Colorism in Fashion:
The first article chosen is Colorism in High Fashion published in April of 2019 on The Pudding website. The author for this article is Malaika Handa with edits by Amber Thomas and Jan Diehm. This article focused on the past nineteen years of Vogue with the main idea being the representation of all shades of women. The question is not the race of the women, as these hirings have increased over the years, the question is whether all shades are being represented equally and appropriately. Variance between shades is examined by removing the hue and saturation of a skin portrait. In addition, the variance can have other factors such as lighting, marketing, whitewashing, photography, and the race of the individual model. They are keeping in mind that achieving true diversity requires not only a focus on representation but a commitment to authenticity in how all shades are represented. The visualizations that we need to do better is the lightness spectrum. Mainly the imagery shows that women with lighter shades are in larger company, while the outliers consist of a few darker shades of women. In addition, with this imagery, we found the idea of bringing in celebrity models decreased the diversity within these cover models. Although not present in the mind before, the specific celebrities brought in would skew these ideas as then one may be able to pick and choose more undetected.
Another visualization that stuck out to me was the time-described graph with the model’s face apparent. Analyzing this graph the axis is well-labeled, the text describes the graph relatively well, as well as the depiction itself is well done. The reason behind the ‘relatively’ statement is due to the following statement: “Her (Lupita’s) four recent appearances are responsible for tugging the trend towards the left.” The wording responsible leaves an odd feeling as it does not give the correct indication one would be hoping for. However, realizing the language in this way will help inform how we continue ahead with our current project as well as applying proper headings and alt-text to help aid in describing the importance of the issue we plan on discussing within our project.
How Every NFL Team’s Fans Lean Politically:
This article is titled How Every NFL Team’s Fans Lean Politically (https://fivethirtyeight.com/features/how-every-nfl-teams-fans-lean-politically/) it is from 2017, using data mainly from 2016 and is writted by Neil Paine, Harry Enten and Andrea Jones-Rooy. This article, sparked by controversy surrounding former President Trump’s issues with players kneeling in protest for racial in-justice,goes in depth on the political affiliation as it relates to NFL teams and their fan base. It is found that the NFL is more bipartisan than other sport fandoms, finding no correlation between either republican or democrat when searching for the NFL. However, when looking at specific teams and their partisanship it is important to understand location. For example the article mentions Jacksonville as a right leaning location in which Trump through out election did well. Therefore it is not suprising to see fans for that team lean republican. In addition location can have more affect, as seen in the article Green Bay is shown to lean more Republican than the Dallas Cowboys based on media market even though fan poll shows Dallas with the advantage. Here the effects of media marketing are seen in which Green Bay having a smaller market needed to extend their fandom outside of Green Bay to different areas. The visualizations in this article are compelling because the article not only uses various types of graphs, but also is very good at color coding and arranging the data in an easy to read and aesthetically pleasing way. For example. The U.S map that highlights all states with a NFL team and colors that states partisanship in shades makes it easy to understand and follow. Secondly the first visualization with search interest and trump vote share is useful in that the facet wrap allows all the graphs to be in a row left to right making it easy to distinguish the NFL as a pretty bipartisan entity. The text is compelling because it does a very through job at analyzing the why. Instead of just noting the fact that the NFL is bipartisan the article digs into the reasoning behind that in addition to the reasoning between specific team partisanship. In addition to continuing to relate back to why we care and why it matters on a larger political scale. The third visualization on NFL fan political leanings is great. While caption dosent describe how to read the graph the visualization itself is very self explanatory and the title does a good job of describing what the graph tell the reader. However, while the party lean is easy to understand based on the colors there is a column titled Partisan Lean with positive and negative numbers based on party that as a reader I think could use a little explanation as to what they mean and how it contributes to democratic or republican lean. Overall this article gives me the idea that it is good to have a good variation in types of graphs and to be creative in how we choose to show data in a understandable and aesthetically pleasing way and makes the reader engaged in our research question.
With this data are setting out to examine if there is a decrease in
Fentanyl and Heroin accidental overdose overtime in the state of
Connecticut when grouped by age. Fentanyl is an opioid drug that can be
prescribed as a result of chronic pain. Heroin is a drug far more
difficult to access but is also highly addictive. This dataset provides
insight into the discrepancy of accidental overdose across different
drug types. Both politically and socially drug overdose is a national
issue especially when it comes to the prescription of highly addictive
drugs such as Fentanyl. In addition it is important to also examine the
ages at which these drugs are being consumed as policy surrounding age
of prescription and determinants are prescription are an ongoing
political issue.
This data was sourced from data.gov and investigated by the Office of
the Chief Medical Examiner. The investigation includes toxicity reports,
death certificated and scene investigations from 2012 to 2023 and the
data set was last updated in July 2024. The data was taken from the
state of Connecticut and utilized to examine drug related deaths and the
extent to which metabolism of Morphine and Heroin create discrepancy as
to cause of death.
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.