Figures Explained: Types, Uses, and Best Practices

Figures: Visualizing Data for Clearer InsightsVisual representations of data — charts, graphs, diagrams, and illustrative images — are essential tools for turning raw numbers into understandable, actionable information. A well-designed figure can reveal patterns, highlight anomalies, and communicate complex relationships far more effectively than tables of numbers or long blocks of text. This article explores why figures matter, the main types and when to use them, design principles for clarity and accessibility, common pitfalls to avoid, and practical workflows for producing high-quality figures for reports, presentations, and publications.

Why figures matter

Figures condense complex data into intuitive visual forms that accelerate comprehension.
They make patterns, trends, and outliers immediately visible.
Figures support storytelling and persuasion by emphasizing the most important points.
Good figures increase accessibility: for many people, visual representations are easier to understand than text or raw numbers.

Main types of figures and when to use them

Bar charts and column charts

Use for comparing discrete categories or showing changes over time when categories are few. Vertical columns are typically used for time series; horizontal bars are useful for long category labels.

Line charts

Best for showing trends over continuous variables (especially time). Use when you want to emphasize the trajectory and relationships between series.

Scatter plots

Ideal for showing relationships or correlations between two continuous variables and for spotting clusters or outliers.

Area charts

Useful to show cumulative totals or parts of a whole over time but can obscure individual series when stacked improperly.

Pie charts and donut charts

Show proportions of a whole at a single point in time. Use sparingly — they are hard to read when there are many slices or when differences are small.

Histograms and density plots

Show distributions of a single continuous variable. Histograms bin data; density plots smooth the distribution.

Box plots and violin plots

Summarize distribution through quartiles, medians, and variability (box plots) or add full distribution shape (violin plots). Useful for comparing distributions across groups.

Heatmaps

Show intensity of values across two categorical axes; good for correlation matrices or showing patterns in large tables.

Network diagrams and Sankey diagrams

Use for relationships, flows, and connections where the structure or pathways are the focus.

Maps (choropleth, point maps)

Spatial data needs spatial visualization. Choropleth maps show intensity by region; point maps display individual observations.

Design principles for clarity

Start with the question

Decide the story you want the figure to tell. Every design decision should support that narrative.

Choose the right chart type

Match data structure and decision task (comparison, distribution, composition, relationship) to the visualization.

Reduce clutter

Remove unnecessary gridlines, heavy borders, and redundant labels. Use whitespace intentionally.

Use color meaningfully

Use color to encode variables or highlight important elements.
Prefer color palettes that are perceptually uniform and colorblind-friendly (e.g., Viridis, ColorBrewer palettes).
Avoid using color alone to convey critical distinctions — combine with shape, line style, or direct labels.

Label directly

Place labels close to the elements they describe instead of relying solely on legends. Direct labeling reduces eye movement and cognitive load.

Keep axes and scales honest

Start axes at zero for bar charts to avoid exaggerating differences; log scales are appropriate when data spans orders of magnitude but must be clearly indicated.

Typography and sizing

Use readable font sizes for labels and legends, especially for figures intended for presentations or posters. Maintain consistent typefaces across a report.

Use annotations strategically

Call out important data points, turning points, or anomalies with concise annotations. They guide interpretation without overwhelming.

Accessibility and inclusivity

Ensure color palettes are colorblind-friendly; also provide high-contrast alternatives.
Include descriptive captions that summarize the key insight and note data sources.
For published figures, include alternative text (alt text) sufficient to convey the main insight for screen readers.
Consider tactile or printable versions for in-person accessibility needs.

Common mistakes and how to avoid them

Overplotting: when too many points obscure patterns. Use transparency, aggregation, or jittering.
Misleading baselines: truncating axes to exaggerate differences. Use consistent scales and note any non-zero baselines.
Too many categories: break into multiple panels or use interactive filters.
Poor color choices: avoid rainbow palettes that distort perception.
Excessive decoration: 3D effects and glossy styles often reduce clarity.

Layouts and multi-panel figures

Use small multiples (repeated simple charts) to show comparisons across groups or time — easier to interpret than complex single panels.
Align axes across panels for straightforward comparison.
Use consistent color and symbol encodings across panels.
When space is limited, consider sparklines for compact trend display.

Tools and workflows

For quick charts: Excel, Google Sheets.
For reproducible, high-quality figures: Python (Matplotlib, Seaborn, Altair, Plotly), R (ggplot2, plotly), Julia (Plots.jl).
For interactive dashboards: Dash, Streamlit, Shiny.
For vector-graphic refinement: Adobe Illustrator, Inkscape, or directly export SVG from plotting libraries and edit.
For collaborative workflows: Jupyter notebooks, R Markdown, or Quarto for literate programming and reproducible outputs.

Suggested workflow:

Clean and preprocess data; confirm units and missing-value handling.
Sketch the figure on paper or digitally with a clear goal.
Build a first draft using code for reproducibility.
Iterate on labels, colors, and annotations.
Export in appropriate formats (PNG for web, SVG/PDF for print/vector).
Add caption, alt text, and provenance/source notes.

Examples and brief case studies

Business reporting: A monthly dashboard uses a combination of sparkline trendlines, bar charts for categorical revenue, and a heatmap of customer activity to guide marketing decisions.
Scientific publication: A paper uses scatter plots with regression lines and error bands, combined with violin plots showing distributional differences between experimental groups.
Public policy: Choropleth maps paired with small multiples help policymakers spot regional disparities in health outcomes and track change over time.

Measuring effectiveness

Ask whether the figure answers the intended question within 5–10 seconds for a typical reader.
Run quick user tests: show the figure to colleagues and ask for their one-sentence takeaway.
Track engagement metrics for interactive visuals: time-on-chart, hover events, and filter usage.

Final checklist before publishing

Does the figure have a concise, informative caption?
Are axes and units labeled and interpretable?
Is the color palette accessible?
Are important points annotated or highlighted?
Is the visualization reproducible and documented?
Is source and date/data-version included?

Visual figures are not just decorative—they are cognitive shortcuts that, when designed well, turn complexity into clarity. Invest time in choosing the right type, refining design, and testing comprehension; the payoff is faster insight, better decisions, and clearer communication.