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.
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.
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.
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.
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.
