Det meste af denne blog er på engelsk. Jeg arbejder i et internationalt felt. Men her kommer mit engelske lidt til kort – i hvert fald på skrift.

Vi har et problem med CPR-numre. Og det skyldes en grundlæggende misforståelse.

CPR-nummeret er den unikke identifikation af en person i Danmark. De ti cifre der er knyttet til mig, identificerer mig. Når vi taler om en person med CPR-nummeret 123456-7890, er der en og kun en person der kan være tale om.

Vi ved også at det er en person født i den ukendte måned 34 og at vedkommende er født med en biologi der gør vedkommende til en “hun”.

Så når jeg går ind i en forretning, og indgår en låneaftale, hvoraf det fremgår at CPR-nummeret på låntager er 123456-7890 – så er det entydigt identificeret, at det er denne meget fiktive person der hæfter for pengene.

Betyder det så at jeg, den person der stod i forretningen, var denne person?

Nej. Jeg kan have stjålet et sygesikringsbevis. Jeg kan for så vidt have produceret det selv. Det er ikke så svært. I de mest grelle eksempler, har låntager slet ikke fremvist noget som helst, men bare sagt CPR-nummeret. Hvis jeg ikke har fremvist legitimation på at jeg er den person der er identificeret med det CPR-nummer – så er der ingen garanti for at jeg faktisk er den person.

Problemet opstår fordi folk ikke kan finde ud af at skelne mellem netop identifikation og legitimation. CPR-nummeret bliver betragtet som en hemmelig kode. Hvis man kan oplyse det, så må man være den person. Men det er ikke spor hemmeligt. Det står på dit sygesikringsbevis. Det gemmer sig i stregkoden. De første seks cifre oplyser du gladeligt til hvem som helst. Det sidste ciffer kan, for 99,4% af befolkningen, begrænses til at være et af fem.

Du tror det er hemmeligt, og at hvis du kan oplyse det, så kan der ikke være tvivl om at det faktisk er dig. Udbydere af kviklån lader som om det er hemmeligt, og at de har fat i den rigtige person hvis de får det oplyst. Jeg er helt sikker på at de godt er klar over problemstillingen, men bare er ligeglade. Alle lader som om at man kan bruge CPR-nummeret til at legitimere sig. Og fordi vi lader som om man kan det, får man problemer med identitetstyveri når folk kan optage forbrugslån ved blot at oplyse et CPR-nummer. Og folk går i panik over at deres CPR-nummer offentliggøres.

Vi burde offentliggøre alle CPR-numre. Så ville det forhåbentlig stå klart for alle at CPR-nummeret ikke legitimerer noget som helst.

As a follow up to the previous post.

Yes, a sub-optimal meeting culture does not automatically become optimal because it goes online.

But! This corona/covid-19/chinese flu crisis amplifies stuff. The racial problems in the US becomes more pronounced. The problems with the british class system are suddenly visible even from across the north sea. The challenges workers in precarious posions in a global economy faces are enlarged.

And the problematic elements in a meeting culture becomes large enough, that something might actually be done about them!

Denmark has been in lockdown – or something like it – for close to two months now. March 11th our prime minister announced that all public employees where to be sent home as soon as possible. A lot of other institutions, companies and functions were similarly closed down. Further restrictions were introduced in the following weeks.

My husband was also told to work from home. He had his first full day at home on the 12th. I had to show up at work on the 12th, and was sent home early because I had an online meeting in the afternoon.

How is this working? The first 1½ week it was great! Finally I had the time to get on top of things, uninterrupted by meetings, colleagues asking if I wanted to drink coffee, or if I could help change the toner in the printer.

The next two weeks – (timeline excluding the 1½ week I was on mandatory vacation at home in connection with easter) – where not that productive. The stuff that could be done without talking with colleagues was running out.

And after that – now we have found the way. Online meetings are working, debating the way we should design a general, introductory workshop on visualization of data is as frustrating as it would have been otherwise. And the amount of irrelevant meetings is back to normal. Do not misunderstand me – meeting with colleagues without having an agenda is nice. And necessary, especially since we are in almost complete isolation at home. On the other hand. Not every meeting is actually necessary. I started the lockdown joking that we would now discover which meetings could have been emails. Now, I think that we are beginning to discover which emails could have been meetings. If we cant meet, at least we can have a meeting.

In other words – the bad habits of the physical office is entering the online office.

Managers and colleagues have no problem booking online meetings at the same time as you are in another meeting. With little to no understanding that you are actually in another meeting. As I wrote my boss: I can’t wait for us to get physically back to the library, so I can tell you that I cannot attend that meeting, since I am out of town at another meeting.

Usually it is possible to get people to understand, that if your meeting at location A ends at 12.00 you are not able to attend another meeting at location B. Or, they can understand if if there is 30 minutes of transportation between the two locations. Not so in the virtual world. Leading to a day (two weeks ago as of this writing), with the first meeting beginning at 9 and ending at 9.30. The next meeting from 9.30 to 11.00. The next again from 11.00 to 12.00. And the next from 12.00 to 13.00. The fifth meeting started 13.00 and was finished 14.00 Not that we were actually finished, but the sixth meeting was at 14.00. Happily it was a short one, only lasting 15 minutes. So I had 45 minutes for a biobreak, before the final meeting started at 15.00.

 

Moderne it-systemer gør det muligt at dele kalendere, så man kan se hvornår ens kolleger laver hvad. Det er super praktisk. De giver også mulighed for at booke møder med kollegerne direkte i deres kalendere. Virkelig smart.

Når man skal booke en kollega til et møde er der to filosofiske skoler man kan abonnere på.

Den ene er: Jeg kan se at Christian sidder i et andet møde mellem 12 og 13 på torsdag. Så vil det nok være en dårlig ide at indkalde til møde på det tidspunkt.

Den anden er: Jeg kan se at Christian sidder i et andet møde mellem 12 og 13 på torsdag. Andre har altså overvejet om det tidspunkt ville være et godt tidspunkt at mødes på. De har sikkert kigget på mange alternativer, og tænkt længe over det optimale mødetidspunkt. Jeg vil også mødes med Christian på det best tænkelige tidspunkt ever. Og det er jo åbenbart torsdag kl. 12 til 13. Så der booker jeg også et møde med ham.

Man kan ikke kritisere kineserne for at spise vilde eksotiske dyr, og dermed udløse den nuværende krise. Du ved, den der begynder at lugte af Den Spanske Syge, sammenbrud af demokratier, økonomisk depression, og stigende nationalistisk populisme.

Men nej. Det er racisme. Det fremmedgør den kinesiske kultur, og placerer den som det “andet”. Og det er meget farligt. Vi spiser også saltlakrids, og det er næsten det samme. Der var også noget med at Den Spanske Syge stammer fra en svinefarm i Kansas, og vi spiser stadig svin.

Og så glemmer vi for et kort øjeblik, at moderne studier mere end antyder at Den Spanske Syge kom fra Kina.

Vi må i øvrigt ikke kalde Den Spanske Syge for den spanske syge. Trump har nemlig kaldt COVID-19 for den kinesiske virus. Det gør han som led i en barnlig spinkrig med kineserne, der har travlt med at undertrykke meldinger om at sygdommen først viste sig i oktober/november. Og iøvrigt antyde at det må være det amerikanske militær der har indført smitten i Kina.

Så pt er der en heftig debat på wikipedia, om hvorvidt den spanske syge skal omdøbes til 1918 influenzaen. Hvorfor? Ja, det giver heller ikke mening for mig.

Den vestlige civilisation kommer ikke til at gå under på grund af en aldrende befolkning, indvandring fra Afrika eller en agressiv virus (fra Kina).

Den vestlige civilisation kommer til at gå under, fordi vi ikke bryder os om at stå ved at den kultur vi dyrker her, faktisk er andre kulturer overlegen.

Må man sige det? Tja.

Kulturer der giver kvinder de samme rettigheder som mænd er efter min mening bedre end kulturer der ikke gør.

Kulturer der praktiserer og beskytter ytringsfrihed, er efter min mening bedre end kulturer der ikke gør.

Kultruer der leverer medicinske og videnskabelige fremskridt der får mennesker til at leve bedre og længere liv, er, efter min mening, bedre kulturer end kulturer der ikke gør.

Kulturer der giver homosexuelle ret til at eksistere, er efter min meget personlige mening, bedre kulturer end kulturer der ikke gør.

Og kulturer der undlader at slippe vira fra flagermus og pangoliner løs, og får verden til at gå under er efter min mening grundlæggende bedre end kulturer der gør.

Og ellers er testen: Hvis du er villig til at risikere at drukne i Middelhavet for at komme fra en kultur til en anden – hvilken kultur mener du så er bedst?

Verden er ved at gå under. Og vi arbejder hjemmefra.

Observationer:

1. Det er stadig ikke muligt at deltage i to møder samtidig.
   1. Det har chefen stadig ikke opdaget
2. I stedet for at finde ud af hvilke møder der kunne være klaret med en email, finder vi nu ud af hvilke emails der åbenbart skal klares med et online møde.

Errorbars

An errorbar is a graphical indication of the standard deviation of a measurement. Or a confidence interval.

The mean of a measurement is something. We want to illustrate how confident we are of that value.

How do we plot that?

I am going to use three libraries:

library(ggplot2)
library(dplyr)
library(tibble)

ggplot2 for plotting, dplyr to make manipulating data easier. And tibble to support a single line later.

Then, we need some data.

data <- data.frame(
  name=letters[1:3],
  mean=sample(seq(4,15),3),
  sd=c(1,0.2,3)
)

I now have a dataframe with three variables, name, mean and standard deviation (sd).

How do we plot that?

Simple:

data %>% 
  ggplot() +
  geom_bar( aes(x=name, y=mean), stat="identity", fill="skyblue") +
  geom_errorbar(aes(x=name, ymin=mean-sd, ymax = mean+sd), width=0.1)

So. What on earth was that?

The pipe-operator %>% takes whatever is on the left-hand-side, and inserts it as the first variable in whatever is on the right-hand-side.

What is on the right-hand-side is the ggplot function. That would normally have a datat=something as the first variable. Here that is the data we constructed earlier.

To that initial plot, which is completely empty, we add a geom_bar. That plots the bars on the plot. It takes an x-value, name, and a y-value, mean. And we tell the function, that rather than counting the number of observations of each x-value (the default behavior of geom_bar), it should use the y-values provided. We also want a nice lightblue color for the bars.

To that bar-chart, we now add errorbars. geom_errorbar needs to know the x- and y-values of the bars, in order to place them correctly. It also needs to know where to place the upper errorbar, and the lower errorbar. And we supply the information that ymin, the lower, should be the mean value minus the standard deviation. And the upper bar, ymax, the sum of the mean and sd. Finally we need to decide how broad those lines should be. We do that by writing “width=0.1”. We do not actually have to, but the default value results in an ugly plot.

And there you go, a barchart with errorbars!

Next step

That was all very nice. However! We do not usually have a nice dataframe with means and standard deviations calculated directly. More often, we have a dataframe like this:

mtcars %>% 
  remove_rownames() %>% 
  select(cyl, disp) %>% 
  head()

##   cyl disp
## 1   6  160
## 2   6  160
## 3   4  108
## 4   6  258
## 5   8  360
## 6   6  225

I’ll get back to what the code actually means later.

Here we have 32 observations (only 6 shown above), of the variables “cyl” and “disp”. I would now like to make a barplot of the mean value of disp for each of the three different values or groups in cyl (4,6 and 8). And add the errorbars.

You could scroll through all the data, sort them by cyl, manually count the number of observations in each group, add the disp, divide etc etc etc.

But there is a simpler way:

mtcars %>% 
  remove_rownames() %>% 
  select(cyl, disp) %>% 
  group_by(cyl) %>% 
  summarise(mean=mean(disp), sd=sd(disp))

## # A tibble: 3 x 3
##     cyl  mean    sd
##   <dbl> <dbl> <dbl>
## 1     4  105.  26.9
## 2     6  183.  41.6
## 3     8  353.  67.8

mtcars is a build-in dataset (cars in the us in 1974). I send that, using the pipe-operator, to the function remove_rownames, that does exactly that. We don’t need them, and they will just confuse us. That result is then send to the function select, that selects the two columns/variables cyl and disp, and discards the rest. Next, we group the data according to the value of cyl. There are three different values, 4, 6 and 8. And then we use the summarise function, to calculate the mean and the standard deviation of disp, for each of the three groups.

Now we should be ready to plot. We just send the result above to the plot function from before:

mtcars %>% 
  remove_rownames() %>% 
  select(cyl, disp) %>% 
  group_by(cyl) %>% 
  summarise(mean=mean(disp), sd=sd(disp)) %>% 
  ggplot() +
    geom_bar( aes(x=cyl, y=mean), stat="identity", fill="skyblue") +
    geom_errorbar(aes(x=cyl, ymin=mean-sd, ymax = mean+sd), width=0.1)

All we need to remember is to change “name” in the original to “cyl”. All done!

But wait! There is more!!

Those errorbars can be shown in more than one way.

Let us start by saving our means and sds in a dataframe:

data <- mtcars %>% 
  remove_rownames() %>% 
  select(cyl, disp) %>% 
  group_by(cyl) %>% 
  summarise(mean=mean(disp), sd=sd(disp))

geom_crossbar results in this:

data %>% 
ggplot() +
  geom_bar( aes(x=cyl, y=mean), stat="identity", fill="skyblue") +
  geom_crossbar( aes(x=cyl, y=mean, ymin=mean-sd, ymax=mean+sd))

I think it is ugly. But whatever floats your boat.

Then there is just a vertival bar, geom_linerange. I think it makes it a bit more difficult to compare the errorbars. On the other hand, it results in a plot that is a bit more clean:

data %>% ggplot() +
  geom_bar( aes(x=cyl, y=mean), stat="identity", fill="skyblue") +
  geom_linerange( aes(x=cyl, ymin=mean-sd, ymax=mean+sd))

And here is geom_pointrange. The mean is shown as a point. This probably works best without the bars.

data %>% ggplot() +
  geom_bar( aes(x=cyl, y=mean), stat="identity", fill="skyblue", alpha=0.5) +
  geom_pointrange( aes(x=cyl, y=mean, ymin=mean-sd, ymax=mean+sd))

Pinligt få tips. Men dog et par stykker.

Rigsdagsbygningen – historie i læssevis. Og man kan komme op i kuplen. Har vi hørt. Man skal booke adgang online. Og det er en meget omstændelig proces. Så gør det i meget god tid! Stiller man sig i kø (og det gør man også i god tid inden de åbner), så er der adgang sen aften. Det skal man ikke være ked af – Berlin gør sig godt ved nattetide.

Vi er glade for jazz. Og vi blev ganske fornøjede med ATrane, der har livemusik flere gange om ugen: www.a-trane.de

Interessant – og meget anbefalelsesværdig brunch: House of small wonder. Japansk-europæisk fusionsmorgenmad.

Det naturhistoriske museum. Klassiske museumsdyder. Og en ganske imponerende samling af dinoer, blandt andet Tristan, et imponerende T Rex skelet. Deres samling af vådpræparater er efter vores mening rigelig grund til at besøge museet, selv hvis man ikke er interesseret i dinoer.

Hvor spiser man? Rotisserie Weingrün på Gertraudenstrasse 10 i mitte er afsindig godt. Men nu gå jeg også gerne efter steder der har brisler på menuen.

Einstein unter den linden er også ok. Det er www.einstein-udl.com

What is the smallest, positive, number that can be divided by all numbers from 1 to 20 without any remainder?

We are given that 2520 is the smallest that can be divided by all numbers from 1:10.

One number that can definitely be divided by all numbers from 1:20 is:

factorial(20)

## [1] 2.432902e+18

But given that

factorial(10)

## [1] 3628800

is rather larger than 2520, it is definitely not the answer.

The answer must be a multiple of all the primes smaller than 20. A number that is divisible by 15, will be divisible by
3 and 5.

The library “numbers” have a lot of useful functions. Primes(20) returns all primes smaller than 20, and prod() returns the product of all those primes

library(numbers)
prod(Primes(20))

## [1] 9699690

Could that be the answer?

What we are looking at is the modulo-operator. 9699690 modulo 2 – what is the remainder? We know that all the remainders, dividing by 1 to 20 must be 0.

prod(Primes(20)) %% 2

## [1] 0

And our large product is divisible by 2 without a remainder.

Thankfully the operator is vectorized, so we can do all the divisions in one go:

9699690 %% 1:20

##  [1]  0  0  0  2  0  0  0  2  3  0  0  6  0  0  0 10  0 12  0 10

Nope.

9699690 %% 4

## [1] 2

Leaves a remainder.

(2*9699690) %% 4

## [1] 0

Now I just need to find the number to multiply 9699690 with, in order for all the divisions to have a remainder of 0.
That is, change i in this code until the answer is true.

i <- 2
all((i*9699690) %% 1:20 == 0)

## [1] FALSE

Starting with 1*9699690, I test if all the remainders of the divisions by all numbers from 1 to 20 is zero.
As long as they are not, I increase i by 1, save i*9699690 as the answer, and test again.
If the test is TRUE, that is all the remainders are 0, the while-loop quits, and I have the answer.

i <- 1
while(!all((i*9699690) %% 1:20 == 0)){
 i <- i + 1
 answer <- i*9699690
}