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And this we do, we learn about aleatoric.

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Area charts are very similar to line charts.

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The only thing is the area below the line has been cleared.

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You can look at this example that I've shown here.

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In this data, the first column is giving us the short sales in four quarters.

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This right graph is the line chart of this, these forward values.

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The left chart is the area graph.

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You can see that area chart is same as line chart.

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Only thing is that the area below this line is colored in this chart.

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Now, if I want to have multiple detectives in an area talked to, just like Landshark will have multiple

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lines and area blue, all those lines will be colored and it will be difficult to interpret.

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Let us see how to do that.

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I'll add a legend.

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Her name is Bantz.

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And the series is these four values, these values.

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Another one I thought was a name.

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And these four are the values.

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We can read the letters also.

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OK?

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OK.

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So you can see now we have three cities and this table is missing the box, so, um, first of all,

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not able to identify which color is representing which cities, letter box or the.

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So.

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So you can see we have three cities in this single chart and most of the blue cities which is representing

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the shirttails in the fourth quarter, we are not able to see it because it is hidden behind the other

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two cities.

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If you want to look at the values of cities, we need to increase the transparency of the bank city

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and the other cities.

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You know how to do it if you select any particular cities.

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And go to formatted as the cities in the solid file, you can add transparency by increasing it here

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you can see that now you can look at the shared dataset is.

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Now, let us increase the transparency of others to the series.

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And also change its color so that it does not match with paint.

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So so now you can see the three little cities and you can also understand why it is very difficult to

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use this ideograph.

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Since the datasets are overlapping with each of the.

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One solution to this is using the stacked area.

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So if you change the area to tape.

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The second option is the stack, the chart you can see in this example here.

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So this is basically putting the these values one way or another, so in cordovan, the short sales

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was really around 470.

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The next 100 is pulled from this 470 value and it reaches up to nearly 6500.

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The third value, which is for others, which is 564, is stacked on top of it.

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So now you can easily see all the three values and you can also see the contribution of each of these

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cities to the total.

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And just like Bordetella in charge, the third option in this graph also is 100 percent stacked in.

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In which the total will be said 200 percent and the contribution of each individual product will be

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shown as a percentage of total.

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The other options and area is a 3-D area, 3-D, Tigrinya and 300 percent area there to select 3-D art.

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You can see that each of these datasets is now 3-D.

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One altitude of this fall is in line with the value at each data point.

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You can see again, this third dataset is more or less so, although this chart may look visually appealing

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to a lot of people, census data is often obscured behind the other datasets.

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This chart type is not recommended for business use most of the names.