I loaded three fact checking analyses from Politifact (links below) into a workspace for analysis and classified the findings as: True, needs context, misleading and false.

I extracted the information from the text of each analysis and sorted each claim into one of three four categories based on the Politifact analysis.

I am not super political but this data is fascinating and I’d love to repeat it across multiple different fact checking sources. This post is intended to be a visualization from one source across three election cycles. However, there are sooo many other fact checking sources out there. My request is that you drop a link to any you think I should include in a comment below and I will repeat this visualization for each source and post a link to any fact checking sites you think would be interesting to visualize.

Data source: https://www.ispo.com/en/health/sport-health-retail-9-game-changing-mindshifts-future

This is the only pan-European survey of over 1,800 people that focuses specifically on the interaction between sport and health in order to provide information and guidance for decision-makers in sport and healthcare. The white paper also clarifies the potential in retail, why corporate health is important and where the sports and healthcare industry need to address the unfulfilled expectations and deficits of the healthcare system.

Data: https://en.wikipedia.org/wiki/Lists_of_school_shootings_in_the_United_States

Tools: R

General description: each cell represents the total for that day of the week of individuals injured or killed in a school shooting (see definitions in the source above). I started with looking at day of week within week of month within month of year but it was way too difficult to read and I probably would have had to more heavily process the data (due to nulls across weekends and other considerations with how to count a week). This analysis involved aggregating data on school shootings by date, calculating the total incidents per day, and extracting date components like the week of the month and the year. A calendar heatmap was then created using a color gradient from light purple to dark yellow to visually represent the frequency of incidents across different decades, months, and weeks within the month, with outliers mitigated using log scaling. I ordered it by August through July to sort of go from start to end of school year, and put the day of the week starting on Monday to better illustrate the first day of school for the week.

If people are interested I will post the data/code.

And here is one where I adjusted it to be just week of year. Again, more legible than both day of week and week of year but I felt day of week was more interesting. https://imgur.com/QdGeGIs

The US Census Bureau just released official income data characteristics for 2023. Here is a visual on some of these by me. See source for defintions of characteristics.

##How to read (Example):

"High School Diploma": Median Household Income for a household where householder has a high school diploma (but no college) is $55,810. 24.1% of ALL US households fall into this category.

Source: https://www.census.gov/library/publications/2024/demo/p60-282.html

Tools: Created with excel.

Cardinals had a probability of winning close to 80% when they we're leading the Game by 17 points. Bills change their probability up to 60% when they scored their second TD.

Data from R package nflreadR. I created a python function to automate the process by taking only the home team and the date to return the plot with the logos, lines and scores. Any feedback is apreciated.

Source article: https://www.csmonitor.com/Environment/2024/0909/air-conditioning-heat-wave-electricity-costs

Data Source: https://www.eia.gov/consumption/residential/ https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a

Tools: Datawrapper, Adobe Illustrator

Don't think this has been done before (words spoken yes, but not lines per character)

Observations:

• Dwight is only character that has a line in every episode.
• The character with most lines in 1 episode is Michael with 159 lines in season 4 episode 18 (Goodbye Toby parts 1&2).

5 Characters with highest average lines across all episodes:

• Michael 59
• Dwight 37
• Jim 34
• Pam 27
• Andy 20

*next best Kevin with 8 lines, 12 lines less per episode. These 5 characters where by far the most present in the series

Creed has it's own fanbase with only 2.1 lines per episode as well as Nate 0.3 Lines per episode.

5 Characters with highest average lines only in episodes with minimum 1 line (excluding episode where they not appear or speak at all):

• Michael 80
• De Angelo 40
• Dwight 37
• Jim 34
• Holly 33

Notes:

• Episode with 2 parts are counted as 1.
• Deleted scenes and webisodes not included.
• The analysis includes only lines spoke by 1 character and not lines spoken by 2 or more characters together (e.g. singing or group exclamations).

source: https://transcripts.foreverdreaming.org/viewforum.php?f=574 tool for scraping and viz: Python

let me know what you think