How to Create Interactive Charts?

What are interactive charts?

Readers can choose which section of the data they want to see and in what format – the most common options 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 more effectively,
• 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 themselves. The article stays fresh, while the company does not have to spend time or budget monitoring content changes.
2. Chart creators can easily adapt colours and the default chart type to match their organisation. One-off settings are 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 continuously.

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

How to create interactive charts?

Depending on your level of expertise, there are three options:

Ready-made tools such as Canva

The best-known example is Canva, but most platforms of this kind work in a similar way.

What does the process look like in practice?

1. Choosing a chart template – after logging in, choose the type of visualisation you want to use, such as a line, bar or pie chart. The interactivity is predefined and usually includes hover tooltips or switching between data variants.
2. Adding data – unfortunately, data must be entered 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. Customising the appearance – you can change colours, fonts, axis labels and the legend.
4. Embedding the chart – export the interactive chart 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 is suitable for simple marketing content, but less effective for content that needs to be scalable, efficient and maintenance-free.

Data platforms such as Monitly

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

Once you have selected a dataset, you can customise its appearance and click “Export”. For an article, select “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 more 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 chart visualisation solution and also updates the data. However, this means that you can only use data available on the platform. Several million statistics are available from providers such as Eurostat, the OECD, WHO and dane.gov.pl.

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

Programming interactive charts from scratch

What does the process involve technically?

1. Preparing the data – data must be structured, usually in JSON or CSV format. With large datasets, this often involves cleaning, aggregation and data normalisation, 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; it is the library we used 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 when filters are applied, which elements users can modify visually and so on.
4. Integrating live data – to keep the chart updated, connect it directly to your database API.

This final element is particularly important: a chart is only as good as the database behind it. When retrieving data from an external source, it is therefore worth setting up a script, for example in Python, which runs every day, perhaps through free GitHub Actions, and retrieves the latest data.

Your own charts provide the greatest autonomy, but every change – even the smallest one – requires code intervention. You also need to maintain the database and continually monitor data updates and quality. For this reason, it is worth choosing this solution only when working at a significant scale.