Choosing the Right Chart for Every Data Type

Choosing the Right Chart for Every Data Type

Learn a repeatable framework for mapping categorical, temporal, numerical, and spatial data to the most effective visualization so that your audience grasps insights at a glance.

Definition

Chart selection is the disciplined approach of picking the visual form—bar, line, scatter, map, and so on—whose visual encodings (position, length, color, size) best match the underlying data type (categorical, ordinal, quantitative, temporal, spatial) and analytic goal (comparison, relationship, distribution, composition, or trend).

Why It Matters

Human short-term memory can only store a handful of numbers, but it can process pre-attentive visual cues—length, slope, position—in parallel. Choosing the correct chart:

• Reduces cognitive load and speeds up decision-making.
• Minimizes misinterpretation by aligning perception with meaning.
• Elevates stakeholder confidence in your analysis.
• Prevents expensive business errors caused by wrong conclusions.

Data Types, Analytic Goals, and Encoding

Categorical & Ordinal Data

Use cases: product lines, customer segments, survey scales.
Recommended charts: bar, stacked bar, dot plot, heatmap.

Quantitative Data

Use cases: revenue, weight, latency.
Recommended charts: histogram, box plot, scatter plot, line (when ordered).

Temporal Data

Use cases: monthly churn, daily active users.
Recommended charts: line, area, candlestick, horizon.

Spatial Data

Use cases: store locations, geotagged events.
Recommended charts: choropleth map, point map, hexbin map.

A Four-Step Selection Framework

1. Clarify the Question. What decision or story should the viewer reach? E.g., “Which marketing channel is growing fastest this quarter?”
2. Identify Data Types. Label each field as categorical, temporal, quantitative, or spatial.
3. Choose the Analytic Goal.
   • Comparison (ranking): bar, dot, lollipop.
   • Trend over time: line, area.
   • Distribution: histogram, violin, box plot.
   • Relationship: scatter, bubble, network.
   • Composition: stacked area, 100% bar, treemap.
4. Check Perceptual Accuracy. Favor position and length encodings (bars, lines) over area and color (pie, word cloud) when precision matters.

Best Practices

• Minimize Axis Junk. Start the y-axis at zero for bar charts to preserve proportional reading.
• Limit Categories. A bar chart with 40 categories is a table in disguise; aggregate or filter.
• Use Color Sparingly. Encode only one extra dimension with color to avoid confusion.
• Label Directly. Replace legends with inline labels near marks when possible.
• Mind Accessibility. Pick color palettes that are color-blind safe (e.g., ColorBrewer’s Set2).

Common Mistakes and How to Avoid Them

1. Using Pie Charts for Precise Comparison

Pies rely on comparing angles/area, which humans estimate poorly. Replace with a bar or stacked bar when exact values matter.

2. Plotting Too Many Lines on One Axis

Overplotting hides patterns. Instead, facet into small multiples or highlight the key series and fade the rest.

3. Ignoring Data Distribution

Choosing a bar chart for skewed numerical data can hide outliers. Use histograms or box plots to reveal distribution first.

Example Workflow: From SQL to Chart

Suppose you have a sales table in Postgres and need to show monthly revenue trends.

-- Step 1: Aggregate monthly data
SELECT date_trunc('month', order_date) AS month,
SUM(total_amount) AS revenue
FROM sales
GROUP BY 1
ORDER BY 1;


Data type: month = temporal, revenue = quantitative.
Goal: Trend over time.
Best chart: Line chart.

If you run this query in a modern SQL editor such as Galaxy, the result grid appears instantly. From there you can pipe the result set to your favorite plotting library or BI tool. Galaxy’s upcoming visualization layer will even suggest a line chart automatically based on detected types.

Galaxy Integration

Although Galaxy is primarily a lightning-fast SQL editor, chart selection still enters the picture. Galaxy’s AI Copilot inspects the schema and result set to infer data types (e.g., timestamp, numeric, varchar). When you ask, “Suggest a visualization,” Copilot maps those types to recommended charts, optionally generating the Python, Vega-Lite, or Tableau spec you can paste into downstream tools.

Putting It All Together

Choosing the right chart is less art, more recipe. Clarify the question, tag data types, match the chart to the analytic goal, and respect perceptual science. Follow those steps—plus the best practices above—and your dashboards will tell compelling, error-free stories.