write-alt-text

Generate accessible alt text for data visualizations in this project.

ARGUMENTS

• label: (optional) specific fig- label to generate alt text for
• file: (optional) specific .qmd file to process

Instructions

When invoked, analyze the figure(s) and generate alt text following these guidelines:

Key Advantage: Source Code Access

Unlike typical alt text scenarios where you only see an image, we have access to the R code that generates each chart. Use this to extract precise details:

From ggplot2 code:

• aes(x, y) → exact variable names for axes
• aes(color = ...) / aes(fill = ...) → what color encodes
• geom_point() → scatter, geom_histogram() → histogram, geom_line() → line chart
• geom_smooth() / geom_abline() → overlaid fitted lines
• facet_wrap(~var) → number of panels and what varies
• scale_color_gradient() → color encoding scheme
• labs(x = ..., y = ...) → axis labels if customized

From data generation code:

• rbeta(), rnorm(), runif() → expected distribution shape
• mutate() transformations → what was done to data
• Recipe steps → feature engineering applied
• Filtering/subsetting → what subset is shown

From surrounding prose:

• Text before/after the chunk explains the purpose and key insight
• Chapter context tells you what the figure is meant to teach
• This is often the best source for the "key insight" part of alt text

Three-Part Structure (Amy Cesal's Formula)

1. Chart type - First words identify the format
2. Data description - Axes, variables, what's shown
3. Key insight - The pattern or takeaway (often found in surrounding text)

Relationship to fig-cap

Read the fig-cap first. The alt text should complement, not duplicate it:

• If caption states the insight, alt text can focus on describing the visual structure
• If caption is generic, alt text should include the key insight
• Together they should give a complete understanding

Content Rules

Include:

• Chart type as first words
• Axis labels and what they represent
• Specific values/ranges when code reveals them (e.g., "peaks between 25-50")
• Number of panels/facets
• What color/size encodes if used
• The key pattern that supports the chapter's point

Exclude:

• "Image of..." or "Chart showing..." (screen readers announce this)
• Decorative color descriptions (unless color encodes data)
• Information already in fig-cap
• Implementation details (package names, function internals)

Length Guidelines

Quality Checklist

• Starts with chart type (Scatter chart, Histogram, Faceted bar chart, etc.)
• Names the axis variables
• Includes specific values/ranges from code when informative
• States the key insight from surrounding prose
• Complements (not duplicates) the fig-cap
• Would make sense to someone who cannot see the image
• Uses plain language (avoid jargon like "geom" or "aesthetic")

Template Patterns

Scatter chart:

Scatter chart. [X var] along the x-axis, [Y var] along the y-axis.
[Shape: linear/curved/clustered]. [Specific pattern, e.g., "peaks when X is 25-50"].
[Any overlaid fits or annotations].

Histogram:

Histogram of [variable]. [Shape: right-skewed/bimodal/normal/uniform].
[If transformed: "after [transformation], the distribution [result]"].
[Notable features: outliers, gaps, multiple modes].

Bar chart:

Bar chart. [Categories] along the x-axis, [measure] along the y-axis.
[Key comparison: which is highest/lowest, relative differences].
[Pattern: increasing/decreasing/grouped].

Tile/raster chart:

Tile chart [or heatmap]. [Row variable] along the y-axis, [column variable] along the x-axis.
Color encodes [what value]. [Pattern: where values are high/low].
[If faceted: "N panels showing [what varies]"].

Faceted chart:

Faceted [chart type] with [N] panels, one per [faceting variable].
[What's constant across panels]. [What changes/varies].
[Key comparison or insight across panels].

Correlation heatmap:

Correlation [matrix/heatmap] of [what variables]. [Arrangement].
[Overall pattern: mostly positive/negative/mixed].
[Notable clusters or strong/weak pairs].
[If relevant: contrast with expected behavior, e.g., "unlike PCA, these are not orthogonal"].

Before/after comparison:

[N] [chart type]s arranged [vertically/in grid]. [Top/Left] shows [original].
[Bottom/Right] shows [transformed]. [Key difference/similarity].
[If overlay: "[color] curve shows [reference]"].

Line chart with overlays:

[Line/Scatter] chart with overlaid [fits/curves]. [Axes].
[Number] of [lines/fits] shown: [list what each represents].
[Which fits well vs. poorly and why].

Workflow

Finding Figures

To find all figure chunks in the project:

# List all figure labels with file and line number
grep -n "#| label: fig-" *.qmd

# Find figures in a specific file
grep -n "#| label: fig-" numeric-splines.qmd

# Find a specific figure
grep -rn "#| label: fig-splines-predictor-outcome" *.qmd

For Each Figure

1. Locate - Use grep to find file and line number
2. Read context - Read ~50 lines around the chunk (prose before + code + prose after)
3. Extract details - Note fig-cap, ggplot code, data generation, surrounding explanation
4. Draft alt text - Apply three-part structure (type → data → insight)
5. Verify - Check against quality checklist

Example

Code context:

plotting_data |>
  ggplot(aes(value)) +
  geom_histogram(binwidth = 0.2) +
  facet_grid(name~., scales = "free_y") +
  geom_line(aes(x, y), data = norm_curve, color = "green4")

Surrounding prose says: "Normalization doesn't make data more normal"

fig-cap: "Normalization doesn't make data more normal. The green curve indicates the density of the unit normal distribution."

Good alt text:

#| fig-alt: |
#|   Faceted histogram with two panels stacked vertically. Top panel shows
#|   original data with a bimodal distribution. Bottom panel shows the same
#|   data after z-score normalization, retaining the bimodal shape. A green
#|   normal distribution curve overlaid on the bottom panel clearly does not
#|   match the data, demonstrating that normalization preserves distribution
#|   shape rather than creating normality.