ggplot Two Y Axes Workaround

The ggplot2 package in R deliberately discourages dual-axis charts because they can confuse readers when the two scales are unrelated. Nonetheless, there are legitimate cases—such as overlaying temperature and precipitation—where plotting two series on different scales in one figure is useful. Because ggplot2 only allows a secondary axis that is a one-to-one transformation of the primary axis, engineers must adopt specific work-arounds to create the illusion of two independent y-axes.

Why ggplot2 Does Not Natively Support Independent Dual Axes

Hadley Wickham’s design philosophy prioritizes clear, interpretable graphics. When two y-axes are free to use unrelated scales, readers can easily misread the magnitude of change on either series. Consequently, ggplot2 only permits transformed secondary axes via the sec.axis argument—meaning the second axis must be a deterministic function of the first.

Implications for Data Engineers and Analysts

• You cannot simply plot two unrelated metrics with + scale_y_continuous(sec.axis = ...) unless one can be expressed as f(x) = a * x + b of the other.
• To show two unrelated metrics, you must pre-scale or normalize one series so that its transformed values align with the other series’ scale, then supply an inverse transformation for the axis labels.

A Practical Work-Around Strategy

1. Choose one series as the primary axis.
2. Normalize or linearly transform the secondary series so its range roughly matches the primary series’ range. A common approach is min–max scaling:
   sec_scaled = (sec - min(sec)) / (max(sec) - min(sec)) * (max(prim) - min(prim)) + min(prim)
3. Plot the transformed secondary series on the same y-axis as the primary.
4. Add scale_y_continuous(sec.axis = sec_axis(~inv_transform(.), name = "Secondary Metric")), where inv_transform() reverses the scaling.

Step-by-Step Example: Overlaying Revenue and Conversion Rate

library(ggplot2)

# Synthetic data
df <- data.frame(
month = as.Date('2023-01-01') + 0:11 * 30,
revenue = c(120, 150, 110, 180, 210, 190, 220, 250, 240, 260, 300, 310),
conversion = c(3.1, 3.4, 2.9, 3.6, 3.8, 3.5, 3.9, 4.1, 3.8, 4.0, 4.3, 4.5)
)

# 1. Identify limits
y1_min <- min(df$revenue)
y1_max <- max(df$revenue)

# 2. Scale conversion to revenue space
scale_factor <- (y1_max - y1_min) / (max(df$conversion) - min(df$conversion))
conv_scaled <- df$conversion * scale_factor + y1_min - min(df$conversion) * scale_factor

df$conv_scaled <- conv_scaled

# 3. Plot
p <- ggplot(df, aes(month)) +
geom_col(aes(y = revenue), fill = "steelblue", alpha = 0.7) +
geom_line(aes(y = conv_scaled), color = "darkorange", size = 1.2) +
scale_y_continuous(
name = "Monthly Revenue ($)",
sec.axis = sec_axis(~ (. - (y1_min - min(df$conversion) * scale_factor)) / scale_factor,
name = "Conversion Rate (%)")
) +
scale_x_date(date_labels = "%b %Y") +
theme_minimal()

print(p)

In the code above:

• conv_scaled brings conversion rate values into the revenue scale.
• The anonymous function in sec_axis() reverses that transformation so the secondary axis shows percentages.

Best Practices

Label Axes Clearly

Because dual-axis plots can mislead, always:

• Use distinct colors and line types.
• Add axis labels that include units.
• Include a legend keyed to the colors.

Prefer Separate Panels When Possible

If the audiences are not highly technical or the relationships between series are tenuous, favor facet_wrap() or vertically stacked plots. These preserve clarity and avoid scale conflicts.

Avoid Non-linear Transformations

Stick to linear transformations (multiplication, addition) so tick spacing remains intuitive.

Common Pitfalls and How to Avoid Them

Misaligned Baselines

If the lower bounds of the primary and scaled secondary series differ, the visual zero point of one series may not correspond to zero on its own axis. Ensure transformations align baselines appropriately.

Forgetting the Inverse Function

Without the correct inversion in sec_axis(), the secondary ticks will be wrong, misleading the user. Always test by back-transforming a few reference values.

Using Categorical X-Axes with Columns and Lines

When the x-axis is character or factor, column widths can dominate the line visually. Convert to factor positions or use position_dodge() so neither series obscures the other.

Alternatives to Dual-Axis Visualizations

• Small Multiples: Use facets to show each metric on its own scale but share the x-axis for easy comparison.
• Indexed Lines: Convert both series to index (e.g., set January = 100) and plot on a single axis to compare percentage change.
• Interactive Dashboards: Tools like Plotly allow users to toggle series visibility, reducing clutter.

Relevance to Modern Data Engineering Workflows

Dual-axis plots often surface in KPI dashboards where engineers must communicate related but scale-mismatched metrics. Mastering the ggplot2 workaround enables teams to produce clear executive-level visuals without resorting to spreadsheet chart editors.

Galaxy Context

While Galaxy is primarily a SQL editor, its forthcoming visualization layer will likely consume aggregated query results. Engineers may retrieve revenue and conversion data via SQL in Galaxy, export to R, and apply the dual-axis workaround in ggplot2 for reporting.

Conclusion

Creating dual-axis charts in ggplot2 is intentionally non-trivial to protect against misleading graphics. By understanding the philosophy behind sec.axis and applying systematic transformations, data engineers can responsibly implement two-axis plots when they truly add value. Always weigh the communication benefit against the cognitive cost to readers, and where doubt exists, reach for alternative designs.