AlumniVisualization: Final Project Update

Progress: 

• So far, we’ve just been working on getting the data. We’ve been in communication with alumni services at Carleton. We’ve received a dataset recently but upon opening the data, there seems to have been some miscommunication. We don’t have any information/data for a specific major. Instead, we have a dataset that sums the amount of alumni living in certain cities and countries. So, we’re talking again with the specific Carleton employee from the alumni services to get useful data categorized by majors as that is one of our primary focuses during this project (whether it be a specific major or a few). So, given the data we currently have, we haven’t built anything. However, once we receive the appropriate data, we will quickly begin the process.  

Problems:

• We haven’t been able to make any progress since our data given so far hasn’t been useful to our project’s goals. However, I can anticipate that some problems that may arise will be cleaning the data. For instance some of the cities are labeled slightly differently. For example, there are “Tokyo” entries for the “cities” column along with specific regions and cities in Tokyo, and there are entries of Tokyo with numbers following the entry (i.e. “Taito City, Tokyo 111-0034” and “Taito City, Tokyo”). So most the locations will need to be cleaned to have uniformly labeled locations. 
• Our initial plan remains the same, we just need the appropriate data to begin our work.
• Our solution is using simple commands from the Stringr package in R. Some of our group members have experience with Stringr to edit entries within a column, removing specific numbers and unneeded words, along with reverting some of the entries back to lowercase or uppercase.   

Tools and Techniques:

• For the data cleaning if need be, we will use R since multiple members in our group have experience with. We definitely plan on using ArcGIS for our visuals and displaying the locations of where the alumni live. For our webpage, we’ll use WordPress and within the page embed some HTML and CSS to help make the display look how we want it to look. 

Deliverables:

• We should still be on schedule for what we had originally planned, although not having the dataset to our liking has pushed that process back a bit. Hopefully before the 28th of February we can get our dataset and then begin to plan how we want to clean and display it in the appropriate places.

Personal Messages:

Doug – As of now, we’ve been setting up the groundwork for our project so most of my contributions have been helping with the blog posts and thinking about how we want to showcase our data. More of my role, as described in the team charter, will come once we have our mapping done and I can start putting everything onto our webpage. As of now I have been reviewing the portion of class where we went over all the HTML and CSS.

Chris – My personal task is to clean/organize/tidy the data to be used for our analysis. I already have an idea and experience as to how I’m going to do that. I will use R’s StringR package to remove the unnecessary numbers in the cities column and with the remaining characters, I will then check for upper vs. lower case.

Maya–  So far, I have been helping with just brainstorming and helping to write blog posts since we are still organizing our data. Later on I will do more work with the data like creating an interactive graph or other data visualization. 

Ty – My task is to create the actual ArcGIS mapping of our data. I’ve been working a little bit with ArcGIS so that I’m more comfortable with working in ArcGIS. I’ve also been helping with blog posts and data cleaning as needed.

Hermela- I have been working to get our dataset for our project. I reached out to Carla J. Thomas, Director of Alumni/Development Information Systems, who was able to assist our group with datasets related to alumni whereabouts. 

Useful Links:

• StringR package
  • https://raw.githubusercontent.com/rstudio/cheatsheets/main/strings.pdf