I'm trying to figure out how (in the long run, this is a small dataset so far) i can visualize which Gametype, bettype, etc is the most successful for me to to be betting on.
It would be easier to visualize if you look at only one or two variables at a time. For single numerical predictors (spread, date, and bet, I guess?), I would use a simple scatter plot and regression line. For single categorical predictors, I would use bar graphs.
For a continuous x continuous interation, you could use a heatmap with each axis being one predictor variable, or a 3d plot in the same vein. For categorical x categorical, I would use clustered bars or an interaction plot. For continuous x categorical, I would use a scatter plot with a trend line for each category.
Three way interactions get very tricky to plot, and I don't have any good suggestions there.
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Very interesting, tells so many stories.
But what’s up with the Coolidge annotation? At least the other annotations are relevant to presidents’ life spans
Kennedy had the shortest lifespan of all the presidents, guess that's what happens when you're younger than most presidents and died while in office.
Could you please add the ones that didn’t win a majority of the states. That’s equally as interesting (and as equally irrelevant) as the ones that didn’t get a majority of the popular vote.
Source: Wikipedia page on US Presidents. I then put the data into Excel and did a lot of tedious formatting. An easier source of the data is this Wikipedia page.
I did the visualization in R.
Also as a note, I did this visualization by hand a few years ago. However, recently there have been a string of similar visualizations that spurred me to get my act together and finish this in R. You may remember this one on the terms of the Presidents. More recently, there was this one on the lifespan of Presidents, and this other one.
edit: "had" to "hand"
Thank you for your Original Content, /u/Crocuz! I've added your flair as gratitude. Here is some important information about this post:
Data collected manually by Umeå Univerity and visualized by me in MATLAB.
Thank you for your Original Content, /u/Gumeo! I've added your flair as gratitude. Here is some important information about this post:
I created this visualization using R base graphics and ImageMagick to assemble it into a gif. I am documenting my process of building a neural network from a functional programming perspective, which you can read about here. There will be another post soon about exactly how these are generated.
The gif shows a 10 by 10 grid of patches/templates that the network learns to recognize handwritten digits. It is initialized randomly, but the network starts to learn shapes that resemble individual digits and strokes needed for drawing them. These templates are matched against incoming digits and the network uses the information of how good these matches are to predict which digits was in the input. In the learning process the network weights are refined to become better at recognizing handwritten digits.
Thank you for your Original Content, /u/Barefootdan! I've added your flair as gratitude. Here is some important information about this post:
The graphic is really hard to read. Others have suggested some good changes. Make it easier to see.Otherwise, great work.
Wow, this is fascinating. I would've thought at the very least that the U.S. was in the top 10. Thanks for sharing!
Sources collected from various sites such as NatGeo and Wiki, newspaper articles and news. (Source)Created using Illustrator by my girlfriend in her second year of Graphic Design (Tool)
Really great concept here!
One thing I'd like to note: plastic does not "decompose" it continues to break down into smaller and smaller pieces until it is quite literally microscopic and then basically becomes a poisonous part of the ecosystem with organisms (including humans) breathing and ingesting it.
(Sorry to go on a short, morbid rant there, I am extremely passionate about this topic!)
It just kind of gets me when the word "decompose" is used in relation to something that will never "go back to the Earth". Makes it sound much too innocent and like it is not changing the oceans as we know them literally forever. Especially things like cigarette butts that seemingly "decompose" in just a few years.EDIT: I'm sorry I don't actually have a good alternative! I can't think of a good short way to say what I said above.
On a more positive note! LOVE seeing awareness of this issue!!!
First off, cool graphic and awesome comments so far. I'm having a trouble with a piece of the graphic though...
I'm colorblind [Strong Protan] and have no idea how to read the 1/5/10 thing on the top right/on the map.
I see three colors in the top right. But I can't match them to the map. I tried matching on the size of the circles but when I used a color picker it showed that those two were different. Any chance of getting a number/symbol on the map as well that I could use to match against?
What's the difference between the water bottle on the left and the water bottle on the right? What's the second thing down on the left column? Labels would be nice.
I thought the cigarette was a tampon actually.
Am I right in assuming that output from African countries are caused by European and North American companies? Maybe China as well?
Not surprised that the Asian Pacific is a major pollutor. I've seen people take wrappings off their goods and put it straight into the water while traveling on boat. They have no concept of what western trash is or what it does to the environment
Is this where the plastic is made or where it is disposed of? That really makes a difference in my eyes.
One of the reasons I smoke hand rolled smokes with biodegradable filters and papers.
I care more about the environment than I do my own health..
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I was trying to come up with a way to visualize how minutes get distributed among top NBA players and the bench, and I decided to look at how cumulative minutes get racked up as you go through a team's most-played guys.
I ranked each team's players by the % of their team's total minutes they play, then plotted this as cumulative minutes vs. rank. Basically, as you go through your most-played guys, how quickly do you close in on 100% of your team's total minutes?
Some teams, like the Wolves, rely heavily on their top 5 and first guys off the bench, with their first 6 or 7 guys racking up most of their minutes, and only 13 guys even getting minutes at all. Others, like Brooklyn, rely less on their top guys and have more bench guys getting higher percentages of total minutes. Golden State has a deep bench, with lots of guys beyond their starters getting real minutes; this looks like a "fat tail" rather than a long, flat plateau at the top of the plot.
The differences, especially looking at top 5, are pretty big across the extremes. Brooklyn's top 5 gets ~45% of their total minutes, while the Wolves are pushing 70%. Looking at a team like Milwaukee, they play their starters a lot, but get deeper contributions from more bench guys, whereas the Pacers bench gets next to nothing.
All stats come from Basketball Reference, and my R code to generate the plots is here.
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Data was recording from Last.fm and I used the website http://savas.ca/lastwave/ to create the stream graph of my previous year of music streaming.
edit: I used /u/Taurheim 's application with my own dataset from Last.fm.
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This is a unique way to represent data, and an unconventional visualization tool... but I think we'll allow it as a "heatmap".
Source: Me, I wrote it.
Tool: MS Word, comparing oldest and newest version of the text.
Awesome out of the box viz! Would love to see a more quantitative viz of the comparison
Feedback is much appreciated!!
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