Noah's PDC Blog

Group Project Reflection

By Noah Scholfield

April 15, 2019


how have you and your teammates approached the assignment?
For this assignment we mostly focused on finding differences between young adult and adult fiction books, mostly focusing on how they are reviewed. Much of the work we did was just collecting all the information about the books and analyzing all the reviews for each book.

how you engaged in revision for the assignment?
When we were first planning what we were going to do for this assignment we were going to look at keywords used in all the reviews but that would have been way too much work so we decided to see if there was a significant difference used in the keywords of all the reviews for each category combined instead. We were also initially planning of getting 5 reviews for each book but we decided that that would be too difficult so we decided to find 3 instead.

how did you integrate web design methods in the website's presentation?
We made sure to use a nice clean design so that the focus is on the data and that everything is easy to read on any device. We also used data visualizations that looked like they went well together to make sure our whole site looks cohesive and professionally done.

how did you decide on and assemble your dataset?
Sarah pretty much came up with the idea for a dataset we could use to compare the perceptions of young adult vs other fiction books. We ended up with essentially two datasets in the end with one for information about each book like author and number of pages, and another collecting reviews on each of the book. Most of the basic book info was found on Wikipedia and for the reviews we tried to find reviews from reliable sources with some sort of editorial review process so we didn’t just get some random person’s opinions.

what kinds of questions do your visualizations address/answer?
Our visualizations mostly answer questions about the different perceptions people have about young adult and other fiction. Some of them also compare differences between the authors and books themselves. For example, we found that there are many more women authors for young adult books that for general fiction books.

how did the smaller assignments along the way affect your choices and the quality of your final work?
The other assignments definitely helped me learn to think more about how to present a dataset in a way that helps explain it and make it easy to understand for someone who doesn’t necessarily know much about the topic going in. The Pittsburgh Dataset project helped me learn how data visualization elements can and should be integrated with text to tell a cohesive, interesting and informative story.

how did collaborating with others affect your choices and the quality of your final work?
Working in a group definitely allowed us to gather a lot more information about the different books that any of us would have been able to do ourselves. It is a lot of work finding all the reviews for each book and consolidating them all into a dataset.  

Other Stuff

When collecting the data we pretty much equally divided up the work so that we could build the dataset as quickly as possible. We initially decided to get information for 100 young adult and 100 adult fiction books for a total of 200 books. Later we reduced that to 80 each when we lost a group member. For each of these books we had to find 3 reviews that we would use to analyze the perceptions people had about each book.

Since I have the most experience in out group with HTML and CSS stuff I did most of the web design stuff. I set up the structure of the page and found and helped pick the Bootstrap template to make the site look clean and professional. I created the word bubble charts using D3 to show the most common keywords found in young adult vs general fiction reviews. One of the most interesting results from these bubble charts was that the words “mr” and “man” were two of the most frequently used words for the general fiction, but not for young adult fiction. It seems like this might be related to the gender gap in authors for general fiction.

After all the text and data visualizations were created, I integrated them and added anchor links to the different sections so that readers can easily navigate and skip between different sections if they want to. After everything was put together into the page I made sure that all of the elements and data visualizations flowed well on the page and looked professional. Basically I glued all the different elements together into a cohesive page that looks like it all belongs together.

While working on this project it was interesting to see the differences between young adult fiction and adult fiction. One of the most interesting things we found was that there are significantly more women authors for young adult books than for general adult fiction books which also reinforced in the words used in the reviews.

Read In Defense of Young Adult Literature


Dataviz D3