And this we do, we learn about aleatoric.
Area charts are very similar to line charts.
The only thing is the area below the line has been cleared.
You can look at this example that I've shown here.
In this data, the first column is giving us the short sales in four quarters.
This right graph is the line chart of this, these forward values.
The left chart is the area graph.
You can see that area chart is same as line chart.
Only thing is that the area below this line is colored in this chart.
Now, if I want to have multiple detectives in an area talked to, just like Landshark will have multiple
lines and area blue, all those lines will be colored and it will be difficult to interpret.
Let us see how to do that.
I'll add a legend.
Her name is Bantz.
And the series is these four values, these values.
Another one I thought was a name.
And these four are the values.
We can read the letters also.
OK?
OK.
So you can see now we have three cities and this table is missing the box, so, um, first of all,
not able to identify which color is representing which cities, letter box or the.
So.
So you can see we have three cities in this single chart and most of the blue cities which is representing
the shirttails in the fourth quarter, we are not able to see it because it is hidden behind the other
two cities.
If you want to look at the values of cities, we need to increase the transparency of the bank city
and the other cities.
You know how to do it if you select any particular cities.
And go to formatted as the cities in the solid file, you can add transparency by increasing it here
you can see that now you can look at the shared dataset is.
Now, let us increase the transparency of others to the series.
And also change its color so that it does not match with paint.
So so now you can see the three little cities and you can also understand why it is very difficult to
use this ideograph.
Since the datasets are overlapping with each of the.
One solution to this is using the stacked area.
So if you change the area to tape.
The second option is the stack, the chart you can see in this example here.
So this is basically putting the these values one way or another, so in cordovan, the short sales
was really around 470.
The next 100 is pulled from this 470 value and it reaches up to nearly 6500.
The third value, which is for others, which is 564, is stacked on top of it.
So now you can easily see all the three values and you can also see the contribution of each of these
cities to the total.
And just like Bordetella in charge, the third option in this graph also is 100 percent stacked in.
In which the total will be said 200 percent and the contribution of each individual product will be
shown as a percentage of total.
The other options and area is a 3-D area, 3-D, Tigrinya and 300 percent area there to select 3-D art.
You can see that each of these datasets is now 3-D.
One altitude of this fall is in line with the value at each data point.
You can see again, this third dataset is more or less so, although this chart may look visually appealing
to a lot of people, census data is often obscured behind the other datasets.
This chart type is not recommended for business use most of the names.