#Data Analysis

[entropificationer]

step 1: develop hyperfixation on coding with R

step 2: ??!!??!? (for three weeks straight)

step 3: fully interactive LCARS inspired website that does CGM analysis for the minuscule audience of diabetic star trek nerds

[victusinveritas]

It's almost like something happened in 2020-now that is causing these problems.

[savagechickens]

Bad Numbers.

[hellyrossum]

it's so fucked actually that lumon repurposed the security room into the family visitation center. not just because it supports the illusion that lumon isn't watching as closely, or because the innies now have no clue where and how to reactive the overtime contingency if they ever needed to,

but because it's overwriting the room where dylan performed his (physically demanding!) act of rebellion, a sacrifice he agreed to out of deep (platonic) love for his friends, with a room where he's rewarded for being a Good (Obedient) Worker with getting to know his outie's family. with each visit he'll make, burying in his mind the place that represents the connections of solidarity he found down here as an innie underneath reminders of the connections that "actually" matter, the reasons he's here in the first place — the connections his outie has. the people (including three kids!) who directly rely on him keeping this job.

lumon isn't just separating dylan from the rest of the team by giving him a perk that the others can't have (because they don't have a family, huh. looks at devon) and encouraging him to keep it secret "for his own sake". they're trying to drill into him that his loyalty has been misplaced. that he's been selfish. that he has to choose one group of people or the other; choose which version of himself gets to be loved.

and innies aren't made to be loved.

[scipunk]

Armitage III: Dual Matrix (2001)

[thatdisasterauthor]

Data analysis makes brain go brrrrr.

[momoguido]

Presenting the results of my latest hyperfixation

Tam Lin is a Scottish folktale, usually presented in ballad form, dating back to at least the 16th Century. It has been summarized and re-told by different people over the years, including Overly Sarcastic Productions. It is the subject of a one-act opera, and was the inspiration for a 1970 movie that updated the story to the present day.

The fairy queen yells at Tam Lin in every version analyzed except for the Anais Mitchell version. Her version is an outlier in many respects, since the entire parade is left out, Janet simply grabs hold of Tam Lin mid-conversation.

Another oddity is Pentangle's version, which was written for the movie and consists of a jumble of images meant to complement scenes from the film, without a coherent narrative.

The biggest surprise to me is how few versions mention Janet seeking an abortion, something that I had always thought of as a central part of the story.

One thing I wish I could have looked at in more depth is the balance between versions that describe Tam Lin's transformations before he goes through them, and versions that describe them as they happen. As they happen is more common, but some songs will do the entire sequence twice.

Going into this, I had two versions prominent in my mind: Anais Mitchell's pop-folk version that leaves out much of the story, and Anne Briggs' very traditional (and very long) version. Because of this, I thought perhaps we would see a decrease in the complexity of the story over the years, but that's not the case, it's basically flat. I think a better comparison might be to separate into traditional and revised versions, although that is something of a judgment call.

There were so many minor discrepancies I couldn't even keep track of them all, but these four stood out because they are so oddly specific. Even the main character's name and the setting aren't consistent across versions, if they're even mentioned at all. Some versions don't even give the main character a name!

My data collection process leaves a lot to be desired, as well as the way in which I decided which events were major or minor, and which things to leave out altogether. This was mainly just a fun way to explore different versions of my favorite folktale, listen to a lot of different people sing different versions of the ballad, and rotate the whole thing in my brain.

[phanchester]

since there are so many data nerd phannies i decided to make a compilation of all the spreadsheets i could find - lmk if i missed any or if you want me to add any additional details <3

last updated: 30/06/24

actively updating spreadsheets

dan and phil uploads from 2021-2024

dan and phil’s upload schedule from all their channels with days and dates

amount of days in between videos in each channel

pie charts of days of the week they upload

made by @ahappydnp 

everything dan and phil related

all of dan and phil’s video links from all their channels from all their accounts (including super amazing project, snapchat, vine, tiktok and more)

all of dan and phil’s radio shows, including reuploads and playlists, as well as the dan vs phil, fan war and internet news if available for each show with misc clips and written recaps

all of dan and phil’s liveshows, including some written recaps and the app where it was originally posted

all of dan and phil’s vyous including the question they were answering

all of dan and phil’s collaborations and video features (even if they were in the background), including the channel they were originally uploaded on 

all of dan and phil’s interviews

all of dan and phil’s merch, including originally shop links and links to the phandom wiki which has further information

all of dan and phil’s professional photos as well as some fan photos, including the event, photographer and platform

the dates and statuses of each of these videos (lost, archived, unlisted or public)

made by @stillarchivingdnp

dan and phil 2024 upload stats

each of their 2024 videos with channel, upload date, upload time in uk, length, sponsor and editor/s (if applicable) with an accompanying colour-coded calendar

(for amazingphil videos) whether dan featured and (for dapg videos) whether it was gaming/talking and who tweeted it

interactive part where you can see the time period between two videos

averages, maximums and minimums for times between uploads, upload times and runtimes with accompanying graphs

percentage of videos with other editors, with pie charts for all channels and each channel

made by @dnpbeats

all or nothing: dan vs phil season 2

all of the games for season 2, with the year they played them and the results with and without all or nothing coming into play

how often all or nothing came into play and who suggested it

the general impact of all or nothing

made by @organized-chaotic-disaster

dan and phil saying “i love you”

when dan and/or phil said ily

the video and timestamp from when they said ily and whether it was prompted

pie chart of dan or phil saying ily

made by @ahappydnp

games where one of them decides the winner

date and link for each video

overall winner with the winner for each round

breakdown of the amount of times each of them have won each round and the percentage phil has won

made by @dnpbeats

dan and phil 2024 upload schedule

upload date for each video, with the day of the week and approximate time it was uploaded in cst, including the most common and second most common upload day for dapg

days between each upload, including the longest gap, shortest gap, average gap and first and second most common gap for dapg

a colour-coded calendar displaying the upload schedule for dapg and amazingphil

made by @kat-aa

completed spreadsheets

all or nothing: dan vs phil season 1 with a great accompanying document with further details and analysis of the data

all of the games they played, with the year they played them and the results with and without all or nothing coming into play

how often all or nothing came into play and who suggested it

the general impact of all or nothing

made by @organized-chaotic-disaster 

youtuber tours

(not necessarily dnp but it includes them!)

120 different tours, including the creators, names, dates, countries, links (if available) and producers (if applicable)

each tours’ venue capacity range, average and total attendance

individual tour show breakdown with city, state, country and additional notes

data on each venue’s capacity, number of tours, and which youtuber went to each venue

data on each country’s amount of shows, broken down into states and cities

made by @stillarchivingdnp 

gamingmas 2023 schedule

all gamingmas video titles from 2023

the time each video was uploaded in gmt

made by @cactuslester

spreadsheet screenshots in posts

listening trends in all or nothing

scatter graph for the correlation between track number and number of listens

analysis of the data

made by @serendipnpipity

analysis of dnp’s letterboxd ratings and movies with part 1 and part 2

(pt 1) rating distributions for all the movies they’ve rated, including details about which movies one rated higher than the other, and which movies they rated the same

(pt 1) a list of their five-star movies

(pt 1) a list of movies one logged but not the other

(pt 1) cute little misc notes about the specific movies and dates

(pt 2) ratings broken down into genre, studio and franchise with accompanying bar charts

made by @philsrosesweatshirt

views on post-hiatus dapg videos after specific time frames

i believe this is a work of progress!

video titles with the dates and months, along with details of whether they were sponsored or had external editors

view count after 24 hours, 48 hours, 1 week, 2 weeks, 3 months and 6 months

made by @goldenpinof

favourite dnp tour song statistics

years phannies started watching vs the year they joined the phandom represented in a bar graph

favourite dnp tour song in a donut pie graph and a bar graph

favourite song vs year joined represented in a bar graph

made by @serendipnpipity

terrible influence: the tour trailer video analysis

all the videos that appear in terrible influence with additional notes

the list sorted by date, view count and channel specifics

timestamps provided for each clip, both in the video and where they appear in the trailer

made by @emojackolantern

[alwaysbewoke]

here is the sheet. it's an excel spreadsheet so it will download to your computer. this is really important for all the people who constantly say "democrats and republicans are the same." if they're not willing to do the work to either prove or disprove that notion, they're NOT someone ANYONE should be taking political advice from.

[sillylotrpolls]

About Reblog Graphs

Have you ever clicked on the "reblog graph" button of a post? I think they're one of the... well, maybe not greatest features on tumblr ever (polls are probably better), but they're still pretty darn neat.

I want to show some cool patterns I noticed on some recent posts of mine, but first I'll explain how reblog graphs work so you can more easily follow along.

This is pretty long with a bunch of pictures, so click the cut to read more.

How reblog graphs work

If you've never done so before, I invite you to click the notes button on this or any other post and then the icon with four circles. You will then see a bunch of dots connected by lines.

For example, if you click the graph for the "blorbo in Elvish" post, you get something like this:

Now, let's zoom in a bit. You can do this by using the mouse wheel and clicking and dragging around the graph until it's showing what you want. (I don't know how it works on mobile, but presumably it's similar to using Google maps?)

This next screenshot is the bit in the lower right of the graph shown above. However, the graph may not always display in the same way because reblog graphs are re-generated each time you click the "reblog graph" button.

Here you can see that I'm viewing the root post, which is the original post made by me. It's indicated by a circle with a dot inside. You can also see that six people reblogged that post. Each reblog shows as a dot with a line connecting it to the post it was reblogged from.

Now here's a cool thing about reblog graphs: they're interactive! You can click on any dot and see the post it represents and the reblog chain that led to it.

For example, clicking this dot that has several lines emanating out from it shows that it is "2 reblogs deep" and was posted by @cycas.

Got it? Close enough? Cool, now let me show you some neat things I noticed. :D

The Swedish Chef poll and very popular bloggers

My polls tend to average between 500 and 2000 votes, depending on subject matter. The Swedish Chef poll, however, took off and eventually garnered over 22,000 votes. How did that happen? A very popular blog reblogged it about five days in.

Initially, the graph looked like this. (This is the first 200 reblogs.) There's nothing unusual here. You can see that the root post had several reblogs, and that there's another cluster developing around a post by @zagreus. There are also several reblog chains where just one person reblogs someone else's reblog. Some of these chains peter out, while others find their way onto the dash of more popular bloggers, creating clusters.

A quick note about "popularity"

Yes, yes, it's all about "popular." However, it's not just about having a bunch of followers. What's more important is that the "popular" person reblogging your post has followers who are specifically interested in your post.

For instance, if I, @sillylotrpolls, make a poll about LazyTown, it's probably not going to get very many reblogs because my followers aren't here for that. However, if @silly-lazytown-polls reblogs the poll, that reblog might then get quite a lot of reblogs itself. It's not that silly-lazytown-polls has more followers than sillylotrpolls, it's that it has more followers specifically interested in LazyTown content. Make sense?

Back to the Swedish Chef poll

The poll eventually got over 5,000 reblogs. Since you can only add 200 reblogs to the graph at a time, you can roughly see how a post spread over time.

With 600 reblogs loaded, a new cluster bursts onto the scene. This is @bunjywunjy, who reblogged the post from @beecreeper who reblogged it from @soggypotatoes who reblogged the original.

Bunjywunjy didn't add any tags or comments, so I didn't even notice at first because it didn't show in my activity feed. However, I did notice a sudden uptick in notes on the post, which caused me to investigate. It had been five days since I posted the poll, and usually polls that are going to take off do so sooner than that.

By continuing to click the "load more reblogs" button I can see how the post further spread, especially from bunjywunjy's post.

When the post reached @beggars-opera (whose icon I am somewhat proud to announce I identified on sight), they added a screenshot of @stylishanachronism 's tags which said:

# all of these are incorrect it's the 'meat's back on the menu boys!' scene

This would become the dominant version of the post as it further spread. Interestingly, this was the only reblog of stylishanachronism's reblog. Literally thousands of people loved their tags and agreed with them, but they quite plausibly never saw it unless they specifically went looking.

By 3,200 reblogs, you can see even bigger clusters developing. @thebibliosphere shows up 10 reblogs deep, and leads to yet another cluster via @teaboot (12 reblogs deep).

Eventually, with all 5,371 reblogs loaded, the reblog graph looks like this:

Like I said: neat. :D

Cool, but if you've seen one, you've seen them all - right?

So what prompted this (extremely long) post was actually the reblog graph for my poll on inspirational LotR quotes.

Here's the reblog graph with 200 reblogs loaded:

And here's the graph with all 1,890 reblogs loaded:

It's just one big cluster around the root post. I've never seen that before!! Almost everyone reblogging this post saw it either because they follow this blog, saw it in the #lotr tags, or because their non-influencer friend reblogged it. (Or maybe it was in some kind of algorithm/the explore feed, but I have nearly zero experience with those.)

And this wasn't just a small post. This poll got over 15,000 votes and more than 4,000 notes. That puts it in the top 10 polls for this blog.

What does it mean? I have no idea. I would really like to know! But really, I got nothing. If you have a theory for why this particular poll should result in a reblog graph like this, I would very much like to hear it.

Orphan clusters

To round things off, I'd like to show another interesting facet of reblog graphs: orphan clusters.

This blog's current undisputed poll champion is the fmk wheel poll. That's not really a surprise, as it combined sex with a fun game where you just had to tell everyone what you got, which meant either a reply or a reblog. So it spread pretty far.

However, if you look at the graph, there's something odd going on.

This is with just 200 reblogs loaded:

Notice how some of the dots don't connect to the root post? That's because somewhere along the chain, a reblog was deleted.

This cluster in the bottom left got pretty big! This screenshot is at 800 reblogs loaded. The missing link is from a blog called @gendertaliban that doesn't exist anymore, as near as I can tell. That makes it impossible to trace the full path of any of these reblogs.

In conclusion

This concludes today's deep dive into a tumblr feature you probably never paid any attention to. Admittedly, there's not a huge use for it outside of determining which of your mutuals is an "influencer," and they get quite difficult to navigate after loading about 1000 reblogs, but I hope you enjoyed staring at dots and lines with me. :)

[gnot-art]

=SUBSTITUTE(myFursona,"fox","lion") :: full body (midi) (2025)

He's broken into my spreadsheet

[shumw4y]

"The myth of cosmopolitan cities: why large urban areas are more segregated..."

[victusinveritas]

[sheets-of-your-own]

the spreadsheet really doesn't lie and it's outing me lol

I was looking at the graphs, and I noticed that the top romantic ship was merthur, at 75 fics. the second ship is at 44

"Oh" i thought to myself, innocently, "the number must be padded by a lot of oneshots, that's too big of a jump to be actually correct. My Merlin phase wasn't that bad"

So I did the obvious thing, and added the ship wordcount to the graph, so that the count can't be padded with oneshots and it actually shows that time read.

...

Okay so maybe it was that bad lol

[hellyrossum]

you know in all the posting about irving and helly’s friendship after last ep, i haven’t seen anyone mention yet that he was one of the two people who first saw her, who watched her (through the screen, on the other side of the wall) as she woke up for the first time, confused and scared and burning with anger. and then he was the one who brought her back; he was the one she first saw (holding her in his arms), as she was confused and scared and freezing. he was there for her start, and she was there again just in time for his end.