## Pentagrammarspin: why twelve pentagons?

This post has been in my drafts folder for a while. With the World Cup here, it’s time to post it!

It’s a rule that a 3D assembly of hexagons must have at least twelve pentagons in order to be a closed polyhedral shape. This post takes a look at why this is true.

First, some examples from nature. The stinkhorn fungus Clathrus ruber, has a largely hexagonal layout, with pentagons inserted. The core of HIV has to contain twelve pentagons (shown in red, in this image from the Briggs group) amongst many hexagonal units. My personal favourite, the clathrin cage, can assemble into many buckminsterfullerene-like shapes, but all must contain at least twelve pentagons with a variable number of hexagons.

The case of clathrin is particularly interesting because clathrin triskelia can assemble into a flat hexagonal lattice on membranes. If clathrin is going to coat a vesicle, that means 12 pentagons need to be introduced. So there needs to be quite a bit of rearrangement in order to do this.

You can see the same rule in everyday objects. The best example is a football, or soccer ball, if you are reading in the USA.

The classic design of football has precisely twelve pentagons and twenty hexagonal panels. The roadsign for football stadia here in the UK shows a weirdly distorted hexagonal array that has no pentagons. 22,543 people signed a petition to pressurise the authorities to change it, but the Government responded that it was too costly to correct this geometrical error.

So why do all of these assemblies have 12 pentagons?

In the classic text “On Growth and Form” by D’Arcy Wentworth Thompson, polyhedral forms in nature are explored in some detail. In the wonderfully titled On Concretions, Specules etc. section, the author notes polyhedral forms in natural objects.

One example is Dorataspis, shown left. The layout is identical to the D6 hexagonal barrel assembly of a clathrin cage shown above. There is a belt of six hexagons, one at the top, one at the bottom (eight total) and twelve pentagons between the hexagons. In the book, there is an explanation of the maths behind why there must be twelve pentagons in such assemblies, but it’s obfuscated in bizarre footnotes in latin. I’ll attempt to explain it below.

To shed some light on this we need the help of Euler’s formulae. The surface of a polyhedron in 3D is composed of faces, edges and vertices. If we think back to the football the faces are the pentagons and hexgonal panels, the edges are the stitching where two panels meet and the vertices are where three edges come together. We can denote faces, edges and vertices as f, e and v, respectively. These are 2D, 1D and zero-dimensional objects, respectively. Euler’s formula which is true for all polyhedra is:

$$f – e + v = 2$$

If you think about a cube, it has six faces. It has 12 edges and 8 vertices. So, 6 – 12 + 8 = 2. We can also check out a the football above. This has 32 faces (twelve pentagons, twenty hexagons), 90 edges and sixty vertices. 32 – 90 + 60 = 2. Feel free to check it with other polyhedra!

Euler found a second formula which is true for polyhedra where three edges come together at a vertex.

$$\sum (6-n)f_{n} = 12$$

in this formula, $$f_{n}$$ means number of n-gons.

So let’s say we have dodecahedron, which is a polyhedron made of 12 pentagons. So $$n$$ = 5 and $$f_{n}$$ = 12, and you can see that $$(6-5)12 = 12$$.

Let’s take a more complicated object, like the football. Now we have:

$$((6-6)20) + ((6-5)12) = 12$$

You can now see why the twelve pentagons are needed. Because 6-6 = 0, we can add as many hexagons as we like, this will add nothing to the left hand side. As long as the twelve pentagons are there, we will have a polyhedron. Without them we don’t. This is the answer to why there must be twelve pentagons in a closed polyhedral assembly.

So how did Euler get to the second equation? You might have spotted this yourself for the f, e, v values for the football. Did you notice that the ratio of edges to vertices is 3:2? This is because each edge has two vertices at either end (it is a 1D object) and remember we are dealing with polyhedra with three edges at each vertex. so $$v = \frac{2}{3}e$$. Also, each edge is at the boundary of two polygons. So $$e = \frac{1}{2}\sum n f_{n}$$. You can check that with the values for the cube or football above. We know that $$f = \sum f_{n}$$, this just means that the number of faces is the sum of all the faces of all n-gons. This means that:

$$f – e + v = 2$$

Can be turned into

$$f – (1/3)e = \sum n f_{n} – \frac{1}{6}\sum n f_{n} = 2$$

Let’s multiply by 6 to get, oh yes

$$\sum (6-n)f_{n} = 12$$

There are some topics for further exploration here:

• You can add 0, 2 or 10000 hexagons to 12 pentagons to make a polyhedron, but can you add just one?
• What happens when you add a few heptagons into the array?

Image credits (free-to-use/wiki or):

Clathrus ruber – tineye search didn’t find source.

HIV cores – Briggs Group

Exploded football – Quora

The post title comes from “Pentagrammarspin” by Steve Hillage from the 2006 remaster of his LP Fish Rising

## I’m not following you: Twitter data and R

I wondered how many of the people that I follow on Twitter do not follow me back. A quick way to look at this is with R. OK, a really quick way is to give a 3rd party application access rights to your account to do this for you, but a) that isn’t safe, b) you can’t look at anyone else’s data, and c) this is quantixed – doing nerdy stuff like this is what I do. Now, the great thing about R is the availability of well-written packages to do useful stuff. I quickly found two packages twitteR and rtweet that are designed to harvest Twitter data. I went with rtweet and there were some great guides to setting up OAuth and getting going.

The code below set up my environment and pulled down lists of my followers and my “friends”. I’m looking at my main account and not the quantixed twitter account.

library(rtweet)
library(httpuv)
## setup your appname,api key and api secret
appname <- "whatever_name"
key <- "blah614h"
secret <- "blah614h"
app = appname,
consumer_key = key,
consumer_secret = secret)

clathrin_followers <- get_followers("clathrin", n = "all")
clathrin_followers_names <- lookup_users(clathrin_followers)
clathrin_friends <- get_friends("clathrin")
clathrin_friends_names <- lookup_users(clathrin_friends)

The terminology is that people that follow me are called Followers and people that I follow are called Friends. These are the terms used by Twitter’s API. I have almost 3000 followers and around 1200 friends.

This was a bit strange… I had fewer followers with data than actual followers. Same for friends: missing a few hundred in total. I extracted a list of the Twitter IDs that had no data and tried a few other ways to look them up. All failed. I assume that these are users who have deleted their account (and the Twitter ID stays reserved) or maybe they are suspended for some reason. Very strange.

## noticed something weird
## look at the twitter ids of followers and friends with no data
missing_followers <- setdiff(clathrin_followers$user_id,clathrin_followers_names$user_id)
missing_friends <- setdiff(clathrin_friends$user_id,clathrin_friends_names$user_id)

## find how many real followers/friends are in each set
aub <- union(clathrin_followers_names$user_id,clathrin_friends_names$user_id)
anb <- intersect(clathrin_followers_names$user_id,clathrin_friends_names$user_id)

## make an Euler plot to look at overlap
fit <- euler(c(
"Followers" = nrow(clathrin_followers_names) - length(anb),
"Friends" = nrow(clathrin_friends_names) - length(anb),
"Followers&amp;Friends" = length(anb)))
plot(fit)
plot(fit)

In the code above, I arranged in sets the “real Twitter users” who follow me or I follow them. There was an overlap of 882 users, leaving 288 Friends who don’t follow me back – boo hoo!

I next wanted to see who these people are, which is pretty straightforward.

## who are the people I follow who don't follow me back
bonly <- setdiff(clathrin_friends_names$user_id,anb) no_follow_back <- lookup_users(bonly) Looking at no_follow_back was interesting. There are a bunch of announcement accounts and people with huge follower counts that I wasn’t surprised do not follow me back. There are a few people on the list with whom I have interacted yet they don’t follow me, which is a bit odd. I guess they could have unfollowed me at some point in the past, but my guess is they were never following me in the first place. It used to be the case that you could only see tweets from people you followed, but the boundaries have blurred a lot in recent years. An intermediary only has to retweet something you have written for someone else to see it and you can then interact, without actually following each other. In fact, my own Twitter experience is mainly through lists, rather than my actual timeline. And to look at tweets in a list you don’t need to follow anyone on there. All of this led me to thinking: maybe other people (who follow me) are wondering why I don’t follow them back… I should look at what I am missing out on. ## who are the people who follow me but I don't follow back aonly <- setdiff(clathrin_followers_names$user_id,anb)
no_friend_back <- lookup_users(aonly)
## save csvs with all user data for unreciprocated follows
write.csv(no_follow_back, file = "nfb.csv")
write.csv(no_friend_back, file = "nfb2.csv")

With this last bit of code, I was able to save a file for each subset of unreciprocated follows/friends. Again there were some interesting people on this list. I must’ve missed them following me and didn’t follow back.

I used these lists to prune my friends and to follow some interesting new people. The csv files contain the Twitter bio of all the accounts so it’s quick to go through and check who is who and who is worth following. Obviously you can search all of this content for keywords and things you are interested in.

So there you have it. This is my first “all R” post on quantixed – hope you liked it!

The post title is from “I’m Not Following You” the final track from the 1997 LP of the same name from Edwyn Collins.