Dimensions and Measures Video Lecture Transcript
This transcript was automatically generated by Zoom, so there may be discrepancies between the video and the text.

15:21:29 Hi! Everybody! Welcome back in this video. We're gonna learn about tableau's dimensions and measures.
15:21:35 So when we load data into tableau, the columns or variables within that data get classified in 2 2 ways.
15:21:43 The first. Is, is it a dimension, or is it a measure?
15:21:47 The second is, is it continuous data, which is often colored or not often, but is always colored green?
15:21:53 Or is it discrete data which is always colored blue?
15:21:56 So these colored blue. So these classifications determine how tableau can use the data when we're making plots.
15:22:00 As well as what is the different functionality it shouldn't be for each of the data columns so the first thing we're gonna talk about are dimensions and measures.
15:22:10 So dimensions are used to categorize segment or otherwise subset the data.
15:22:16 So as an example, sex from the Galton data set, which, if you're trying to follow along and your tableau desktop or online app, use the Galton dot Csv data when you open the app and load the data so sex here is considered a dimension so it would allow us
15:22:35 to filter for male or female, as well as color.
15:22:39 Our points, as male or female. These tend to be qualitative or discrete, or maybe you've heard categorical data, and if you can see here, I've said above the data dividing line.
15:22:54 So do I mean by that over here on the left we have data.
15:22:57 And then these things called tables, these are the columns of our data, the dimensions are often by default, listed at the top, so sex and there's a thing called measure names.
15:23:08 But sex is the one from the data set then these things at the bottom will learn about in a second.
15:23:14 So that's dimensionions. Measures contain numereric variables that can be measured.
15:23:21 So, for example, in the Galton Height data, the height column or heights table is a measure because it is a quantitative variable that can be measured I know it sounds silly, so these are below the data dividing line, and we're do I mean by the
15:23:38 dividing line. It's this very thin line you kind of see right here.
15:23:41 So the measures are below this line. Okay, measures tend to be continuous.
15:23:49 They're used to sort of like. These are the things we're plotting and are interested in.
15:23:55 Typically so, these are aggregated by default.
15:23:59 When we add to a chart, what does this mean? As a quick note, so like as an example, let's say I wanted to add height as my vertical axis.
15:24:08 You'll notice here. It doesn't say height.
15:24:11 S it says, sum of height so and less actually, let me go ahead and go back to default.
15:24:18 It doesn't say, Hey, it's some of height, and the default version. So what?
15:24:24 That's what it means when it says that it's aggregating by default.
15:24:27 So when you drag and measure over into the plotting area by default, some sort of aggregation will happen.
15:24:34 So a sum or an average, or a Median, something like that will happen to measures.
15:24:41 As I said, I haven't said that yet, so measures quantitative below the data dividing line.
15:24:51 Okay, so continuous versus discrete data is the other way, tableau will break down your data.
15:24:58 Your columns of your data, setting your variables, quantitative variables are known as continuous variables in tableau.
15:25:06 So these are colored greens. So in this example, all of our measures are quantitative variables.
15:25:12 They're colored green, so they're colored green when we hover over and they're colored green.
15:25:18 When we add them to the plotting area. So here in the rows we can see, we have a measure which has been aggregated, continuous measure.
15:25:28 So you can have. You can have discrete measures as well.
15:25:33 So, for instance, number of kids would technically be a discrete measure.
15:25:40 However, it is currently read in as a continuous measure and we'll talk about that more in a second.
15:25:47 Qualitative variables are known as discrete variables and tableau.
15:25:50 So these are colored blue. So as an example, sex. When you hover over it is blue, and if I were to add it to the columns, you'd see that up there in the columns that is also blue.
15:26:02 So continue, or to continue us is green. Discreet? Is blue.
15:26:07 You'll also notice that the tableau app puts little icons.
15:26:13 Next to all of the data, variables. So things that are numeric have the number, sign, and things that are quant categorical have the little letters because they're read in a strings.
15:26:28 There are categorical. Have the little letters because they're read in as strings. There are other icons that we.
15:26:32 So, as I was saying earlier, sometimes tableau will misclassify data.
15:26:39 So maybe it reads in a discrete, categorical, variable as a continuous measurement when it's meant to be a discrete dimension.
15:26:53 So? Why is that so? If your data is coded up so that the variable has the categories represented by integers, it would read those as numerics, and then just assume that they are meant to be measures.
15:27:07 Perhaps so, you can always convert the data. You can either do this by dragging and dropping.
15:27:12 So let's say, I wanted the number of kids to be like a category.
15:27:15 I could drag it up to dimensions. And now it's a dimension, and it's also blue.
15:27:20 So it's thought of as discrete. So let's take it back to where it belongs or where it started.
15:27:25 I can also click on the little arrow, and once I click on that little arrow.
15:27:31 So, for example, you can't really see it. So why don't we do it again? I hover over.
15:27:35 There's this little arrow here. I click on it.
15:27:37 You can also see. I can convert to discrete, convert to dimension change, data, type default properties.
15:27:45 So I can change the data type. If I think that tableau is read and wrong.
15:27:49 Okay. So now, we know about dimensions and measures and continuous versus discrete maybe something that's useful, as you'll sometimes hear me call these things as fields sometimes.
15:28:06 That's what people in tableau and the documentation calls the different variables.
15:28:12 They might hear these called fields. That's a another note.
15:28:15 Okay. So now that you have a better idea of the data within how tableau treats the data, once it's been loaded you're ready to start learning how to make some visualization.
15:28:25 So in the next lecture video, and the next lecture slides we'll talk about how to make scatter plots.
15:28:33 All right. I hope you enjoyed this video. I enjoyed having you here.