For this lab, I chose to use the rawgraphs.io application to visualize the names data. Overall, the experience was much more troublesome and comprehensive than I originally thought. I had originally chosen to use a circle grouping type graph, the toughest component was figuring out how to separate the data points on the plot. However once I felt like I finally had constructed the perfect graph, to my dismay, the labels (the names) were too large to understand so unfortunately I had to choose a different visualization. As pictured here, I concluded my search by choosing the tree leaf plot (I don’t remember the actual name). First, the size of each individual box represents the count of names and is labeled with the year, name, and further denoted by the color, with blue representing females, green representing males, and the orangish color representing a tie in count, ie. the bigger the box, the more people were given that name that year. Finally, each year is represented by the rectangles that are created by the thick lines dividing the graph. It took a lot of trial and error to create the finishing division of the data, but it seemed to be the most comprehensive. Yet, I still encountered the label problem wherein I could not legibly read all of the names, so I simply made the dimensions larger in order to better accommodate the data and all seemed successful. All in all, this type of project has to directly relate to what Lin portrayed to us in her lecture no matter the approach taken to complete the project; the data must be visual! Meaning that the goal of the assignment was to portray data in a way that can be easily understood. Furthermore, this project in particular related to Digital Humanities because it can be seen as a trial and/or practice run for us as Digital Humanities students to learn how to quantify and qualify data so we can apply learned skills to later, more impactful data sets.
