R Tutorial: Exploring temporal structure

Want to learn more? Take the full course at https://learn.datacamp.com/courses/network-analysis-in-r-case-studies at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Up until now, we've been just looking at one daily snapshot of the co-purchase graph. However, we have access to four days of data. Let's start to think about how we can use all the data. The first thing we'll need to do is extract all the unique dates from the raw data. We'll then build up our graph from the earliest date, in this case, d[1]. Just like before, we'll find important vertices based on high out-degree and low in-degree. Finally, we'll be joining these vertices back to the data at each unique date so we'll store them as a data frame. Now we want to see how the important vertices look at each snapshot. To do this we'll need to do a few things. First, we'll create a list that will hold an igraph object at each timestamp. Because we want to plot at each timestamp all together, we'll set the graphical parameters so we have a 2x2 grid to plot on. Now we have to loop over each timestamp, creating a dataframe of important vertices from the first timestamp. We do this by filtering the raw data by date and then joining that back on to the earlier data frame of important vertices we created. Next, we create an igraph object and store it in a list. Lastly, we plot that graph, setting the date as the main title. The earliest graph shows all the vertices meeting the criteria of being "important vertices", which is exactly what we'd expect. Looking at the next three weeks though we can notice a couple of things. First, there are just many fewer vertices, meaning that those important vertices in the first week aren't even present. Secondly, later graphs include vertices that don't meet the criteria; they just have an out-degree of one or two. However, we can see that a few are important across time. Now in the next lessons, we'll be exam