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…