How to Create Data Visualizations for Research Reports With Tool Suggestions
How to Create Data Visualizations for Research Reports With Tool Suggestions
5 minute readA well-designed data visualization communicates a finding in ten seconds that a paragraph of text would take two minutes to convey. A poorly designed one confuses readers, obscures the finding, and undermines confidence in the research. Most research reports have too many visualizations and too few that actually work. This guide covers how to choose the right chart type, what tools to use, and how to build visualizations that make your findings land.
Data visualization is a standard component of the deliverables we produce in our market research and UX research services. For how to structure the full report around your visualizations, see our post on research reports vs. insight briefs: which one should I use.
We'll cover:
How to choose the right chart type for your data
The design principles that make visualizations work
The best tools for research visualizations in 2026
The most common visualization mistakes
Frequently asked questions
Table of Contents
- 1. How to choose the right chart type
- 2. Design principles that make visualizations work
- 3. Best tools for research visualizations in 2026
- 4. Most common visualization mistakes
- 5. Frequently asked questions
- 6. Key tips
1. How to Choose the Right Chart Type for Your Data
Chart selection should follow the question you're trying to answer, not the data format you have. Different questions require different chart types.
| The question you're answering | Best chart type | Avoid |
|---|---|---|
| How do categories compare? | Bar chart or column chart | Pie chart (hard to compare) |
| How has something changed over time? | Line chart | Bar chart (obscures trend) |
| What's the distribution? | Histogram or box plot | Pie chart |
| What's the relationship between two variables? | Scatter plot | Line chart |
| What portion does each part represent? | Stacked bar chart | Multiple pie charts |
| What is the magnitude of a single number? | Large callout number or gauge | Any complex chart |
According to research on data visualization cognition by the Data Visualization Society, bar charts and line charts are the most universally interpretable chart types across audiences with varying data literacy. When in doubt, default to a bar chart. Complexity rarely adds clarity.
2. Design Principles That Make Visualizations Work
One finding per visualization.
Every chart should answer one specific question. If you're trying to show two things in the same chart, you probably need two charts. A chart that tries to do too much ends up communicating nothing clearly.
Put the finding in the title, not just the topic.
A chart titled 'Program Completion Rates' tells the reader the topic. A chart titled 'Completion Rates Improved 24 Points After Program Redesign' tells them the finding. The title is the most-read element of any visualization. Use it to communicate.
Reduce chart junk.
Chart junk is any visual element that doesn't contribute to understanding the data: excessive gridlines, 3D effects, decorative images, unnecessary legends, redundant axis labels. Remove everything that isn't doing cognitive work. Simpler charts communicate faster and more clearly.
Use color intentionally.
Color should carry meaning: use it to highlight the most important element, to distinguish categories, or to show scale. Don't use color decoratively. Limit your palette to two to three colors per visualization. Ensure contrast meets accessibility standards for color-vision deficiency.
Always include context.
A standalone number or percentage has limited meaning without comparison. '68 percent completion rate' is more meaningful as '68 percent completion rate, up from 44 percent in the prior year and 12 points above the sector average.' The comparison is the insight.
The best data visualization communicates the finding before the reader has consciously decided to look at it. That's the standard to design toward.
3. Best Tools for Research Visualizations in 2026
| Tool | Best for | Cost |
|---|---|---|
| Datawrapper | Charts for reports and web publishing | Free for most uses |
| Flourish | Interactive and animated charts | Free tier available |
| Canva | Infographics and designed data visuals | Free tier, $13/month Pro |
| Tableau Public | Complex multi-variable analysis, interactive dashboards | Free (public data only) |
| Google Looker Studio | Dashboard reports with live data connections | Free |
| Microsoft Power BI | Enterprise data visualization, integrated with Microsoft 365 | Free tier, paid for full features |
| Observable Plot | Code-based custom visualization (JavaScript) | Free and open source |
The tool recommendation by use case:
For researchers without coding skills who need publication-quality charts: Datawrapper. Clean output, fast learning curve, free for most research uses.
For researchers who need to communicate complex findings to non-technical audiences: Canva. Strong infographic templates, accessible design tools, widely understood output.
For researchers building dashboards with live data: Google Looker Studio. Free, connects to Google Sheets and most common data sources.
For researchers who need interactive charts for web reports: Flourish. Excellent interactivity, good free tier, minimal coding required.
4. The Most Common Visualization Mistakes
Using pie charts for more than three categories. Pie charts are hard to read accurately for anything other than large, obviously different proportions. If you have four or more categories, use a bar chart.
Starting the Y-axis at a number other than zero on bar charts. This visually exaggerates differences. Only truncate the axis on line charts where the relevant variation is small relative to the total range.
Using 3D charts. Three-dimensional chart effects add no information and make comparison significantly harder. Avoid them entirely.
Putting the finding in the caption instead of the title. Most readers never read captions. The title is where the finding lives.
Including too many data series in a single chart. More than four to five lines on a line chart or more than six bars on a bar chart creates visual noise that obscures the finding.
Frequently Asked Questions
Do I need to know how to code to create good data visualizations?
No. Datawrapper, Flourish, and Canva all produce excellent research-quality visualizations without any coding knowledge. Coding tools like Observable Plot or Python's matplotlib/seaborn library give you more control for complex custom visualizations, but the majority of research visualization needs can be met with no-code tools.
How do I make visualizations accessible to colorblind readers?
Use colorblind-safe palettes (tools like ColorBrewer provide research-tested accessible palettes), don't rely on color alone to convey meaning (use patterns or labels as well), and test your visualization using a colorblind simulator. Datawrapper and Flourish both have built-in accessibility checking features.
How many visualizations should a research report include?
Enough to support your key findings, no more. A 10-page research report rarely needs more than five to eight visualizations. Each visualization should earn its place by communicating something that text alone communicates less clearly. When in doubt, replace a chart with a well-written sentence and a highlighted callout number.
Key Tips
Match the chart type to the question, not the data format.
Put the finding in the chart title. Not the topic, the finding.
Remove every visual element that doesn't carry information.
Start with Datawrapper if you're new to research visualization. It produces clean, fast output.
Always provide comparison context for every number you visualize.
How Praxia Insights can help
At Praxia Insights, we design and run research that gets to the real answers. Whether you need prototype testing, a stakeholder analysis, or a full research plan, we're here for it.
