1. Exploring user data
Let's begin to explore who the Divvy users are. We'll start by adding the counted value of all trips to the Columns shelf. This represents the total number of rides completed with Divvy bikes. Let's slice and dice that particular group of data using the characteristics available to us in the dimensions part of the data pane.
We are going to use user type first and drag it to Rows. Let’s also look at the gender of the users. Note, that the field that is listed first in the rows shelf, will appear first in the table as well. As you can see usertype is the classification of the kind of relationship users have with the Divvy organization. Now, let’s also bring gender to color on the Marks card to give the visualization some additional characteristic and flavor. I'm going to make this more readable by changing the fit to Entire View.
In the legend Marks card for Gender, we can see that there are Null values. We know from Divvy that this data is missing for people that don’t have an ongoing subscription with the company, but are day pass users instead. So let’s relabel them to represent that information. You can do that by right-clicking on Null and then Edit Alias. We’ll change the alias to Day Pass Users. So, the null values are more appropriately labeled now.
Let's also add some filters and find out where the users were going! We are going to add To Station Name and From Station Name as filters. The shortcut to get that done is to right-click on the field and say Show Filter. Right click on the other field and then Show Filter again. That quickly adds them to the filter shelf and provides us with the legend on the right-hand side. Let's reform how this is presented and use a single value drop-down menu for both.
Now, we also want these filters to only show the options that díd exist, not that could exist. This means that if nobody took a trip from station A to station B in the past, we don’t want it to show up in the filter options. The way to do that is to select only relevant values for both of these. Next, I'm going to clear the filters so it goes back to looking at all the trips that are in the dataset at the moment.
Now, I'd like to add a time and date stamp to this. So, let's take our start time and add it to the filter shelf. By dragging it onto the filter shelf, rather than using Show Filter like before, it gives me more choices. I want to look at the start time in the Month and Year format, and then click next. I don’t want to leave out any of these months for now. So I'm going to say All and that will select each one of them and bring them forward. Now, because I didn't use the shortcut and I did the additional level of detail in my selection, I also need to tell it to put the legend on the right hand side by showing my filter. Now we have it over here.
Let's also add the Apply Button to the filter.
Finally let's add a refinement to our x-axis. That will help us show a name that is more clear to the consumers of our visualization. I'll change the name of the field that's being shown here on the x-axis, and rename it to Number of Trips. This will make it more clear in our communication. And there you go. We now have a first idea of who uses Divvy bikes. Your turn!
2. Let's practice!