Foundations of Data Visualisation - Computerphile 

* nervously sweats with 2 perpendicular sweat drops on his forehead and the 3rd drop doesn't know where to go *

When you inflate the balloon, can you do it in the shape of a kitten?

​@@phandao5404You are talking about different Mr Anderson lol

yo thats a swastika?

Let me run that past the ethics committee for a sec.

If we're talking about, visualizations, that must mean the electrodes will be hooked up to our eyeballs. Extra FUN!

Welcome.

thanks!

RGB on a computer screen is a super poor way to refer to saturation. HSV and HLS are super poor derivatives. If you do not understand color spaces and vision in depth you shouldn't be talking about levels of saturation because that is a very complex topic and knowing only RGB values and your run of the mill Photoshop color pickers will lead you only to talking a lot of pseudoscientific nonsense that misleads people. To be more accurate it is necessary to talk about the Lch, Luv and L*a*b color spaces. Then you can use words like "twice as saturated" and have it actually mean something. Luminance is also fraught with peril. Something twice as bright doesn't look twice as bright. When printing on paper or viewing on a monitor there's a maximum brightness but this isn't true in general. Twice the number of lightbulbs will give it twice the brightness but it doesn't look it. It's all a bit too complicated for this comment but suffice it to say that there're not many people that understand it yet there's a large percentage that really think they know when in fact they do not.

@@xbzq Yeah exactly. I completely agree. To obtain something absolute from "a color" is incredibly sensitive and depends on way more factors than you'd expect. It becomes even worse when you use colormaps that vary multiple channels.

I can do another video if there is enough interest about visualising large dataset (I assume this is what you mean by 'high density'). A bit of spoiler: there is no silver bullet for large dataset unfortunately.

@@lens07 How do you define "saturation" that allows you to say that one area has twice the saturation of another?

@@lens07 Yeah, although what I mean with high density is quite specific. So I'm not too sure about the wording. When doing optimization problems, the results should converge to the optimum. So what happens is that your distribution of markers is quite dense around the optimum. Which doesn't leave a lot of space for varying marker size (and thus the possibility to distinguish between the individual markers). Which makes me always opt for varying color (however a porkchop is superior of course). Hopefully that clears the "high density" part up a bit. Thanks for the reply!

Idk if you were just making a joke or if you actually want to see the presentation. But if so, it's in the description.

The visual display of quantitative information - second edition The grammar of graphics - second edition Visualisation analysis and design

Apologies: photos.app.goo.gl/brmCVQYgFked85kx8

@@Computerphile Thanks a lot

@@AF-lt2fr Thank you

@@sanketdutta4981 no problem - I ended up getting it by changing the video quality under advanced to 4k and zooming in.

I also would like to have the slides.

It's in the description.

I think pie charts utilize a mix of area and length. The example in the video with squares compares 2 geometrically similar squares with different areas. The pie chart's pie's are not geometrically similar with changes in area but instead the arclengths change (since radius is constant to the bounds of the entire pie). I'm assuming this study comparing areas used uniform scale. We could change the visualization to compare a square and scaled square along one dimension (rectangle) because now you can compare side lengths (linear term), or display the numerical values inside the marker.

No, people's perceptions of triangular area or sectors (pie slices) are unreliable especially those undergoing rotation rely on their ability to estimate angular displacement. If instead of percentage of totals they display raw count data, say 9,720 votes, 9,000 votes, 8,280 votes, and 7,200 votes, and 1,800 votes, you would have trouble recognizing that these are 27%, 25%, 23%, 20%, and 5% respectively from a total of 36,000 votes. A linear graph with 4 closely but separated marks at each raw vote count and another very near 0 would show the differences of 720 votes between the first three and 1,080 votes to the fourth and the very wide gap to the last 5%. You may even notice that the gap between the 3rd & 4th values is wider than those between 1st & 2nd and 2nd & 3rd which are of equal width.

Yes as someone in a field where visualization of data is very important (biology) i have been told to always steer clear of pie charts. Honestly i’ve never seen someone use one in a talk, and i think there would be snickers from the crowd if they did.

Yep, but can you visualize it?

That's obviously how he got his funding 😆

At 10:00? It's your screen doing something funny. Here it is precisely 4.5 vs 9.0 cm.

and how is the raw data formatted?

Nah. It's only you.