One of our users need to find the max-value of a variable. He also needs to find the corresponding value in another variable.
As in – the maximum value in column A is in row 42. What is the value in column B, row 42.

And of course we need to do it for several groups.

Let us begin by making a dataset. Four groups in id,

library(tidyverse)
id <- 1:3
val <- c(10,20)
kor <- c("a", "b", "c")


example <- expand.grid(id,val) %>% 
  as_tibble() %>% 
  arrange(Var1) %>% 
  cbind(kor, stringsAsFactors=F) %>% 
  rename(group=Var1, value=Var2, corr = kor)

example

##   group value corr
## 1     1    10    a
## 2     1    20    b
## 3     2    10    c
## 4     2    20    a
## 5     3    10    b
## 6     3    20    c

We have six observations, divided into three groups. They all have a value, and a letter in “corr” that is the corresponding value we are interested in.

So. In group 1 we should find the maximum value 20, and the corresponding value “b”.
In group 2 the max value is stil 20, but the corresponding value we are looking for is “a”.
And in group 3 the max value is yet again 20, but the corresponding value is now “c”.

How to do that?

example %>%
  group_by(group) %>% 
  mutate(max=max(value)) %>% 
  mutate(max_corr=corr[(value==max)]) %>% 
  ungroup()

## # A tibble: 6 x 5
##   group value corr    max max_corr
##   <int> <dbl> <chr> <dbl> <chr>   
## 1     1   10. a       20. b       
## 2     1   20. b       20. b       
## 3     2   10. c       20. a       
## 4     2   20. a       20. a       
## 5     3   10. b       20. c       
## 6     3   20. c       20. c

The maximum value for all groups is 20. And the corresponding value to that in the groups is b, a and c respectively.

Isn’t there an easier solution using summarise function? Probably. But our user needs to do this for a lot of variables. And their names have nothing in common.

One can only hope that the concept “Digital Natives” will soon be laid to rest. Or at least all the ideas about what they can do.

A digital native is a person that grows up in the digital age, in contrast to digital immigrants, that got their familiarity with digital systems as an adult.

And there are differences. Digital natives assumes that everything is online. Stuff that is not online does not exist. Their first instinct is digital.

However, in the library world, and a lot of other places, the idea has been, that digital natives, because they have never experienced a world without computers, groks them. That they just know how to use them, and how to use them in a responsible and effective way.

That is, with a technical term, bovine feces. And for far too long, libraries (and others) have ignored the real needs, assuming that there was now suddenly no need for instruction in IT-related issues. Becase digital natives.

Being a digital native does not mean that you know how to code.

Being a digital native does not mean that you know how to google efficiently.

Being a digital native does not mean that you are magically endowed with the ability to discern fake news from facts.

I my self is a car native. I have grown up in an age where cars were ubiquitous. And I still had to take the test twice before getting my license. I was not able to drive a car safely, just because I have never known a world without cars. Why do we assume that a digital native should be able to use a computer efficiently?

For many years, from 1977 to 2006, there was a regular feature in the journal for the Danish Chemical Society. “Kemiske småforsøg”, or “Small chemical experiments”. It was edited by the founder of the Danish Society for Historical Chemistry, and contained a lot of interesting chemistry, some of it with a historical angle.

The Danish Society for Historical Chemistry is considering collecting these experiments, and publishing them. It has been done before, but more experiments were published after that.

We still don’t know if we will be allowed to do it. And it is a pretty daunting task, as there are several hundred experiments. But that is what I’m spending my free time on at the moment. If we get i published, it will be for sale at the website of the Danish Society for Historical Chemistry.

I Dansk Kemi var der i mange år en klumme “Småforsøg”. Små kemiske forsøg, der illustrerede en eller anden interessant kemisk pointe.

Dem vil vi samle. Og jeg er i fuld gang. Om det fører til en udgivelse ved jeg ikke, men i så fald vil den kunne anskaffes fra Dansk Selskab for Historisk Kemi

 

A palindromic number is similar to a palindrome. It is the same read both left to right, and right to left.

Project Euler tells us, that the largest palindrom made from the product of two 2-digit numbers is 9009. That number is made by multiplying 91 and 99.

I must now find the largest palindrome, made from the product of two 3-digit numbers.

What is given, is that the three digit numbers cannot end with a zero.

There are probably other restrictions as well.

I’ll need a function that tests if a given number is palindromic.

palindromic <- function(x){
  sapply(x, function(x) (str_c(rev(unlist(str_split(as.character(x),""))), collapse="")==as.character(x)))
}

The function part converts x to character, splits it in individual characters, unlists the result, reverses that, and concatenates it to a string. Then it is compared to the original x – converted to a character.
The sapply part kinda vectorises it. But it is still the slow part.

If I could pare the number of numbers down, that would be nice.

One way would be to compare the first and last digits in the number.

first_last <- function(x) { 
  x %/% 10^(floor(log10(x))) == x%%10
}

This function finds the number of digits – 1 in x. I then modulo-divide the number by 10 to the number of digits minus 1. That gives me the first digit, that I compare with the last. If the first and the last digit is the same – it returns true.

Now I am ready. Generate a vector of all three-digit numbers from 101 to 999. Expand the grid to get all combinations. Convert to a tibble,
filter out all the three-digit numbers that end with 0. Calculate a new column as the multiplication of the two numbers, filter out all the results where the first and last digit are not identical, and then filter out the results that are not palindromic. Finally, pass it to max (using %$% to access the individual variables), and get the result.

library(dplyr)
library(magrittr)

res <- 101:999 %>% 
  expand.grid(.,.) %>% 
  as_tibble() %>% 
  filter(Var1 %% 10 != 0, Var2 %% 10 != 10) %>% 
  mutate(pal = Var1 * Var2) %>% 
  filter(first_last(pal)) %>% 
  filter(palindromic(pal)) %$% 
  max(pal)

There are probably faster ways of doing this…

Et af målene med tilbagetrækningsreformen er at alle skal få ca. lige mange raske og sunde år på pension. Det er urimeligt at folk med høje uddannelser kan gå på pension samtidig med nedslidte murersvende. For de højt uddannede får flere raske år.

Må jeg i al fredsommelighed fremføre, at kvinder i Danmark pt lever 3 år længere end mænd. Og i snit i øvrigt er mere sunde og raske end mænd. Betyder det at Socialdemokraterne går ind for at mænd skal kunne gå på pension ca. tre år tidligere end kvinder?

When the mail from the students open with: “Dear really nice guy from #datalab, whose name we have forgotten”

Er når mailen fra de studerende indledes med:
“Kære rigtig flinke gut fra datalab, som vi har glemt hvad hedder. “

When is data anonymous? That is a very good question, and one that is increasingly relevant for my work.

Our datalabs at the University Library of Copenhagen (or whatever our current name is), is beginning to be a success. We have a steady increase in the number of students and researchers from the health sciences. And that triggers a recurring discussion.

Let me begin by noting that our users are very conscious of the issues regarding protecting sensitive information.They use encrypted hardware, secure connections to a degree I have only ever seen amongst people security consciuos enough to border on tinfoil folks. But they are still a bit naive about anonomyzing data.

I have no idea how to anonymize data. And the more I read about it, the less sure I am that it is actually possible. People smarter than me are probably able to figure out something. But I fear that this is a game rather like the DRM-games.

Yes, the studios can encrypt their Bluray discs. But they still need to be able to show the movie on a screen. The disc will have to be decrypted somehow. Otherwise it will just show static. And the data you are working with may be stripped of all identifying information. But there still needs to be information in it. Otherwise it is just useless.

So – I cannot advice our students on how to de-identify patients in a clinical study. But I can tell them horror stories. And I do. These are a few of them.

Netflix and IMDB

The classic story is the de-identification of Netflix users. Netflix has periodically released data on their users. Anonymized of course. Which movies have a given user watched, and how has that user rated them.

Another source about information on what movies a person has watched and rated is IMDB. And that information is not so secret. Let us asume that an unknown person has watched ten obscure movies on Netflix, and given the first five a high rating and the others a low. And that a known person on IMBD has rated the same five obscure movies high, and the other five low. Intuition would suggest that those two persons are the same. Is that a problem?

If you live in an area where being gay is a problem, you might not have problem people knowing that you have watched obscure, but innocent movies on IMDB. But the Netflix data, if linked to you, would reveal that you also have watched Another Gay Movie, Philadelphia and Milk. That might be a problem. I don’t think “Salo” is on Netflix. And I’m not necessarily that embarrassed to admit that I have watched it. But I would probably not want people to know that I have watched it ten times (if I had. Its horrifying). Heres a paper on the case.

Postcodes

A lot of demographic data is released to the public. We want people to know if living in a certain area causes cancer. And we want the underlying data out there, because there is just too much data to analyze, so if we could crowdsource that part of the process, it would be nice. So we anonymize the data, but leave in the postcodes. That might be a problem.

The danish postcode 1301 corresponds to the street “Landgreven”. According to www.krak.dk, 17 persons have an adress there. There might be a bit more. They only register people with a phonenumber. And leaves out people with an unlisted number. But let us assume that there are only those 17 persons. 8 of them are women. So if we have health data on medical procedures – broken down by postcode and gender, we might be able to say that one of 8 named women had an abortion. Not that there is much stigma associated with that in Denmark, or at least there shouldnt be. But it is still something you probably would like to keep to yourself.

Twitter, Flickr and graphs

Some people like to be anonomous on Twitter. Looking at the name-calling, flamewars and general pouring crap over people you disagree with on Twitter, it is surprising that not more people are trying to be anonymous on Twitter. But some people have legitimate reasons to try to be anonymous. Whistleblowers, human rigts activists etc.

Social media are characterized by graphs. Not pie charts and such. But networks. Each person is a node, and each connection, following eg, between nodes is an edge. The network defined by nodes and edges is called a graph. Two researchers Narayanan and Shmatikov have made an interesting study, “De-anonymizing social networks”. Take a lot of persons that have accounts on both Twitter and Flickr. Anonymise the Twitter accounts. One third of those Twitter accounts can be linked to the Flickr acocunt of the same person. In spite of the anonymisation.

How? Well, the graph describing who you follow and who follows you on Twitter, will share characteristics with the graph on Flickr. And those graphs are pretty unique. Read more here.

 

At være nøjagtig lige så pisseligeglad med hvad som helst som alle andre åbenbart er.

“Vi skal have annonceret kurser på webben”. Fem mand høj. Vi ved at det skal gøres. Alle ved det skal gøres. Alle kan gøre det. Men der er ingen der gør noget som helst. Jeg ender med at gøre det.

“Regnskabet skal revideres”. Hele bestyrelsen ved at det skal gøres. Kassereren ved det skal gøres. Ingen gør noget som helst. Revisoren – det er mig – ender med at sidde med det i sidste øjeblik. Det er ellers ikke noget der burde komme som en overraskelse. Det skal gøres en gang om året…

Der er simpelthen for meget i mit liv der kun sker hvis jeg tager initiativet. Det er åbenbart kun mig der mener det er vigtigt. Jeg skal simpelthen lære at være nøjagtig ligeså pissehamrende ligeglad med hvad som helst som alle andre omkring mig tydeligvis er. Det vil formentlig være godt for mit blodtryk.