How every NFL Team’s Fans Lean Politically:
https://fivethirtyeight.com/features/how-every-nfl-teams-fans-lean-politically/
2. Include a 3-5 sentence summary of the article.
Among the sports leagues we considered, the NFL had the most search traffic and the least partisan fan base. There is basically no correlation between how Democratic or Republican an area is and how often its residents made NFL-related searches. Not surprisingly, teams that are located within markets where Trump did his best tended to be the places where there are more Republicans, compared with Democrats. Arguably, though, the biggest takeaway from the poll is that every team in the NFL has both Democrats and Republicans in their fan base.
One of the very first graphs that contains the differing subgraphs of all the different sports was interesting. It showed the political lean of each sport, with a trendline for the mean political lean for each sport. What was most interesting was just to see what sports fall where on the political spectrum, as there are obvious ones such as NASCAR leaning right, but also not so obvious ones like NHL leaning left.
One compelling aspect of the text was diving in depth to certain areas that are represented in the tables. The extra information given gave more context in a way that made me understand the graphs in a greater context.
I chose the second image which depicts the geographical location of every team. It does a good job displaying which areas are leaning more democratic than republican, but there is a legend that is not explained well. It shows the differing levels of democratic vs republican, but contains numbers from a scale of -50 to +20 that are not well explained.
This article gives me ideas on separating data into geographical locations. With our data, we have different species that are from different areas around Alaska, so making comparisons just like the ones in this article would help us understand different trends we may see. ## Part 2: Written Introduction
To hand this assignment in, follow the instruction on the part2_rubric.RMD to publish to Rpubs. Submit the link to your publication on moodle.
Article: Colorism in High Fashion
2. Include a 3-5 sentence summary of the article.
This article investigates how Vogue magazine represents women of varying skin tones on its covers, by analyzing data from the past 19 years. It explores colorism—a form of discrimination where lighter skin is favored over darker tones—and highlights how this bias impacts women of color, particularly those with darker complexions. The study reveals that while more women of color are being featured, dark-skinned women remain underrepresented. Lupita Nyong’o, one of the models who was interviewed, accounts for the majority of dark-skinned women featured. Ultimately, the article suggests that meaningful diversity in fashion will require deeper changes in hiring practices, including more black photographers and a broader commitment to diverse representation.
What is one thing that was particularly compelling about the visualizations?
One to thing that I found compelling was through the use of visualizations as a way to help describe the data collection process. The study took the covers of many issues of Vogue magazine, then isolated the face of the model, ran facial recognition, then removed the background using a K-Means clustering algorithm, and then found the average color value of all the ‘skin’ pixels in the face. This process is animated so while you scrolled through the article it displayed step by step how the images were manipulated, alongside text box descriptions. In fact, all of the visualizations are animated, so while you scroll it allows for more simple charts to be described one at a time instead of looking a a big blob of confusing data. They also mapped the model’s faces to points on scatter plots which I though looked really interesting.
What is one thing that was particularly compelling about the text?
One particularly compelling aspect of the text is the section talking about tokenism through Lupita Nyong’o’s repeated appearances on Vogue covers. The analysis doesn’t just show that dark-skinned women are underrepresented; it emphasizes how one person—Nyong’o—has been repeatedly cast as the “face” of dark-skinned women, highlighting Vogue’s reliance on a single individual to claim diversity. I thought that this helped to focus on a how using a single model underscores the superficial nature of progress in representation, making it clear that true inclusivity in high fashion has yet to be achieved.
Pick one visualization from each article. Did the authors do a good job describing the visualization through the figure title, figure caption, or in-text description? What was something missing or confusing about the figure/figure description?
One visualization from this article that stood out to me was the chart showing year on the y axis, and skin tone on the x axis, with each point showing the face of the model. The authors did a good job describing and introducing the data, the graph itself has no title, which I thought was odd. The one text box that pops up right before this chart is a note drawing attention to the lack of darker-skin-toned models, and it also shows different controls/options that the user can input into the graph which allows for interaction. I thought the authors did a good job with this but I did think that the lack of labels and a title was a little confusing. There is an option to show a single model to see where they are but I think it would have been nice to be able to hover your cursor over data-points to show the names of the models.
What is one idea this article gives you as you think about your own data set and project?
One idea that I got from this article for my project is the use of interactive charts/graphs/visualizations. Although I do not think we are on the level of animating these charts, I think it would be beneficial if we could put multiple graphs or alterations of graphs showing the same thing allowing for users/viewers to select the one that makes the most sense to them while observing it.
Also, this article highlights the importance of looking beyond surface-level diversity and considering the nuances within data representation—specifically, the need to investigate which groups are genuinely included versus just symbolically represented. In my own project on Alaskan salmon species, I can apply this approach by not only examining broad trends but also breaking down the data to see if certain species or attributes are disproportionately highlighted or overlooked.
Context and Background:
In this project, we will be exploring data on the lipid and biochemical makeup of marine life in Alaska. This dataset, collected and maintained by the Nutritional Ecology Laboratory, contains detailed information on various species, from small organisms like phytoplankton to large animals such as whales and seals. This data was last updated in April 2024 and covers a wide range of factors, including the size, age, and developmental stage of the creatures sampled, along with seasonal information and energy content. It seems like an incredible resource for understanding the nutritional profile of Alaska’s diverse marine species and ecosystems. Our specific focus is on salmon species, which are critical to Alaska’s ecosystems, economy, and cultural heritage. By analyzing different species of salmon, we aim to uncover trends over time and across various areas/regions of Alaska by looking at the weight and size of these salmon. Splitting the data by region (and subregion) as well as by species will allow us to detect patterns that could indicate changes in the salmon population’s. We will be able to see differences in size over time, as well as over different parts of the state to see the interplay between these variables. Ultimately, we hope to reveal insights into how environmental factors may be affecting salmon—a topic of interest for scientists, fishers, and all those connected to Alaska’s marine environment. Through visualizations and analysis, we plan to tell the story of these changes and what they might mean for the future of salmon in Alaska. There has been significant research conducted on the nutritional ecology and biochemical characteristics of salmon and other marine species for the use of food wed dynamics and fisheries management. Included in these are studies about the changes in size of salmon over time and hopefully our project will find evidence that supports this prior research.
Description of the Data:
You should answer the who, what, where, why and how of the data collection in this description.
The lipid dataset of Alaskan fish, marine mammals, and invertebrates comes from the Nutritional Ecology Laboratory, which is part of the Alaska Fisheries Science Center (AFSC) and the Alaska Biological Laboratory (ABL). The data was collected through systematic sampling of various marine species, including different species of salmon, over several years. Samples were obtained from diverse environments within Alaska, including coastal waters, rivers, and lakes. The collection process involves field surveys and laboratory analysis, where researchers capture specimens, measure their size and age, and examine their biochemical composition. The dataset includes samples collected over multiple seasons and years, providing a snapshot of the species’ health. The last update to the dataset was on April 1, 2024, suggesting ongoing data collection efforts, while the earliest entries are from 1997. The key variables that we will be examining are species (speciesbio), region, subregion, weight and length.
To hand this assignment in, follow the instruction on the part2_rubric.RMD to publish to Rpubs. Submit the link to your publication on moodle.