Write Chart 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 code that generates each chart. Use this to extract precise details:

From plotting code:

• Variable mappings → exact variable names for axes

• Color/fill mappings → what color encodes

• Plot type functions → scatter, histogram, line chart, etc.

• Trend lines or fitted curves → overlaid statistical fits

• Faceting/subplots → number of panels and what varies

• Color scales → encoding scheme (sequential, diverging, categorical)

• Axis labels and titles → customized labels

From data generation code:

• Random distributions → expected distribution shape

• Transformations → what was done to data

• Feature engineering → preprocessing 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)

• Chart type - First words identify the format

• Data description - Axes, variables, what's shown

• 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

Complexity Sentences When to use

Simple 2-3 Single geom, no facets, obvious pattern

Standard 3-4 Multiple geoms or color encoding

Complex 4-5 Faceted, multiple overlays, nuanced insight

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

• Locate - Use grep to find file and line number

• Read context - Read ~50 lines around the chunk (prose before + code + prose after)

• Extract details - Note fig-cap, ggplot code, data generation, surrounding explanation

• Draft alt text - Apply three-part structure (type → data → insight)

• 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.