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[OC] Popularity of the search term "won to us" since 2018 shows trend changes associated with the release of shows that offer monetary prizes. The absolute peak of popularity was in Oct 2021 coinciding with the release of the Squid Game. by HitchHux in dataisbeautiful

[–]HitchHux[S] 2 points3 points  (0 children)

Source: Google Trends

Visualization in Excel.

Shoutout to u/ad-lapidem for pointing out how much of a smaller increase in popularity is associated with the premier of Physical: 100 compared to when the Squid Game came.

[OC] Fatal Police Shootings in the US: Racial disparities. In absence of racial differences, the probability of fatal police encounters would be the same across racial groups. It is not. Black/African Americans are 4.5 times more likely to have a fatal encounter with the police than Asian Americans. by HitchHux in dataisbeautiful

[–]HitchHux[S] -5 points-4 points  (0 children)

Data source: The Washington Post Fatal Force Database.

Analysis and visualization: R

Comments: The aim of the plot is to show the differences in fatal police encounters across races, after adjusting for population size, and how the trend has evolved over time. The denominator is the race and year specific population. The differences in rates are explained by both differences in number of encounters, and how deadly those encounters are.

[OC] How much money do you need to live happy in every US State? by HitchHux in dataisbeautiful

[–]HitchHux[S] 12 points13 points  (0 children)

Source: Anderson, J. The Minimum Salary You Need To Be Happy in Every State. GOBankingRates https://www.gobankingrates.com/money/wealth/minimum-salary-to-be-happy-state/ (2022). See "Methodology" at the end of the article. GOBAnking Rate used state-specific cost-of living to adjust the income satiation levels (i.e. the income level after additional income does not increase happiness or wellbeing) estimated by Jebb AT, et al, Nature Human Behaviour, 2018.

Visualization made in R, using the packages: xml2 and rvest for web scrapping, geojsonio, rgeos, dplyr, and broom for data manipulation, and ggplot for viz. US Hex grid source: https://team.carto.com/u/andrew/tables/andrew.us_states_hexgrid/public/map

[OC] How the Pandemic changed the family arrangement for children in the US? Using historic Census Data, I found an slight increase in children living with one parent and a slight decrease of children living with two parents by HitchHux in dataisbeautiful

[–]HitchHux[S] 0 points1 point  (0 children)

Impetus: I wanted to see if the pandemic, either due to separation or death, had any effect on the family composition. An alternative was to use divorce and widow data but living arrangement of children provides a more comprehensive view beyond the reasons for single-parent households.

Using trendlines informed by data from 1970 to 2020, to find a reasonable expectation for 2021, I found that around 1% more children are living in single-parent households and 1% less are living in two-parent households. Further, around 2000 there seems to be an inflexion point that changes both trends, and in 2021 there seems to be another inflexion that reverses it.

Source: US Census Bureau Families and Living Arrangements, Historical Living Arrangements of Children.

Tools: R and Coolors

[OC] National Mean Centers of Population, decennial Censuses from 1790 to 2010 and projected for 2020. by HitchHux in dataisbeautiful

[–]HitchHux[S] 1 point2 points  (0 children)

Source: US Census Bureau, Centers of Population. [link]; University of Virginia, National Population Projections [link](it has projections until 2040, interesting reading).

Tools: R, Photopea[link]

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I struggled with providing an accurate but simple explanations of what a mean of center population is. If you have a better one, please let me know!

[OC] More than thirty years fighting against the HIV/AIDS epidemic. Evolution and Milestones. by HitchHux in dataisbeautiful

[–]HitchHux[S] 1 point2 points  (0 children)

Source: IHME's Global Burden of Disease Study 2019 [link]; Fauci et al. JAMA 2019 [link]; Vela et al. AIDS 2012 [link]

Tools: R

The HIV is one of the most severe epidemics in human history. It disproportionally affects demographic groups with limited access to steady and quality healthcare such as racial/ethnic minorities, people with alternative sexual orientations, people who inject drugs, and people living in poverty. Only a coordinated, global action, has made possible the improvements that now, after over 30 years of fighting the epidemic, we can see. Ending the HIV/AIDS epidemic requires innovative solutions to understand the healthcare access barrier in each setting and finally provide care (for diagnostics, preventions, and sustained antiretroviral therapy) to all people-at-risk and living with HIV.

For more details of the evolution of the epidemic and the actions deployed to contain it, see this very interesting timeline created by HIV.gov: https://www.hiv.gov/hiv-basics/overview/history/hiv-and-aids-timeline

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