Cross filters are arguably one of the most powerful features in Graphext. It’s as simple as it sounds: it filters your data given some criterion. It’s the way in which we define these criterion that is so powerful. You can chain however many filters you want, hence the ‘cross filter’ naming. This allows you to hone in to a very thin slice of your data that responds to only the criterion you’ve selected. Changing, removing and iterating on this selection is as simple as a couple clicks, which makes data exploration extremely fast.

Graphext filtering a ~19K row dataset in realtime and stacking filters in several columns

To filter a column, you can simply interact with the little chart that’s associated to it. In the case you reach to a particular selection you may want to preserve, you can do so by clicking on the little dropdown arrow in the top left corner. This will save the current selection as a segment. Saving a selection as a Segment

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Absolute and Relative percentages

Upon filtering, all the other variables react to the filter. They show the relative percentage of entries that fall into their respective categories, effectively showing you a real-time distribution of the selected data, but in every other column. To bring it home, let’s see this example.
Cross Filter Price

We've only selected those Airbnb listings whose price lies between 1005 and 34391. This leaves us with 58 entries out of the 7158 in total.

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Example: Host Acceptance Rate

Upon filtering the price, we see the histogram for host_acceptance_rate changed. Now, it shows a percentage y-scale. The gray bars that sit in the background correspond to the percentage of entries that lie in that bin, had we NOT filtered the data. We can see that the last bar, which corresponds to an acceptance rate range from 100 to 110, goes to just over 60%. That means that over 60% of all the hosts have an acceptance rate of 100 or more. The blue bar on top indicates the percentage of entries that lie in that bin, out of the current selection. Around 55% of the 58 rows we have selected lie in that acceptance rate range. That’s around ~31 rows.

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Example: Is Super Host

Another example is the is_super_host variable, just under it. This variable is either true (t) or false (f) indicating if the host is marked as a Super Host. In the whole dataset, around 63% of the hosts are not Super Hosts (f). However, in our particular selection, this is accentuated. Around 70% of the entries we have selected are not Super Hosts. This can be of relevance, depending the questions we are asking. The opposite also applies: the percentage of hosts in our selection is less than the percentage of hosts in the entire dataset.

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Significant Variables

Also, the significant variables kick-in, showing what other variables may be interesting in regards to the selected one.