We are interested in social phenomena and how interactions between individuals are carried out in specific spaces. This is incredibly crucial in college residency and residence life where developing community and understanding social interactions between constituents can lead to an increased quality of life and the implementation of improved student programming. We know firsthand the importance of trying to fit into a social group. At Grinnell College, where floor community and "self-governance" is a huge aspect of residence life, we think that this project has unlimited potential to demonstrate social interactions, which can be used at the offices of residence life at many academic institutions to promote and maintain communal activity.

What it does

The Network Visualization of Student Interactions allows users to observe how individuals are connected in a student dormitory. Users can use the application to identify networks between participants of a study conducted by the MIT Human Dynamics Lab; both survey and mobile data was collected from these participants as they lived in their own respective student dormitories. Using these data, we can subset the participants into different categories and look at interesting relationships they may have with one another. The lines that connect students to one another represent mobile phone calls, short message services (texts), and the physical distance from one student to another.

How we built it

We wrote this visualization in the R statistical programming language using the visNetwork and shiny packages. visNetwork allows us to take our cleaned data and spatially visualize interactions using network analysis techniques. shiny introduces interactivity between the user and visualization that enables a more sophisticated exploration of the underlying data.

Challenges we ran into

Wrangling data often takes a heavy amount of time since most data come in an unsuitable format. Particularly, we discovered that the data we worked with did not fit well with what visNetwork required in order to properly create visualizations. Additionally, the time constraints of a hackathon made us, at times, sacrifice some sanity and omit certain coding practices that we would have benefited from. For example, we did not necessarily think about how some variables may need to be in a specific format when they could not be given the constraints of a specific package.

Accomplishments that we're proud of

We made it with an hour to spare. For Krit, it was especially exciting to come out with a product that operated relatively well and looked aesthetically pleasing. For Jarren, it was a relief that he could contribute some knowledge and insight despite being a beginner in statistical programming and programming in general.

What we learned

Krit and Jarren both learned that hackathons are taxing on sleep and wellness. Jokes aside, we were able to learn more about the packages in R that can help craft spatial visualizations, like the one that we created in our app. We also learned that teamwork prevails and that a project like this can easily be created as long as both are willing to commit to the cause and stay on-task.

What's next for student-network-vis

We hope add a temporal element to the project since the data that we worked with was organized by month and year. To observe this visualization over time may reveal interesting patterns and relationships of students during the school year. Additionally, the Social Evolution data contain a plethora of other variables including music genre preference, type of relationship between students, and wireless network proximity that we could incorporate into our project.

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