![]() First, we need to be able to register which bar a user clicks on. A user should click on a bar and the respective plot should be shown. You could add an additional scatterplot underneath with some species-dependent colouring. Of course, there are various ways to accomplish this. Or he wants to verify that the relationship is indeed linear and so on. Perhaps he does not trust the calculated value we visualised and wants to see for himself. However, in the next step, a user might want to check the individual measurements. We can observe that correlation between length and width varies between species. In our case, we use the iris dataset and plot the correlation between sepal length and its width. ![]() We start with a simple barplot that allows the user to check the correlation between two variables. In your app, you can use Plotly’s event data to trigger filtered plots or tables with one click. However, often people reading these graphs want to dig deeper after the first glance. Plots in your application can give a great overview of events and allow you to observe characteristics like trends or seasonalities. ![]()
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