Now let's dig deeper on what is temporal information and what kind of structure temporal information can have. So, first of all, you might have noticed already, with the previous example, that typically, temporal information is about two elements: time and dates. It's very common to find in datasets, both of these components, but there are also cases where there are datasets where you have data recorded only in terms of time or data recorded only in terms of dates, but the general case is date plus time. Now let's talk about time structure. What is really interesting and unique about time is that time can have different kind of structures or can be considered as having different types of structures. So, the first one, the first categorization is between sequential time and cyclic time. What do I mean? Well, it means that if you look at time, you can consider it under the lens of something that changes over time indefinitely, so starting from our starting point and it goes sequentially towards the future, but you can also consider time as cyclic. So, for instance, if you look at the days of the week, or hours of the day, or say months in a year and so on, then time as our cyclic structure, and you can in principle and also in practice do data analysis and communication and visualization by looking at the structures, at the cyclic structure of time. Another very interesting property of time is that time typically can be considered at different levels of resolution there's are hierarchical structure. So, for instance, we can go from month to weeks, and every month has a certain number of weeks, and from weeks to days, and every week has a certain number of days, and every day a certain number of hours, and so on. So, this, as we will see, has a really big implication on how to visualize temporal data. Well, why? Let me give you a preview. Well, because since time is hierarchical, we can represent hierarchical information visually or/and we can also aggregate time at the level of resolution that we are interested in. So, the implication is actually not trivial because we have to aggregate or disaggregate data and we have correspondingly different levels of details in the visual representation. So, let me give you a few examples. So, this is a dataset showing sales of a company over time. In this coming examples, I'm going to use probably the most common visual representation for time data, which is line charts. So, don't worry. I'm going to talk about much more specifically about line charts and other visual representations available for temporal data later on, but for now, just for the purpose of giving you some examples to show you the structure of time, I'm going to use line charts. So, when time is considered sequentially, we can answer questions such as how did our sales change over the years, across all the years that we have recorded in the dataset. As you can see here, we go from 2013-2017, and the number of sales go up and down, up and down, but eventually, across all years, you can see there is a trend upwards. So, this is considering data as sequential. We go from a starting point to an endpoint. But if I ask you, how does the number of orders change by the day of the week? So, once again, I'm using a line chart to create a graph that answers this question, but what is interesting here is that every data point in this graph represent a value that belongs to a specific day in the week. So, this question is inherently cyclic because we don't look at something that changes over time sequentially, but we look at something that changes over time cyclically. So, that's very important consideration for temporal data, keeping in mind what kind of structure time is. So, to summarize, we have sequential data, sequential time, cyclic time, and hierarchical structure.
