Possibly, I guess? It's not sourced so we're only guessing.

Also they contract out a lot of stuff like cleaning and cooking. Source: ex NHS (business services) employee here

Could it include people who are contracted to work for NHS but don't actually work for the NHS, like dentists? It's still wrong if that's the case, but might explain the difference.

Data for Iran is likely untrue, stock market capitalization is around $1.3 Trillion (Ceic data), so according to this graph the GDP is around $250 Billion and in terms of GDP per capita it would be around $2900, far below Iraq where it's over $7000. 2900 is around the average for african countries. The GDP is prob. around 500bn to 700bn.

unfortunately there are different figures that cause confusion, IMF says 2 Trillion but they go by the official exchange rate but the real exchange rate is much worse, but anyway, no way Iran would have a GDP of 2 Trillion anyway, World Bank says 230bn in 2020 which is also ''no way'' territory. UN in 2020 says 940bn, that's more likely but i don't believe Iran's would be higher than Turkey (850bn), both have a similar population, 86.5m in Iran, 85m in Turkey.

World Bank also claimed Iran's GDP PPP in 2020 (Purchase Power Parity) is 5.74x higher than Nominal, at 1.33 Trillion, even in the country with the highest PPP to Nominal Gap, Sudan, it's just 4.85x higher but in Sudan it makes sense as GDP per capita nominal is very low at $920.

Edit: retraction, I was confidently incorrect as pointed out below

20.5 + 79.5 = 100

So…. This chart is wrong! And maybe the sources too.

So I checked the NHS statistic, and according to the NHS November report they employ almost 1.4 million people. Initially I thought this was really high, but the number for France is a little over 1.3 million so I guess it is fair. I'm just surprised by how high it is (about 1 in 50 people work for the NHS).

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Often the data is fine but the interpretation is so far removed from the actual axes that I want it to be removed and banned. Revise and resubmit!

I found it hard to track in a static visualization but I bet it works better on a dynamic platform on a desktop but not well over my phone;

I do like the context provided by the middle two charts.

The coolest thing about penalty kicks is that on average, the xG of the chances before a foul in the box normally averages out to be ~80%. Meaning that penalties come from fouls or infractions that would've resulted in a goal 80% of the time. The fact that they have an 80% success rate means they are the perfect penalty for a foul in those locations!

The inverse of Hugo Lloris's stats.

Ministry of Defense of Russia needs to be adjusted 100,000 down. Only 1.8M now...and falling.

McD should top Russia soon, the way the war is going.

Brazil, Colombia and Peru surprised me because I know they’ve each taken in quite a few Venezuelans…I mean hasn’t Colombia taken in over a million just by itself? And that’s just from one country. But then when you factor in how large your country and my country and others are, it makes sense. But, at the same time, this data doesn’t reflect that the US has around 50 million immigrants (almost 25% of the global total) and would make you think we’re slacking lol. And I bet Brazil and others and underrepresented too

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In the 2021 NFL season, just over 85 percent of field goal attempts were successful.

20-21 NBA? 78.5% of free-throws! So stupid!

/s (just in case)

I was going to point out why the US non military personnel seems low, China's has a lot of non military personnel but I'm stuck on Walmart being on the list.

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Americans be like: Is that Russia?

Lets say I have 1000 books that have tags associated with them for example

Book 1: [environment, fiction, story]
Book 2: [fiction, science, space, environment]
Book 3: [science, workbook, space]
....

As you can see any book can have multiple tags. I want to present how many books are in each category, but if I draw a simple bar graph that might not be the best representation since the total of each bar would be more than 1000 and that might be confusing.

Is there a better way to present my data.

The biggest shopping weekend in 4 years was the 2 weeks in the middle of March 2020 as everyone panic bought. Every Year July 4th, Memorial Day, Christmas and Thanksgiving are the normal outliers for shopping

That left April and October as good comparisons

What's even more interesting is that the majority of these jobs are either government jobs or jobs in state-owned corporations.

Source USDA ERS The Weekly Retail Food Sales series

Made in Goodle Sheets

I manually gathered this data from the self-reported EEO-1 data reported by each company. The data and links to their source are here: https://docs.google.com/spreadsheets/d/1Uev9f_sqN7laMtntuf-YQJZrGcj2UFAo18TFNrSap5s/

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I collected all the data I could find in a reasonable amount of time. Most companies do not publicly disclose their EEO-1 data.

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I created this figure using Python and Plotly.

Iirc the success rate used to be below 70%, interesting.