💃 Let’s Bring Them to Life!

👋 Hi!

Last week, I gave you the shortest explanation of how a graph on a website works. So now you know what HTML, CSS, SVG, and JavaScript are.

95% of you said you're interested in how the web works, so today I want to take it one step further and answer this question:

➡️ Can I make interactive graphs with R or Python, and how does it work?

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Short answer: yes.

Both R and Python offer powerful libraries that let you build stunning interactive charts with just a few lines of code.

In R, this family of packages is called HTML widgets. In Python... I'm not sure there's a name for it.

I love HTML widgets.

I couldn't live without plotly. The ggplotly() function turns any ggplot2 chart interactive with just one line!
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There's also ggiraph, which allows finer control and lets you link multiple charts together.

You’ll also find chart-specific packages like leaflet for interactive maps, DT for interactive tables, and dygraphs for time series.
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Just invest a couple of minutes and the wow effect is guaranteed next time you show it to your boss.

In Python, it works pretty much the same way.
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​Plotly is available too, along with solid alternatives like Bokeh and Altair. And just like in R, you’ve got chart-type-specific tools like Folium for maps and dash_table for interactive tables.

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How does this dark magic work?

I said HTML, CSS, and JS are necessary for interactive graphs. So how can R and Python handle it so easily?

Because all those packages are actually wrappers around JavaScript libraries.

A wrapper is just a function that calls another function behind the scenes.

For example, when you call Chart() in Altair (Python), it builds a JSON description of your chart. That JSON is passed to Vega-Lite, a JavaScript library, which generates the actual HTML and JavaScript that draw the interactive chart in your browser.

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So yes, you're technically writing HTML + CSS + JavaScript. You just don’t realize it! 🔥

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How to share it

Since the output is HTML, your graph needs a browser to be viewed.

That has a big impact on how you share it.

You could attach the HTML file to an email... but that would be a shame.

If you’re using GitHub (and if you’re not learning it should be your top priority), your HTML file is already on the web!

You can turn it into a public website and share a URL with your collaborators. Your work will be accessible forever, and you can update it anytime without sending your 1000th email.

This is the main topic of my productive R workflow project, and I will give you some very practical tips about it next week!

See you there!

Yan

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PS: Something about the web still confusing or frustrating you? Just hit reply! I read every message. 🫡
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​PPS: I’m receiving a bunch of awesome testimonials about Matplotlib Journey. Thank you so much! (And don’t forget the course is half-price for groups, academics, and lower-income countries.)
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