Dataisbeautiful

Thank you for your Original Content, /u/houndrunner!
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What’s the point of displaying all of the rainfall vertically on the right side if I can’t correlate them to the geographical location? I don’t see any reason for the vertical ordering.

I think this graph would be much more useful if the dots started out as light blue and the colour changed to darker corresponding with water level. The shape of the dots should change to a square if above flood levels, and (as is already the case) can remain as a solid X if data is cut off. It's nice data but very complicated to follow.

Really cool idea, wish it was slower and maybe had numbered points to help match the gauges to their points

We've updated an earlier post with rainfall totals and river levels that are up to date as of 4am ET today (9/19).

This GIF was created in R using code up on github and all parts of it are available for re-use.

Methods: We used R to get data from the National Weather Service (flood stage thresholds and river predictions), NOAA for hourly precipitation and Hurricane track, U.S. Geological Survey for river gage locations and gage height data, and the maps/mapdata packages for spatial data.

We built the GIF in R with dataRetrieval, maps, ncdf4, geoknife, xml2, jsonlite, dplyr, tydr, sf. See the code

This is brilliant. I really like the collected ordering on the right for easy visual comparison, though as someone else has pointed out that does make it tricky to link the visual back to the map. If this were interactive, and you could click on a point on the map and it would hiighlight the corresponding gauge (also, isn't it spelled "gauge" not "gage"?), it would be perfect.

It looks beautiful but it really is not showing something useful. Why don't you colour-code the points? Show the intensity level right on the points?

Even after seeing your explanation this seems absolutely pointless other to make something "pretty". However, It's neither useful nor interesting.

I don't even understand what happens after the main part ends, what is the text and the little orange and blue dots? Your gif only shows them for a couple of seconds, and in that time it's difficult to read the text, look at the plot, read the legend (which doesn't have the little orange dots, i.e. it doesn't change for this "second" plot), and understand what is going on. I would either lengthen the last part, or just remove it altogether, because as is, it's not adding any value to your visualization.

I haven't seen Florence being covered much in the news. Can someone catch me up? Do we still have an East coast?

I think this is brilliant.

Showing which gauge correlates to which point at the start and backed up with the size and colour of the point was enough for me to follow and understand.

I would love to utilise something similar in agricultural yields!

You have just inspired me to learn R! Any suggestions or online guides you would recommend?

Day 24 - I went hardcore and removed all my distractions.

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Average Hours (Regular) = 6.5 Hours

Average Hours (Hardcore) = 9.98 Hours

Thank you for your Original Content, /u/typhoidisbad!
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• I've kept location history enabled on my phone for years, so I can use Google Takeout to export all the metadata. Using half a million data points over the past 2-3 years, I can figure out if I am in my office (closer than X meters to specific latitude,longitude) at a given point in time. The granularity of the data is great.

• I'm a grad student, and my work hours are not at all structured, which is why I wanted to plot this.

• I used Google Takeout, pandas, and matplotlib.

Nice viz. And thanks to this I learned about Google Takeout. Oh man, the data trove!

What's up with your Thursdays, though? You clearly were not in the office as much as other weekdays.

Thank you for your Original Content, /u/__compactsupport__!
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My gym tweets out how many people are in the weight room over the day. I scraped the data over the last 4 years and made this plot for the first semester of school. Plots made with ggplot2. Find the data here.

A couple things to note:

• The best time to go is always in the morning, but the worst time to go changes as the semester progresses. Between 4 and 7 sees the most activity in September (with numbers declining after 7), but October and November stay busy from 5 to 10.

• The effect of October midterms is clearly visible.

• In September, there is a "Friday night pump", where students git the gym quickly before getting ready to go out. Friday sees a faster decline in usage after 6 pm than other weekdays.

EDIT:

Same plot in viridis as well as the entire year

It would be awesome to see the whole year. I expect a smaller raise in January and a maximum low in july-august.

This is really cool! Also, wow, that is one popular gym.

How much did this take you to finish out of curiosity? How hard is it to use ggplot2?

Thank you for your Original Content, /u/BetaDecay121!
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Created using the Ordnance Survey Open Roads shapefile and QGIS, this is a map of the roads of Great Britain coloured according to the first number in the road's name.

For example, the A5867 will be coloured according to the number 5, and the M1 will be coloured according to the number 1.

The random blue roads on Shetland are due to an error with QGIS' rendering of the image. I have not been able to reproduce it.

Key:

1. Easternmost blue
2. Pink
3. Southernmost green
4. Brown/beige
5. Westernmost (light) blue
6. Yellow
7. Purple
8. Red
9. Northernmost green

If it's not too much hassle for you, would you be able to link a high-resolution version with a true black background? I'd love to use it as wallpaper on my phone. Don't worry if not, probably use it anyway!

It is amazing to see that our road system makes so much sense when put under the microscope. I feel some pride.

Whats the little bit of purple/blue at the very top? i'm looking at a map of GB and it looks like they all begin with 9 in that region which i assume is the green color.

This is really cool. As an American, I had no idea GB numbered their roads this way. I imagine a similar visualization of the American road system would be awful because outside of the interstate system, numbering is pretty much up to each city/state.

The boundaries between these regions in England pretty much match the routes of the A1, A2, A3, A3, A5 and A6 in to London.

Awesome map. Can we get the key to the colours?

I love that Anglesey is cut in half. You can see that conversation:

"Now look you, why can't we all be 4s like most of the rest of Wales?"

"I think you'll find that the majority of the island is north or rather clockwise of the A5 and as such is correctly designated as roads commencing with 5. We can't just go ignoring the manual for streets to make thing convenient you know. Uh-huh-huh-huh. Can you imagine the chaos?!"

What’s the blue tentacle reaching down to Exeter? And why is it named as so?

EDIT: It’s the M5 lol

I work in traffic gathering operations and have to search for roads on maps all over the world. Few places have such widespread organization and it makes my job way easier. Thanks England!

It would be even easier if every third Street wasn't called church St though

How to navigate without maps in the UK. Realise that the A19 is the ninth road from the A1, and the A192 is the second road from the A19. It's all really logical if you watch the pattern in the numbers around you.

I have lived in this country all 22 years of my life, I have been on the roads since I was 17 and passed my driving test at the beginning of the year, but only just now on this post have I realised this numbering system was the case.

God damn it.

Aha, a map where Skye is attached to the mainland. Very interesting to look at the Highlands on the map.

The odd orange line the stretches into southern Essex is the A414, presumably because it ‘starts’ West of the A1

Its more England, Scotland and Wales than Great Britain because youve included all the islands and island groups. A lot of people get tripped up on that.

And heres the Shetland Islands performing the famous Great Britain highboard dive taking a starting position at John O'Groats

Before I read the caption I thought this was a rendering of some protein haha anywho...thank you for posting this it is really interesting

Thank you for your Original Content, /u/carnewbie911!
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I made it myself using Google sheets, tallying all my receipts and collected my own data

Thank you for your Original Content, /u/hobofighter!
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Plotted using Excel.

In the spring, Metacritic ran a survey where they asked site readers to predict the Metascore of 30 movies due to release this summer. The average of all respondents was saved and compared to the movies. I compared the actual Metacritic score to the predict score over the course of the summer, against each other and against time.

Three movies had release dates pushed out into 2019 and were not included in the figures.

On average, respondents over predicted movie scores by 3.1 points.

However, for the first 58 days after the article published, released movies were within 10 points of the predicted score, suggesting that the media available for movies within 2 months of their release is likely enough to be able to judge how good it will be.

Most disappointing movie of the summer: The Happytime Murders, with 40 points less than predicted (114 days after the article was published)

Most surprising movies: Mamma Mia and Mission Impossible Fallout, with 15 points more than predicted (79 and 86 days after article was published)

Original Source: https://www.metacritic.com/feature/metascore-predictions-summer-movies-2018

Additional data and visualization: https://imgur.com/yaWzuGd

Thank you for your Original Content, /u/iEslam!
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Wow I didnt realise there was some hurricanes at the beginning of the century that made it all the way across America and Canada to the northern side of Quebec... those must have been some power houses of a storm sell to go that far on land.

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It took me much longer than I'd like to admit to realize the darker area was the ocean and not some sort of disfigured land area.

This just goes to show that no one should live anywhere other than Western Hudson's Bay, which is apparently immune from hurricanes

Pretty cool. This prompted me to go look up that crazy hurricane form 1923 that formed in the pacific, ran through Mexico, back to the gulf, and finally hitting Louisiana. Crazy.

The sixth storm of 1923 was the first recorded example of a storm crossing from the Eastern Pacific basin into the Atlantic basin. It formed in the deep tropical latitudes south of Guatemala in mid-October. The storm struck the Mexican state of Oaxaca on October 13, crossing the Isthmus of Tehuantepec into the Bay of Campeche. It strengthened into a hurricane while continuing northward across the Gulf of Mexico, making landfall along Atchafalaya Bay, Louisiana as a Category 1. Damage was restricted to harbor areas, including sunken barges or barges driven ashore. There were reports of downed trees and telegraph poles.

You should have added opacity to points that you are drawing, so that shade would also carry some information with regard to frequency

Original Animation by /user/Tjukanov - Edited with Adobe Premiere - added a vector of Georgia with a 'darken' blend mode.

Is there one of these for Florida? It'd be interesting to see how quickly that state gets colored in when it's isolated like this.

Why do the tracks disappear occasionally? I noticed multiple cases of a track making a line across the state, then a few frames later some pieces of those lines disappeared or moved.

And that little blip without a hurricane path is Cobb County. No extreme weather ever hits Cobb County because we made a deal with Satan a Dee years back that keeps us safe and gets us a new ball park.

Intensity matters far more than track. Systems that have been down graded to tropical storm strength are still included in the tracks.

I thought that one little part in the upper state was the best place to be, then BAM a big old red line went across it.

Now do one for Florida so we can see how bright red a state can get.

Every state freaks out about hurricanes... and then there's south Florida.

Theres a single black spot at the northern-ish part of GA that has not been touched by paths.

What city is in that area?

The fact that you limited the animation to only the political boundaries of Georgia makes it profoundly useless.

Dude. I work on Friday. In Savannah. Already worried about Florence. This is not what I needed to see.

Thank you for your Original Content, /u/Grandpa_Willy!
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Hovering over a node shows transaction details and clicking will bring you to the blockchain explorer entry for the wallet address. I thought this was a really cool way to visualize transactions...more to come as it's a WIP! If you look closely you can see an airdrop taking place, as well as more popular addresses being sent to/from.

For the extra-interested, here is a larger sample I rendered of around 500 recent blocks. As you can see with the exception of some obvious (and interesting!) airdrops being distributed the transactions look very decentralized. This does not imply that they're totally disconnected as it's only a small snapshot - still cool!

https://i.imgur.com/CbLcmcJ.png

I am writing this using Node and the d3 framework to render in the browser.

Thank you for your Original Content, /u/Amaracs!
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To create this project I used the reddit python api called PRAW, for data visualization I used plotly, also for debugging I inserted the collected data into a PostgreSQL database by using psycopg2 API.

Basically the script just iterates over submissions from all time top and looking for OPs approval comment in the second level comments then it takes its top level comment and subtracts the submission creation time which can be seen on the graph.

To get reliable results I only worked with the submissions that has a solved flair, and where OP wrote solved for a top level comment.

​

Unfortunatelly with PRAW I could only iterate over 1000 submissions. Out of it 751 posts have solved flair, 68 posts have likely solved and the rest have not specific flair.

Out of the 751 solved solutions 360 was marked "Solved" by OP which can be seen in the graph.

​

The average solution time is 74.96578703703705 min.

​

Data: https://docs.google.com/spreadsheets/d/1aiPB6YY_cB27Qlvx59lIy5gNUuMTxaAJGKDlgzGxYQ4/edit#gid=38137914

Interactive Graph: https://plot.ly/~amaracs/1#plot

Code: https://github.com/Amaracs/RedditApi_SpeedOfWhatIsThisThing