See the interactive map here: https://gcappaert.github.io/datavis/inspections.html

You can click on a point to view or download the PDF report.

Mapping tools: Plotly + Pandas

Data source: USDA APHIS inspections by way of Data is Plural

And again - thats not an argument. You are not capable of defending your believes so you resort to insults.

Don't let the people believe it's capitalism or the queen of the company's fault for the wreck

None of them say that.

They all contain the same explanation I’ve given you multiple times and none support you at all.

What a complete load of high school rubbish

It's never going to happen. I never expected that it would.

The colour scheme is awful. Why not just use a constant gradient with pale for lowest and dark for highest?

The people who live in the area of the latest toxic spill will be so happy to hear that bit of news

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Data collected from the US DOT Federal Railroad Administration and Operation Lifesaver

https://oli.org/track-statistics

https://railroads.dot.gov/accident-and-incident-reporting/train-accident-reports/train-accidents-cause

I used Excel to created the trend line graph and pie chart after cleaning up the data to be more clear and condensed. Items combined and finished in Adobe Illustrator. Some of the categories for the pie chart were combined/reworded to fit the infographic, but the main cause is still conveyed accurately in my opinion.

Quick note about the derailment pie chart: The majority of the reasons are categorized as “other”, but these were hundreds of reasons with little occurrences each. I included all of the major reasons as labeled amounts, and also cherry picked the vandalism statistic as that was something I thought some people were curious about. I do want to make note: vandalism is not the next lowest cause after ‘motorist deliberately caused” category, I simply wanted to make sure the percentage for that was actually shown.

Wait, I actually found it in my old notes lol. I was wrong. It was very similar, but it was life expectancy vs average income by country, and it was animated by year, where each node had a radius based on population.

https://ourworldindata.org/grapher/life-expectancy-vs-gdp-per-capita?country=AFG~ARM~AZE~BHR~BGD~KHM~CHN~GEO~HKG~IND~IDN~IRN~IRQ~ISR~JPN~JOR~KAZ~KWT~KGZ~LAO~LBN~MYS~MNG~MMR~NPL~PRK~OMN~PAK~PSE~PHL~QAT~SAU~SGP~KOR~LKA~SYR~TWN~TJK~THA~TUR~TKM~ARE~UZB~VNM~YEM~BRB~CAN~CRI~CUB~DMA~DOM~SLV~GTM~HTI~HND~JAM~MEX~NIC~PAN~PRI~LCA~TTO~USA

Pretty close to this, but slightly different

Bitches be gambling on court rulings? I love the US

I think the ED is shorthand for EDGE.

Depending on the scheme edge rushers can be lined up at outside linebacker or defensive end.

It’s an interesting animation, but not having an easy way to see the results without manually pausing, and worse yet the final frame resetting to the beginning of the animation makes it challenging to interpret the data. It would have been really nice to have the results listed in order.

And am I missing something or should ED actually be DE?

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All I see is a strip of bacon

It seems to me the more something is left to free market the cheaper it is, while the more something is in regulation or outright supplied by government the more expensive it is.

No, he was just showing the visualization it self (a similar one) to analyze methods for "telling a story through data".

We also watched this as optional extra material https://youtu.be/6xsvGYIxJok

In australia the minimum wage is a anchor point with wages set to an offset from it, meaning that the minimum for most jobs is way higher than that. Almost all jobs have a legal minimum “award” rate which is a set offset above minimum wage.

On-field workouts for the 2023 NFL Combine start today. Here is a look at the average 40-yard dash time by position over the last decade. Cornerbacks have clocked in at the fastest average time at 4.50 seconds, with wide receivers (4.51 seconds) and safeties (4.55 seconds) right behind them.

Data Source(s) and Tools:

This visualization was made by Keegan Abdoo of the NFL’s Next Gen Stats team using the ggplot and gganimate packages in R. The data source is the official measurements from the NFL combine, which we have tracked going back to 2003.

Im not talking about climate change, we are talking about what would happen if all the ice were to melt. Politics and agendas aside, what do you think would happen if all of the ice would melt? I just said sea levels would rise which is what would happen unless you know otherwise

Research it more. Basically everything about climate change is fabricated lies. It’s all politics.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1112950/

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I'm not well acquainted with the data and I don't think the data presented in this graph is useful at all to understand the issue (as I discussed in other comments). So I can't say I acknowledge that it exists.

I would say this is something that needs to be addressed if it was clear that women make less than men when performing exactly the same job, out of sheer discrimination.

By looking at this data alone, there could be so many different phenomena skewing the average. It could be that women prefer professions that make less money. It could be that women have a greater chance of stopping their careers to take care of their families. If that's the case, then the gap in the average is not an issue, IMO.

Please explain?