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):

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

For R Markdown (report.Rmd):

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

Got: The YAML header contains a params: block with named parameters, each having a default value of the correct type.

If fail: If rendering fails with "object 'params' not found", ensure the params: block is correctly indented under the YAML frontmatter. For Quarto, params must be at the top level of the YAML, not nested under format:.

Step 2: Use Parameters in Code

```{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)
```

Got: Code chunks reference parameters via params$name and conditional chunks use #| eval: !expr params$flag for Quarto. Inline R expressions like `r params$region` render dynamic text.

If fail: If params$name returns NULL, verify the parameter name matches exactly between the YAML definition and the code reference (case-sensitive). Check that default values are the correct type.

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"
)

Got: A single report renders successfully with custom parameter values overriding the YAML defaults. The output file is created at the specified path.

If fail: If Quarto render fails, check that quarto CLI is installed and on PATH. If R Markdown render fails, verify rmarkdown is installed. Ensure parameter names in execute_params (Quarto) or params (R Markdown) match the YAML definitions exactly.

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
  )
})

Got: One HTML file per region-year combination.

If fail: 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
)

Got: The validation code chunk runs at the start of each render and stops with an informative error if any parameter is out of range or the wrong type.

If fail: If stopifnot() produces unhelpful error messages, switch to explicit if (!cond) stop("message") calls for clearer diagnostics.

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"))
)

Got: Output files are written to a date-stamped subdirectory with descriptive names (e.g., reports/2025-06/report-europe.html).

If fail: If dir.create() fails, check that the parent directory exists and is writable. On Windows, verify the path length does not exceed 260 characters.

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

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