Free Online Ridgeline Plot Maker

Stack overlapping density curves to compare distributions across groups. Visually striking and information-rich.

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What Is a Ridgeline Plot?

A ridgeline plot (also called a joy plot, after the Joy Division album cover) stacks partially overlapping density curves vertically, one per group or time period. Each curve shows the distribution shape for its group, and the vertical stacking makes it easy to scan how distributions shift, widen, or skew across categories.

Key components include the smoothed density curves, vertical group labels, a shared value axis, an overlap parameter controlling how much curves overlap, and optional color coding. Ridgeline plots are one of the most visually striking statistical charts — they reveal patterns in distribution change that would require many separate histograms to show otherwise.

What Data You Need for a Ridgeline Plot

One column (series) per distribution, sharing a common x-axis of category labels; each series' values are plotted directly as a smooth spline and offset-stacked above the one below it. You supply the already-shaped curve values per category — the tool does NOT bin raw observations or estimate density. Layout: an X-label column (e.g. Hour) plus one value column per group (e.g. Monday, Wednesday, Friday), like Hour,Mon,Wed,Fri / 8:00,15,16,14 / 9:00,12,13,11.

When to Use a Ridgeline Plot

Use a ridgeline plot when you want to compare distribution shapes across many groups in a compact, visually engaging layout. Common applications include monthly temperature distributions, income distributions by country, response-time distributions across software releases, gene expression levels by tissue type, and customer rating distributions by product category.

Ridgeline plots work best with 5-20 groups and continuous data. They excel when the story is about how the distribution shifts over time or across conditions. Choose a ridgeline over multiple histograms when you want all groups in one view. Choose a violin plot for side-by-side comparison of fewer groups with more statistical detail.

Where a Ridgeline Plot Works Best

Showing how a distribution shifts over time: monthly temperature highs, weekly page-load latency, or daily delivery times across a year
Comparing the same measurement across ordered categories: test-score distributions by grade level, or income distributions across age brackets
Visualizing survey or rating spread across many items: satisfaction scores for 15 product features, one curve per feature
Tracking a sensor or metric across ordered segments: traffic speed distributions by hour of day, or sound-frequency spectra across recording sessions

Ridgeline Plot Example: Hourly Visitor Spread by Day

A store manager compares foot-traffic shape across days of the week. Each day is one column of 24 hourly counts sharing the same 0:00-23:00 x-axis; the tool draws each as a spline ridge and offset-stacks them so the daily rhythm reads as a wave.

Sample data: x = 0:00..23:00; Monday peaks ~15 at 8:00 and ~14 at 18:00; Saturday peaks ~16 midday at 12:00.

Weekday curves show twin commute humps at 8:00 and 18:00, while Saturday's single broad midday hump sits higher and later — the offset stacking makes the shift in peak timing read at a glance.

How to Create a Ridgeline Plot Online

Step 1: Open ChartLoad and select the Ridgeline Plot type from the chart picker on the main page
Step 2: Enter group names in the first column and their raw data values in subsequent columns or rows
Step 3: Adjust the overlap amount to let curves overlap more (compact) or less (more separated)
Step 4: Click Export and download your ridgeline plot as PNG, PDF, SVG, JPEG, or WebP

Ridgeline Plot Best Practices & Tips

Order groups meaningfully — chronologically, alphabetically, or by median — to reveal progression patterns.
Set overlap to 30-50% for a balanced view: enough to see each curve individually, close enough for comparison.
Use distinct colors per group or a gradient from top to bottom to help readers track individual curves.
Ridgeline plots need 30+ observations per group for the density estimate to be smooth and reliable.
They are named after the Joy Division album cover "Unknown Pleasures" — a fun fact for data-literate audiences.
Use a gradient color scheme from cool (bottom) to warm (top) to add a visual ordering cue to the stacked curves.
Ridgeline plots are particularly effective in showing how a distribution shifts over time — like monthly temperature profiles.
Add a vertical reference line at the overall mean or median value to give all stacked density curves a common benchmark reference point for easier and more meaningful cross-group comparison.

Common Ridgeline Plot Mistakes to Avoid

Too few observations per group: density curves need roughly 20+ points each. With 5 values a month, the smoothed curve invents bumps that aren't real and the plot looks authoritative while being noise.
Over-stacking groups so curves overlap too deeply. With 40 months crammed together, tall peaks bury the labels and baselines of curves above them, hiding which group a bump belongs to. Reduce overlap or split into panels.
Ordering groups arbitrarily. Ridgelines tell a story only when the y-axis follows a meaningful sequence (time, age, latitude); sorting months alphabetically or groups randomly destroys the wave pattern that makes the chart worth using.

Frequently Asked Questions

How many groups can I compare?

Ridgeline plots work well with 5–20 groups. Fewer groups leave the chart sparse; more can become hard to read.

Can I control the overlap?

Yes — adjust the overlap parameter to let curves overlap more (denser) or less (more separated).

What is the difference between a ridgeline and a violin chart?

Violin charts show mirrored density curves for individual groups. Ridgeline plots stack many one-sided curves vertically for cross-group comparison.

Related Chart Types

Ready-Made Ridgeline Plot Templates

Skip the blank canvas — open a free, editable Ridgeline Plot template, swap in your own data, and export. Browse all templates.

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