How to Create Interactive Charts?

What are interactive charts?

Users can choose which data segment they want to view and in what format. The available options most often include line charts, column charts, tables, and even a world map.

Why create interactive charts?

According to a MarketingLTB report:

• 62% of users prefer interactive content,
• 72% of audiences remember brands that use interactive content better,
• 91% of buyers prefer visual, dynamic content to a static block of text.

There are also practical benefits:

1. Interactive charts can be connected to a data source and update automatically. The article remains fresh, while the company does not have to spend time or budget monitoring content changes.
2. Chart creators can easily adapt colors and the default chart type to their brand. One-time configuration is enough to generate consistent, attractive charts quickly.
3. With more advanced solutions, users can export data, track changes, and even access an API. The possibilities can be expanded over time.

It is worth noting that these benefits are not limited to visualizations. Static content can also be connected to a data source, allowing an article to refresh not only its graphics but also entire sentences.

How do you create interactive charts?

Depending on your skill level, there are three solutions available:

Ready-made tools such as Canva

Canva is the best-known example, but the way these platforms work is similar in most cases.

What does the process look like in practice?

1. Choosing a chart template – after logging in, we select a visualization type, such as a line, bar, or pie chart. The interactivity is predefined and usually includes hover tooltips or switching between data variants.
2. Entering data – unfortunately, data must be added manually by pasting a table or imported from a CSV file. This works well for simple comparisons but quickly becomes problematic with larger datasets or frequent updates.
3. Customizing the appearance – we can change the colors, fonts, axis labels, or legend.
4. Embedding the chart – the interactive chart can be exported as an iframe or an embed link.

This solution is quick, but it has clear limitations: no automatic data updates from an API, no advanced logic such as filters or relationships between series, and limited control over accessibility and performance. It works well for simple marketing content but is less suitable for content that needs to be scalable, effective, and low-maintenance.

A data platform such as Monitly

In this case, simply choose the data you want to include in your article:

After selecting a dataset, you can customize its appearance and then click “Export”. For an article, choose “Embed” to easily add an interactive chart that updates automatically.

You can also export the chart for other purposes:

• PNG – as a static image for a document or presentation,
• CSV – for in-depth data analysis, for example with AI,
• API – to use the data in your application,
• Animation – to create an animation for YouTube or TikTok.

Monitly provides a ready-made solution for chart visualization and also updates the data. This means that you can only use data available on the platform. Several million statistics from providers such as Eurostat, the OECD, the WHO, and data.gov.pl are available.

If you want to visualize your own data, however, you need to choose tools such as Canva or move on to the next solution.

Programming interactive charts from scratch

What does the process look like technically?

1. Preparing the data – data must be structured, most often in JSON or CSV format. With large datasets, this is often followed by data cleaning, aggregation, and normalization, known as ETL processes. Great charts start with a solid foundation: a well-prepared database, such as PostgreSQL.
2. Choosing a library – Chart.js is the most commonly used option because it is simple and quick to implement. D3.js is more flexible but more demanding, while Recharts offers a compromise between the two; we used it to present the interactive charts in this article. Each option requires knowledge of JavaScript and basic frontend development, although AI now significantly lowers the barrier to entry.
3. Interaction logic – this means defining what happens when users click or hover, how the data changes after filtering, which elements users can modify visually, and so on.
4. Integrating live data – to keep the chart updated, it must be connected directly to your database API.

The last element is particularly important: a chart is only as good as the database it uses. Therefore, when retrieving data from an external source, it is worth setting up a script, for example in Python, that runs every day, perhaps through free GitHub Actions, and retrieves the latest data.

Custom charts provide the greatest autonomy, but every change, even a minor one, requires code modifications. You also need to maintain the database and continuously monitor data updates and quality. For this reason, this solution is best reserved for large-scale projects.