generate all combinations
combinations <- expand.grid(region = regions, year = years, stringsAsFactors = FALSE)

# Render each
purrr::pwalk(combinations, function(region, year) {
  output_name <- sprintf("report-%s-%d.html",
    tolower(gsub(" ", "-", region)), year)

  quarto::quarto_render(
    "report.qmd",
    execute_params = list(region = region, year = year),
    output_file = output_name
  )
})

Expected: One HTML file per region-year combination.

On failure: Check that parameter names match exactly between YAML and code. Ensure all parameter values are valid.

Step 5: Add Parameter Validation

#| label: validate-params

stopifnot(
  "Region must be a valid region" = params$region %in% valid_regions,
  "Year must be numeric" = is.numeric(params$year),
  "Year must be reasonable" = params$year >= 2020 && params$year <= 2030
)

Step 6: Organize Output

# Create output directory
output_dir <- file.path("reports", format(Sys.Date(), "%Y-%m"))
dir.create(output_dir, recursive = TRUE, showWarnings = FALSE)

# Render with output path
quarto::quarto_render(
  "report.qmd",
  execute_params = list(region = region),
  output_file = file.path(output_dir, paste0("report-", region, ".html"))
)

Validation

• Report renders with default parameters
• Report renders with each set of custom parameters
• Parameters are validated before processing
• Output files are named descriptively
• Conditional sections render correctly based on parameters
• Batch generation completes for all combinations

Common Pitfalls

•Parameter name mismatch: YAML names must exactly match params$name references in code
•Type coercion: YAML may parse year: 2025 as integer but code expects character. Be explicit.
•Conditional evaluation: Use #| eval: !expr params$flag not eval = params$flag in Quarto
•File overwriting: Without unique output names, each render overwrites the previous
•Memory in batch mode: Long batch runs may accumulate memory. Consider using callr::r() for isolation.

Related Skills

•create-quarto-report - base Quarto document setup
•generate-statistical-tables - tables that adapt to parameters
•format-apa-report - parameterized academic reports