Another very important element of the first group of contextual components is, annotations. So this is often overlooked, but annotations can be extremely powerful. What do I mean by an annotation? I mean, adding graphical elements in your graph that actually explains some of the patterns or some of the objects that are of particular interest to the reader, okay? So, in a way annotations are a way to guide the attention of the reader and then typically textual elements that explain why this particular element of the graphics is important or interesting or useful. Let me give you an example that comes from a personal project. So, this is a personal project of mine that I developed some time ago. And it's a visualization of a dataset that I've created. This dataset is a dataset that records information about when I am working and for how long I am working. More precisely, when I am working in a deep modality, I've taken these ideas from a book that is called "Deep Work." So this is when you are working on something without being interrupted. So, one thing that I did was recording this information for a whole year in a spreadsheet and I had information about the day when I'm working, of the time that I'm working, for how long I'm working, on what particular project, what type of activity, the location where I'm working from and so on. So it's an interesting personal project. Why am I showing this to you? Because in the project, I've actually been using annotations quite a lot. So, this is one of the graphs that I used in the project. This graph is actually an histogram that shows how frequently I work for a given amount of time, for a given duration. So, on the X-axis you see duration and on the y axis you see how many sessions I had of that duration. You can see they are mostly clustered around 20 minutes and 60 minutes more or less, but that's not the point here. The point is that on top of this graph, I've been adding a number of annotations, so that the reader can actually read the annotations and interpret the patterns that are seen there. For instance, look at this one. Between 30 and 60 minutes is my deep work zone, is what is most common. Another one is at the far right of the chart, more than two hours of uninterrupted deep work. These are very long sessions and not surprisingly are pretty rare. And another annotation that I have there is setting Pomodoro timer to 60 seconds. So, that's a technique that I've been using when I said a timer for exactly a certain amount of time. And that's why I have that peak there that is exactly at 60 minutes. So it explains why we have these very somewhat anomalous peak in this chart. Let me show you another chart from the same project. So, in this particular chart, I have time of day, hour of the day on the x-axis and on the y-axis, I have different locations that I've been using to do work. So, the first row is Tandon, which is actually the university, the NYU school where I work. The second one is Bobst, which is actually the NYU library and the third one is Home, when I'm working from home. And the size of the bubbles in this case is how frequently, I've been working at that time of the day in that location. And as you can see different locations have different trends and in order to help the reader focus on interesting patterns, I have added three annotations. So the first one is that there is no proper lunch break at work, it is actually a pretty sad one. So when I'm working from my office, I am often working during the lunch break, which is probably not a very good habit. Then we have at the bottom, we have between, I think this is between 7:00 and 9:00 PM. When I'm at home, I'm never working between 7 and 9 p.m. Why? Well, because I have to take care of my kids and I just can't work. So there is a big gap there. And the last one is I'm never working late at night from the NYU library. So, the last annotation is the library is too depressing at night and I never have any Deep Work sessions at that time of the day, in that location.
