WEBVTT captioned by amine, checked by sachac
NOTE Introduction
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[Lukas]: Welcome to our presentation,
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Collaborative Data Processing
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and Documenting using org-babel.
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My name is Lukas Bossert, and I'm
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from the RWTH Aachen University
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in the city of Aachen, Germany.
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[Jonathan]: And my name is Jonathan Hartmann.
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I'm also from the IT Center here at RWTH Aachen.
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[Lukas]: Great.
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And we will show you today how you
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can use Org Mode for data processing.
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So you see a little workflow what we are going to do.
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First, we will give you a slight introduction to Org Mode.
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Then we will dive into the part of data preparing.
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First, you're going to query the data using the language SPARQL.
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Then we're going to clean it using a different language.
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And in the main part of our presentation,
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we're going to do the data processing, first aggregating
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using Python, later on counting items using Org,
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and even visualizing it using R. At the end,
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we're going to show you how to preserve
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the data and the document and its documentation,
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first doing in plain exporting, then adding some metadata,
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and showing you two different ways, first a manual export,
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and also then a batch-processed export.
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All right.
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Let's dive in to that.
NOTE Org Mode
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Jonathan, can you give us an introduction about Org Mode?
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[Jonathan]: Of course.
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So in case anyone isn't familiar with it,
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Org Mode, in the words of Carsten Dominik,
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is back to the future for plain text.
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So this is just a module available for Emacs,
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plain-text base.
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It's been around since 2003, which
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makes it about 20 years old.
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And it's extensible and fully customizable.
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And especially, it's very convenient, very good
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for scientific text production and organization.
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So for example, you can do project management, agenda,
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diary, journaling, personal knowledge management,
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presentation.
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Even this is written in Org Mode.
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It's an Org Mode presentation.
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You can do single source publishing,
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which we will do later on, and also
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literate programming, which is the core of our talk.
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OK.
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[Lukas]: So let me stop this presentation here.
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So what you see here is the plain text underneath it.
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So this is Org Mode.
NOTE Working together
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And Jonathan, since we kind of already
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did the introduction together, should we
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also do the working part together?
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[Jonathan]: Of course.
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So you see on the screen there on the right,
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that's my screen in Emacs.
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And Lukas, why don't you host a session using CRDT,
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and I'll connect to your buffer.
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[Lukas]: OK. Great.
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I do that.
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So what I do, I'm using Doom Emacs.
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And I can use the `SPC` and then the `l`
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for the live share/collab part.
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I can use the `s` for share current buffer.
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So when I do this, I'm getting asked for some settings.
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I'm going with the default settings here.
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So default port, no password, and my display name.
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And now Emacs is connecting.
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And once it's connected, which just takes a couple of seconds,
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I can get the URL.
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So I'm going back to this menu and using `y`
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for copying the URL of the current session.
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And this is the URL I'm going to send over to you, Jonathan,
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to pick that up.
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[Jonathan]: Right.
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OK.
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And now on my screen, I'm going to do a `SPC l c` for connect.
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And I'm going to paste the URL
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that Lukas just sent me in here.
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Default port, no password.
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And we're connecting now.
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So this takes a second just to get us synced up.
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So we can work on the same document at the same time.
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We can follow each other's cursors around.
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We can have multiple buffers open and work on them
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at the same time.
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And so here you see that we are both in the same document.
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You can see my cursor popping around.
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And you can see we're both editing the same item.
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Great.
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[Lukas]: So we also see who else is currently in our buffer
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with the user overview.
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So let me just delete that window.
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And that's going to work in our main one.
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So we said first part is about data retrieval.
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So we should give it a headline.
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We said prepare stage.
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So what are we going to do first, Jonathan?
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[Jonathan]: So what we're going to do,
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what this whole document is based upon,
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is we're going to pull data from Wikidata using a SPARQL query.
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The data we're going to pull is related to the NFDIs,
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which here in Germany is the National Forschungsdaten
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Infrastructure, which is a sort of collection of universities
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that work together on various research projects.
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And this is emblematic of the kind of data
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that we would be interested in working with here.
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So I'm going to paste a--forgive the pre-written code--
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I'm going to paste some text in here.
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[Lukas]: And while you are talking, I just
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keep on documenting what we do
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so we can split the work.
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[Jonathan]: In here, after a minor technical upset,
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is the raw dataset cell.
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And it's going to use SPARQL,
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which is how we have the syntax highlighting
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in our code here.
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It's going to go to the URL endpoint
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query.wikidata.org/sparql ,
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and it's going to return the data as a text CSV,
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and it's going to cache that data
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so that we don't constantly hammer the API every time
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we run this notebook.
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So I'm going to run that there.
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You can see down at the bottom of my screen,
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we're contacting the host query.wikidata.org .
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[Lukas]: And there's the result.
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[Jonathan]: Yeah, except I think that for our purposes here,
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we're just going to limit this to 50 results.
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[Lukas]: Oh, yeah.
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[Jonathan]: Just so it's a little easier for us to manage.
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I'm going to run that again.
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There we go.
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That looks a little better.
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[Lukas]: I think that's fine.
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50 items is fine.
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So what do we see here, Jonathan?
NOTE Data cleaning
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[Jonathan]: Right.
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So the first thing we see when we look at this
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is a couple of Q codes at the top,
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which are an artifact of Wikidata.
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So these are pages which don't have
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the label for whichever institution they happen to be.
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For our purposes here, we're just going to exclude them.
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We could just go on Wikidata and edit them ourselves.
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But for now, it's a little more interesting
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if we go and remove them.
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So I'm going to create a new cell.
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Lukas, if you don't mind starting one for data cleaning.
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[Lukas]: Oh, yeah.
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Good point.
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Yeah, data cleaning.
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OK.
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How do you want to do that, Jonathan?
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[Jonathan]: I'm going to use a shell command.
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So let's see.
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There we go.
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And so you can see, here is another cell,
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that the cell is now using a shell,
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and that we have this thing `:var input=raw-dataset`,
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which is the name of the cell above
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where we got our data from Wikidata.
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This is going to run just a simple shell command.
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It's going to take the input and then run `sed` on it
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and exclude any records which have a Q
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followed by one or more digits afterwards.
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That should remove those from our data set.
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So I'm going to run that.
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That seems to have done the trick.
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[Lukas]: Great, yeah.
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That's really good.
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We got rid of all the Q items.
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Very good.
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So we just have two-column table: institutions
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and consortia.
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Very nice.
NOTE Processing
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So let's come to our main part, doing some processing.
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Let me give you a headline here, process the data.
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What do you want to do first?
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[Jonathan]: This is not a very complicated data set,
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but let's just do some simple counts first.
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I'm going to start with Python,
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and we're just going to do some aggregation with Python.
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Again, I've got some pre-written code here.
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You can see that we've started a cell using Python.
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The variable `clean_df` now is equal to `clean-dataset`.
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So we're going to take that data
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that we retrieved from the SPARQL query,
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we're going to run it through the cleaning cell,
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and then we're going to import it into this cell.
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This is just going to do some simple Python aggregation.
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We're going to import `pandas`,
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which is the Python data science library,
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create a data frame out of our input,
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and then aggregate it, grouping on `wLabel`,
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and getting a count from that and returning it.
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So if we execute that cell...
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[Lukas]: Nice, we get institutions and a count.
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But what about not ordering it by the alphabet,
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but more like ordering by counts?
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[Jonathan]: Sure.
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So let's do this... `sort_values()`, I think, as the Python.
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How does that look?
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[Lukas]: Better, but I would like to
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have the highest number first
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and then ascending.
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Well, not ascending, descending.
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[Jonathan]: Right, so we can do `ascending=False`.
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[Lukas]: This is perfect, I'd say.
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[Jonathan]: Great.
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[Lukas]: Very good.
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OK, that's nice.
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We get a good overview here.
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But can we also do something else,
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like counting how many institutions are
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involved in one consortium?
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And also using this later on in the text?
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[Jonathan]: Sure, so I'm going to put a new...
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If you give me another heading down here
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for institutions per consortium...
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And here we're going to use awk code just to spice things up
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and add yet another language in here.
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So you can see this is awk.
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We're using standard in instead of defining a variable.
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But the really interesting thing about this cell
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is that we have this `:var consortium="NFDI4Memory"`.
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And what this code is doing is
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it's counting any time it sees
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that particular consortium name
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and keeping track of that.
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So if we execute this,
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Lukas, why don't you execute this one?
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[Lukas]: OK, I'm going to enter it.
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And I get a result, NFDI4Memory,
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because this is our default value for this variable.
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And we get the count.
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So it's five institutions are involved
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in the NFDI4memory consortium.
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Great, but the very nice thing, what I think,
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is here that we can use this code snippet within our text.
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So, blended in seamlessly.
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Let me give you an example.
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I'm writing out the text.
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Now we know how many institutions are in...
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Give me an example.
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I would like to know how many institutions are
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involved in NFDI4Objects, which is a consortium.
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So I'm writing `call_` and using
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the name of this snippet here, of this cell,
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which is `inst-count(`,
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and writing my value, `NFDI4Objects`.
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As soon as I evaluate this using `C-c C-c`,
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I get the result back here.
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I can do this even for more.
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Or in writing, `call_inst-count`, go with `NFDI4Earth`,
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which is another consortium.
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`C-c C-c`, it's three institutions.
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This can be used throughout your text,
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and as soon as the data set changes from in the beginning,
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maybe different results requiring Wikidata,
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this also will be updated once it's exported.
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Very nice, Jonathan.
NOTE Visualization
12:36.040 --> 00:12:38.974
But I think we did a lot of analysis
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on text and counting things.
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Can we also do something more visual?
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Show me something.
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[Jonathan]: Sure.
12:45.760 --> 12:48.639
So what we can do with this, because we just
12:48.640 --> 12:51.399
have two columns here that are sort of related,
12:51.400 --> 12:53.759
we can build a little network plot out of it.
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So let's make a network visualization.
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We're going to use the `igraph` library from R
12:59.600 --> 13:02.559
and just plot the edges that we see here.
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There we go.
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There's my little heading and space.
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Here is our code.
13:13.480 --> 13:16.039
Again, just to be fancy and keep using
13:16.040 --> 13:19.719
different languages in here, we set a variable called
13:19.720 --> 13:21.560
`NFDI_edges` equal to `clean-dataset`.
13:21.600 --> 13:23.399
So this, again, is sort of cascading
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through the original data
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that we pulled from the Wikidata endpoint,
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cleaning that data, and now it's being inserted
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into this cell as well.
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But you see the difference here.
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Instead of exporting a table, what we're saying
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is that there will be a graphics file,
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and it will be called network-plot.png.
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All right.
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And so Lukas, why don't you execute this one?
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[Lukas]: There you go.
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I can click `C-c C-c`
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and I get a nice plot of the network below our cell.
13:59.160 --> 14:01.759
So this is very nice indeed.
NOTE Preserve
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So I think it's about time to wrap it up and to export
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and to preserve the data and the documentation
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that we have in our very last step, calling preserve.
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So I would like to do it in two steps.
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First, maybe manually exporting it,
14:18.800 --> 14:22.239
but then also doing it in a batch process.
14:22.240 --> 14:27.119
Giving you some insights how to do that manual export.
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For example, you can do a LaTeX export.
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Let me write down the key combination to do that here.
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So you press `SPC m e l o`.
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Let me show you how this is done.
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So I'm pressing `SPC`.
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I'm pressing `m`, which is my local leader.
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I'm pressing `e`, which is now the `org-export-dispatch`.
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And now I have different options I can choose from.
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I want to do a LaTeX export because I want to get in PDF.
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So I'm pressing `l`.
00:15:08.675 --> 00:15:11.479
Now I've got different options available.
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So I'm pressing `o` for a PDF file and open that.
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Let's see now the code.
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Now this is exporting document.
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And what we have here is PDF,
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which contains our workflow in the beginning,
00:15:31.975 --> 00:15:35.707
our bullet points we have here,
00:15:35.708 --> 00:15:37.919
and also the code snippet
15:37.920 --> 15:41.120
that we use for querying the data.
15:41.280 --> 15:43.599
And we have the result below that.
15:43.600 --> 15:46.999
So this is our table with all the data sets.
15:47.000 --> 15:51.879
But as you can see, this is running out of the page.
15:51.880 --> 15:55.679
So this is not very nice using the default settings.
15:55.680 --> 16:00.239
But everything is in this PDF.
16:00.240 --> 16:02.759
I guess we can now show you a way
16:02.760 --> 16:06.519
how to improve this result.
16:06.520 --> 16:07.039
[Jonathan]: Right.
16:07.040 --> 16:09.399
So we have, of course, a version of this
16:09.400 --> 00:16:10.774
that we prepared ahead of time,
00:16:10.775 --> 00:16:14.279
which is more or less identical to the one we just made,
16:14.280 --> 16:17.839
but it has a little more text, a little more explanation,
16:17.840 --> 16:20.559
a little more documentation along with the code.
16:20.560 --> 16:23.879
You can see we have some metadata up at the top,
16:23.880 --> 16:26.879
the title, the authors, a bibliography,
16:26.880 --> 16:31.679
and most importantly, the `custom-export.setup` file,
16:31.680 --> 16:36.879
which lists specifically the sort of LaTeX commands
16:36.880 --> 16:43.599
that we're using and the HTML styles that we're going to use.
16:43.600 --> 16:45.919
And then down at the bottom of this file,
16:45.920 --> 16:49.119
we have our automatic batch process.
16:49.120 --> 16:51.719
Here is one more language we're including in here.
16:51.720 --> 16:53.439
So this is Lisp.
16:53.440 --> 16:57.359
And you can see here we are exporting to HTML, ASCII,
16:57.360 --> 16:58.079
and PDF.
16:58.080 --> 17:01.359
The nice thing about this is that this is a document.
17:01.360 --> 00:17:03.307
It's a sort of document that we have a couple of
00:17:03.308 --> 00:17:08.639
that we can have running automatically and building.
17:08.640 --> 17:12.919
It will export a HTML, an ASCII file, and a PDF file
17:12.920 --> 00:17:14.674
every time it's run based off of
00:17:14.675 --> 00:17:17.319
the most recent data available on Wikidata.
17:17.320 --> 17:19.719
So it's self-documenting.
17:19.720 --> 00:17:22.440
We have, of course, our data retrieval steps,
00:17:22.441 --> 00:17:25.159
our data cleaning steps, our data preparation steps,
17:25.160 --> 17:28.359
and our preservation steps all listed at the same time.
17:28.360 --> 17:30.239
And then you can see over on the right,
17:30.240 --> 17:34.320
there's an example of the HTML file that we get out of this.
17:34.360 --> 17:37.639
We also get a very nicely formatted PDF file,
17:37.640 --> 17:39.239
which doesn't have that little issue
17:39.240 --> 17:41.719
with the overflow of the table.
17:41.720 --> 17:43.559
It's very nicely put together.
17:43.560 --> 17:46.199
And we even have an ASCII file.
17:46.200 --> 17:47.879
And I should also point out very quickly,
17:47.880 --> 17:51.799
while you have this one up, Lukas, after the awk code,
17:51.800 --> 17:56.079
you can see the text for the number of consortia,
17:56.080 --> 17:57.839
or the number of institutions per consortia
17:57.840 --> 18:00.519
is actually printed inline.
18:00.520 --> 18:01.799
[Lukas]: Yeah, you're very right.
18:01.800 --> 18:06.119
So this is what we had as code,
18:06.120 --> 18:10.719
and now this is nicely integrated into our text.
18:10.720 --> 18:15.279
So we got the consortium and number of institutions.
18:15.280 --> 18:19.199
You can't tell a difference between code and text.
18:19.200 --> 18:20.719
[Jonathan]: And those are automatically updated.
18:20.720 --> 18:23.879
So if another institution joins NFDI4Earth,
18:23.880 --> 18:26.319
then the next time this runs, we update the text right here.
18:26.320 --> 18:28.519
It's nothing we have to worry about.
18:28.520 --> 18:30.400
We just pull it directly out of Wikidata.
18:31.840 --> 18:34.679
[Lukas]: And for the sake of completeness,
18:34.680 --> 18:37.879
this is the ASCII file.
18:37.880 --> 18:39.320
That's in the export format.
18:42.760 --> 18:46.440
It contains also everything, code and data.
18:48.360 --> 18:51.680
Yeah, so this is what we wanted to show you,
18:53.240 --> 18:56.639
how to do some data processing,
18:56.640 --> 18:58.679
some collaborative work,
18:58.680 --> 19:01.119
documenting using org-babel.
19:01.120 --> 19:03.960
Thanks for listening.
19:05.720 --> 19:07.280
[Jonathan]: Thank you all, have a good day.
