I normally work with full numerical data, not categorical data. R, when using read.csv() seems to recognize such categories and marks the column as to have factor levels. This is useful indeed. However, I wanted to make a PCA biplot on this data, so was looking for ways to convert this to class numbers. After some googling we, Anna and me, ran into as.integer() which can be used on the factor levels. So, today I learned this trick:

> a = as.factor(c("A", "B", "A", "C"))
> b = as.integer(factor(a))

Well, probably basic to many, it was new to me :)

Now, wondering if it is equally easy to convert it into a multi-column matrix where each column indicates class membership (thus, resulting in three columns for the above...). That's another trick I need to learn...
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Labels: PCA R statistics
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Hi all, as posted about a year ago, I moved this blog to a different domain and different platform. Noting that I still have many followers on this domain (and not on my new domain, including over 300 on Feedly.com along).

This is my last post on blogger.com. At least, that is the plan. It has been a great 18 years. I like to thank the owners of blogger.com and Google later for providing this service. I am continuing the chem-bla-ics on a new domain: https://chem-bla-ics.linkedchemistry.info/

I, like so many others, struggle with choosing open infrastructure versus the freebie model. Of course, we know these things come and go. Google Reader, FriendFeed, Twitter/X (see doi:10.1038/d41586-023-02554-0).

Some days ago, I started added boiling points to Wikidata, referenced from Basic Laboratory and Industrial Chemicals (wikidata:Q22236188), David R. Lide's 'a CRC quick reference handbook' from 1993 (well, the edition I have). But Wikidata wants pressure (wikidata:P2077) info at which the boiling point (wikidata:P2102) was measured. Rightfully so. But I had not added those yet, because it slows me and can be automated with QuickStatements.

Just a quick note: I just love the level of detail Wikidata allows us to use. One of the marvels is the practices of 'named as', which can be used in statements for subject and objects. The notion and importance here is that things are referred to in different ways, and these properties allows us to link the interpretation with the source.

I am still an avid user of RSS/Atom feeds. I use Feedly daily, partly because of their easy to use app. My blog is part of Planet RDF, a blog planet. Blog planets aggregate blogs from many people around a certain topic. It's like a forum, but open, free, community driven. It's exactly what the web should be.

This blog is almost 18 years old now. I have long wanted to migrate it to a version control system and at the same time have more control over things. Markdown would be awesome. In the past year, I learned a lot about the power of Jekyll and needed to get more experienced with it to use it for more databases, like we now do for WikiPathways.

So, time to migrate this blog :) This is probably a multiyear project, so feel free to continue reading it hear.
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The role of a university is manifold. Being a place where people can find knowledge and the track record how that knowledge was reached is often seen as part of that. Over the past decades universities outsources this role, for example to publishers. This is seeing a lot of discussion and I am happy to see that the Dutch Universities are taking back control fast now.

I am pleased to learn that the Dutch Universities start looking at rankings of a more scientific way. It is long overdue that we take scientific peer review of the indicators used in those rankings seriously, instead of hiding beyond fud around the decline of quality of research.

So, what defines the quality of a journal? Or better, of any scholarly dissemination channel? After all, some databases do better peer review than some journals.

A bit over a year ago I got introduced to Qeios when I was asked to review an article by Michie, West, and Hasting: "Creating ontological definitions for use in science" (doi:10.32388/YGIF9B.2). I wrote up my thoughts after reading the paper, and the review was posted openly online and got a DOI. Not the first platform to do this (think F1000), but it is always nice to see some publishers taking publishing seriously. Since then, I reviewed two more papers.
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