[SOUND] So, data visualization can consist of some very simple charts,

but the success of a data visualization can often depend on how

we map our data variables to the elements of those charts.

So we can start with a Bar Chart.

And the bar chart has two axises typically.

You've got a horizontal axis and a vertical axis.

And you're usually measuring discrete values here, and

some either discrete or continuous value vertically.

And this benefits from the fact that you're mapping a variable,

a data variable, to both position, the actual height of these bars,

as well as to a length, the size of the bar.

And so you do a really good job of not only seeing.

That, for example, the orange bar is larger than the blue bar, but

how much larger the orange bar is to the blue bar because

position and length are both at the top of perceptual effectiveness for

displaying quantitative values.

And so usually vertically we have some sort of quantitative dependent variable.

And then horizontally these can be categories.

And so we have some nominal variable or at least some discreet variable here

indicating the individual bars that we're plotting.

And this is an independent variable, a dimension.

And then this is some kind of measure of that dimension.

It's a dependant variable depending on the value of this independent variable.

Similarly, you have a line chart.

A line chart has data points that are connected by a line.

And so this is very very similar to a bar chart.

These data points are at the same altitude as the tops of the bars.

So they benefit from position but they don't have the length.

That you visually see with the bars in a bar chart.