[Auto-generated transcript. Edits may have been applied for clarity.]
In this video, we are going to look at what a cute quarto document is and how we can use it.

And I will also talk you through how to run code, to read in data and build a plot.

Now, before we do that, we first need to make sure that we download the files that we need and then upload them to the server.

So let's go to, um, the handbook pages.

Um, I'm in Lab activity one.

So you can just use this navigation bar on the right to get there quickly.

Um, there is a little bit of background for you to read.

Um, it just explains what kind of data we're working with today.

Um, so I do recommend that you read that and have a look at the questionnaire where it comes from.

Um, and then the aims of this activity are to download the files to set everything up in our studio.

Then we're going to read in the data. And then we are going to build our first plot.

So step one download the files. We will be working with an example script um called this.

And you can see a Schmid file extension. So that means that it is a quarter document.

And we will also need a data file called data underscore BMW three.

So that file contains the responses of the participants, um that we will be working with.

Now that is a file of the type CSV, which means that the individual bits of data are separated by commas.

Um, we'll often use CSV files. It's a reliable kind of format to store data in.

So to download these two files 2001 go we.

You just click on this zip folder. There we go.

This should have put it in my download a downloads folder.

So let's see whether it's there. We go to downloads and indeed there it is.

So far so good. So the next step is to go to the RStudio window here.

Um, and now we're going to upload that zip folder.

So in the bottom right quadrant in the files tab there is an upload button in the middle.

So we click that. Now in here it asks you where you want the zip folder to end up.

And this is fine. This is inside our psych for one for one project.

So that's excellent. So here we need to choose the file.

We can just choose that zip folder and say upload.

And then okay. And there we go. Here it is.

Now some of you might wonder, um, about the zip folder.

So a zip folder is a folder in which the files have been compressed.

And usually when you have a zip folder you need to unzip it or decompress it.

But the answer, if it does that for you. So you don't need to worry about that.

You can just upload that zip folder in one go and the decompression will happen automatically.

So here we have the four one for one week one lab folder.

And inside it we have the Schmid file and the data file.

Excellent. Now let's click on let's open the quarter document.

And we do that by just clicking on this here um and it opens up the source quadrant that we talked about in a previous video.

So here we have a quarter document.

And um, a quarter document is basically a single file that combines your written text with the AR code that runs your analysis.

Um, so when you render it, that's just a particular word to kind of process it.

Quarto runs the code and produces a finished report.

And you can tell, um, quarter what type of report you want.

So that will have your results, your tables, your figures. And it will automatically, uh, those will automatically be included alongside your writing.

Now that takes a little bit of practice to, to to make that all work smoothly.

Um, but it is well worth investing a little bit of time in that because it means that your analysis,

your analysis is fully reproducible because anyone can rerun the documents and get exactly the same numbers that you got when you run your analysis.

Now you will have heard Rob talk about why reproducibility is important.

So that is why we teach you how to write a fully reproducible report using quarter.

Okay, so I have written this lab activity in a quarter document.

So you will see text like the background or the aims. Um, but then down here you will also see these grey boxes.

And this is an R code chunk.

So you can tell that it's an R code chunk because it has R here in the corner.

Theoretically you could write code in different programming languages, but we're not going to do that.

So here we have our first r uh code chunk.

Um, and then, you know, you can add a bit more text and then there is a next chunk.

Etc., etc.. Now, um, as I mentioned, uh, in an earlier video, you have this outline here on the side.

So if I click on this it goes away. But it is really helpful to have it here.

So you can easily navigate through this document.

Instead of having to scroll up and down, you can just click on on let's say step two.

And there you are. So um, whenever we share an example script like this,

it is a good idea to read through it from kind of from top to bottom and do the things where we say like action.

Run the code below to load the libraries. Um, so there will be different actions for different bits.

Um, so yeah, start at the top and work your way through it.

Um, kind of bit by bit.

And of course, if you have questions about something, then, um, do, uh, ask one of us, uh, during the labs about the code chunk.

So, um, in the top right corner of the code chunk here, you can see a few buttons.

Um, so this green arrow, which very much looks like the run thing up here.

That is, that is the the kind of the button that makes, uh, that allows you to if you click it, it a quarter will run that code chunk.

So, um, is there any reason not to do that?

No. I think we can do that. Let's do it. So it has now done it.

So it has now loaded those libraries. Um, so just as an example.

Um, okay, I think so.

This is quarto has two views. So we're currently in a visual view.

Um, and that is probably the easiest to read in a way.

Um, but it also has a source view and that kind of has the code, uh, that sits underneath.

So you can just click on that and then um, you can see what the code is that sits underneath,

um, the, the kind of thing that produces the visual output.

So this is basically this is written in something called markdown.

So if headers have, um, two hashtags in front of them, um, if you want to produce, uh, that, that grey box, you can do something like that.

Um. Importantly here you have the R code chunk.

So there's three back ticks and then curly brackets with an R, and you need to close that code chunk again by again producing these three brackets.

Now in this because this is your very first script.

We have kind of written code for you, and we've explained a bit kind of line by line, what each line of code does.

Um, you know, when the weeks go by and, um, you will be asked to write more of this code yourself, but, um, as you've never done it before.

In this first example script, you kind of have the code here.

And the task for this week really is the kind of work through that and understand what each line does.

Okay. I think that is all that I wanted to explain about, um, the quarto document at this point.

Um, so now I'm going to walk you through, uh, the different bits of code.

Okay. So we have downloaded the data, we've uploaded the data, and, um, now we need to set up a few things in our studio.

Um, specifically, we need to load the libraries.

Um, now, you might wonder, um, what what that is about.

Um, so I think previously I described, uh, kind of r as the kitchen and all the different packages as the cookbooks.

Um, and you need to tell r which cookbooks it needs to use for what you want to do right now.

So we want to use two libraries for this particular activity, the library here and a library called Tidy Verse.

So the library here is um, is a very useful thing that a very useful package that helps you find your files.

Um, and the tidy verse is a set of packages that handles almost everything you want to do with data.

Um, so if you want to learn more about a tidy verse, you can go to go to this website.

But we'll encounter a kind of packages that are part of this larger set.

That's called the tidy verse a lot during this module.

So that is what these two lines of code do.

Okay. Now we've set things up in the sense that we've kind of told our which packages to use.

Now we are ready to load to read in the data.

Um, so step three, that is and this is the bit of code that does that, does that job.

It. I just realised that the file path in the file that I've shared with you looks a little bit different.

Um. This is because I made this on my machine. And if it needs to find the files on my machine, this is what the file path needs to look like.

Whereas I've adapted the file path in the file that you've downloaded.

Um, so that it works on the server. Uh, but the, the, the principles are, of course, the same.

So to read in, um, the data, we use what is called a function that's called read underscore CSV.

Now you might remember that it is the file type is CSV.

So that makes a little sense. It's a sense read underscore CSV.

Mind you there is also a version um that is called readcsv. We do not want to use that.

We recommend that you use read underscore CSV.

That is part of our part of the tiny verse, and it keeps the data in a particular format that is easy to work with.

Okay, so, um, if we use this function, we need to tell our where the files, where the file is and what it is called.

So we, uh, here is where we tell it, where the file is.

And this is the bit that spells out what the file is called.

You can see that we use this other function called hey in front of the file path.

And that just, um, helps R to locate this file path a little bit more easily.

Okay. So we have told our what we want to do.

We want to read in a CSV file. We have told our where that file is and what it is called.

Um and then you have this kind of arrow to the left that is called an assignment arrow.

And it is basically saying put that data in an object or data frame, um,

that I have called data underscore MW for mind wandering because this is data from a mind wandering questionnaire.

You can call this something else, whatever you like. I would.

It should not contain any spaces or any funny characters. So I would stick with, with, um, letters and underscores.

Um, but other than that, you can call it whatever you like. Okay, so let's just go back to the, um, RStudio window and, um, actually do this.

So we have already, uh, loaded the libraries.

Now we go to the next code chunk. We run that. Let's see.

Okay. Um, that works. And now you can see that in the environment.

Uh, quadrant in the top right, we have a data file called data underscore MW.

um, so that's because that's what we've asked.

Ah. To put it in. Um, you can also see that, uh, this has 215 observations of 22 variables, which means that it contains data of 215 participants.

Now you can look at this data either by clicking on it and it will open it.

Here you can see subject 1 to 215.

Uh yes 215. Um, and then there's various other um, variables age, gender, education.

And here we have the responses of each participant to the different questions on the questionnaire.

And we will be working with the first question. So BMW W3 UN.

Zero one. You can also kind of preview this.

What's in this data file by clicking on this white arrow. And then you can see the different variables here.

And kind of a little preview of what is in that variable.

It's very handy if you're writing code in your document here to not have to constantly look at the actual data file,

but you can say, okay, what was that? You know, what was that exact variable name.

And you can you can see it in the environment window. Okay.

Now, um, let's go back here.

So now we have read in the data. That's very good. Now we're going to build our plot.

Now to build our plot. We will use um the ggplot2 package um, which is part of the tidy verse.

So we don't need to load anything additional for that.

It is already available because we've loaded the first package.

Um, inside, uh, kind of a function from the ggplot2 package is called ggplot.

Now the GG stands for a grammar of graphics.

Um, so there is actually a philosophy behind, um, kind of how to, uh,

behind all the code that comes from that, that sits in ggplot2 of how best to create graphics.

Um, so the grammar of graphics is kind of the rules of how to make good plots.

Um, so we have ggplot is the function that we use to tell r that we want to create a plot.

Um, and if we do that we need to tell R as well what data we want to use and what it is from within that data that we want to,

uh, kind of put in the plot. So here you can see kind of that we are, um, using the, the variable name for that first question in the questionnaire.

Um, and I'm planning to create something that's called a histogram.

And for that we only need to specify something that we want on the x axis.

So the axis that it goes from left to right. Now S stands for aesthetic.

So that's kind of that's just what it's called. So we we um say that we want to plot something.

We tell it which data we want to use, and then we have exactly what it is that we want to map.

Um, so let's just go briefly, go back to that.

If you, um, just do that, you will actually get an empty plot.

So if we look at the plot, um, it has created kind of the background and it has said, okay, you want to put that thing on the x axis.

Um, but there is nothing else to see. Now, why is that?

Because we haven't told R what type of plot it is that we want.

So that is what we need to do next. Um, and as I said, we want to you to create a histogram.

So then we need to say geom histogram.

So um. Geom stands for a geometric object.

So we wanted to use the geometric object object histogram to create the plot.

Now there are there are many different types of geometric objects and will encounter a few during this module.

During this module. Um, the other thing to notice is, is this plus sign here at the end of line one.

So this is very important because otherwise, um, r would not know what to do.

Uh, so basically you can think of, uh, a the way a plot is built in, uh, R is in layers.

So you can kind of think of this, what's in the first line of code as the first layer, kind of the basics.

And on top of that we then create another layer that um, contains the um, the actual histogram.

And later on you will see we'll keep adding layers. And each time we do that we need to add a plus at the end of the previous line of code.

So do we now have a histogram. We do indeed.

So a histogram is a plot that shows you count data.

So it basically shows how many people how many participants responded, um, with this response option.

Um, for instance, uh, on on this first question.

So just under 100 people, uh, selected option number three, um, and just under, well,

just about ten people, uh, selected option zero will figure out what the what those options actually mean.

Uh, a little later. Now, I don't like the background very much with this grey and white line.

So I would quite like to change that. And that's what we're going to do next.

So we are going to um, add another layer you see plus sign here.

And this is called theme underscore BW for black and white.

So there are there are many. There are many different themes and you can explore them.

Um, in the ggplot uh documentation.

Uh, but here I've gone for a simple black and white background.

So what does that look like? Here we go. So now, instead of grey with white lines, we have white with grey lines.

And I find that a little easier to read. Next.

I don't particularly like these grey bars. I would like to change that to something, um, different.

So I'm going to change the colour of the bars.

And to do that, I need to add a little bit of additional code to this first line.

So inside the mapping part of the ggplot function I um Add comma fill equals and then a colour.

And there are many different colours as well that you can, you can choose something else.

Um in the quarto document that you will be working with, I've added a link so that you can go to a website that specifies the different colours.

I would say pick one that you like. Um, so instead of the default grey, I'm telling our to fill in the bars with a kind of a tomato red colour.

And this is what that looks like. I think that's a lot prettier.

Um, now we have this annoying legend here.

If you have multiple groups, for instance, that would be really useful.

Um, so there is occasions where you do want a legend on the side, but in this case we do not.

So we are going to remove that legend. And that's what we do in this next layer.

So basically I'm saying, hey, the position of the legend should be none.

So that is um, basically it removes the legend here on line four of this code, and indeed it is gone already.

It looks much better, I think. Then.

This kind of. Here we have the file, the variable name of um, of this first question.

But if you're looking at that or if you're presenting your data and somebody else sees that figure it, they're not going to know what that is.

So how about we put a more informative title on the x axis.

We can do that. We add a layer here with the plus.

And then we say labs x equals and whatever we wanted to call it.

So this is data from the brief mind wandering three factor skill.

Um, and it is question one. So that is what I've put there. If you want to keep it simpler, you could just say mind wandering question.

What? Um, so, yeah, put something, uh, informative on your axes.

Always label your axes so that it is clear to whoever is looking at it what is on your axes, and then use a title that is informative.

So here we go. Already much more informative.

Now I would so these um value labels go from zero, one, two, three and four.

And that is what the data look like.

So if we go back to our studio here and we go back to our data file, you can see here that this is every response has a numerical, um code in it.

But it is a code. It stands for something else. Because if um, in the in the kind of at the beginning of the if we go to the background,

um, it specifies, I believe, um, what, what it is that people did somewhere.

I think I put this in in here somewhere. Um.

Or maybe I put it somewhere else. I think in this document somewhere. I put what, what each of these codes mean.

So, um, again, if you are looking at a graph like this, it is helpful to actually have a more informative label here.

So that is what we're going to do next. So lines 6 to 9 here do that.

So it um uses this function called skill x continuous.

And then it says we have um labels that go from zero to 1 to 2 to 3 to 4.

That's what the colon just says. Take everything from 0 to 4.

Um, and instead of having those numbers, we want labels that go from fully disagree,

to somewhat disagree, to neutral, to somewhat agree and to fully agree.

Um, so lines 6 to 9, they replace the numerical values with text labels.

Um, and if we run that bit of code, this is what it looks like.

So now you, uh, have a much more informative plot that shows the responses on this first question of the brief mind wandering three factor skill.

And I believe that question was something along the lines of when I listen to a presentation, my mind wanders.

Um, I put a link to the questionnaire in the quarter document so you can have a look.

Um, so almost a hundred people responded with somewhat agree to that question.

Very few said fully disagree or somewhat disagree.

So to get there, probably about 25 people. Um, and then a few people said neutral and then another 75 people said, fully agree.

Um, okay. That is all I wanted to show you.

Um, yeah. Work your way through that quota document. Run the bits of code.

Try to remind yourself what each line does. Play around.

Change the colour. Um. And, you know, do something else that you think should be changed.

Um, and if you want more practice, you could create a similar plot for, let's say, question two.

Thank you for watching.