Standard chart types

Stacked columns

../_images/stacked-columns-icon.png

Use this chart to breakdown your dataset along one axis. Each value or bin of the first breakdown creates one column. Columns can show the count of records, or an aggregation on a variable.

In the following example, each “Hour of day” from the “client_ts_parsed” column makes one column. The height of the column if the average of “br_width” for this hour.

../_images/stacked-columns-1d.png

If you break down on two dimensions, the second dimension will subdivide each column into stacked elements:

../_images/stacked-columns-2d.png

Note

Note that it generally does not make sense to use the “AVERAGE” aggregation when you breakdown on two dimensions (X axis and stack). Only aggregations that “naturally stack” should be used: SUM and COUNT.

Options

If you have two dimensions, you have the option to “Normalize Stacks at 100%” on the Y axis. This causes all columns to have the same height, the height of each element within the column becoming a percentage.

../_images/stacked-columns-normalized.png

Stacked area

../_images/stacked-area-icon.png

This chart works roughly like Stacked Columns but it will create a smooth area instead of columns. For example, here is how the previous sample looks like as a Stacked Area:

../_images/stacked-area-2d.png

Scatterplot

At the moment, DSS does not feature a simple scatterplot (also known as XY plot). However, it features the “Binned XY plot” and “Grouped XY plot”.

Note

Both “Grouped XY plot” and “Binned XY plot” are aggregated plot that do not display data points individually, but aggregated by the values of one or two dimensions.

Grouped XY plot

../_images/grouped-scatter-icon.png

This chart breaks down the dataset on one dimension. For each value of the dimension, it creates one circle. The X-Y location of the circle is determined by performing aggregations on two different variables. The size and color of the circle can additionally be determined by more aggregations or counts.

../_images/grouped-scatter.png

In this example, we create one circle per hour of the day. For each hour, the circle is positioned at the average of br_width and br_height for this hour, and the more records there are for this hour, the darkest the circle.

To know which hour corresponds to each circle, hover a circle and a popup will appear.

Note

This is an aggregated plot. For each value of the breakdown dimension, only one circle is placed. Therefore, the measures used to position the circle must be numerical, because we must be able to aggregate them.

Binned XY plot

../_images/binned-scatter-icon.png

This chart breaks down the dataset on two dimensions and creates one circle for each value or bin of the dimensions. The dimensions do not need to be numerical. The color and size of each circles are represented using aggregations of measures.

../_images/binned-scatter.png

In this example, we can see that our most frequent case is Chrome in France.

Note

This is an aggregated and binned plot. If you display a numerical dimension, it will be binned and one circle will be created for each bin. It does not represent individual values

Hexagonal binning

If both your dimensions are numeric, you can switch to Hexagonal binning. In this mode, circles are replaced by hexagons. You do not control individually the binning of each dimension anymore but instead the desired radius of your hexagons.

../_images/binned-scatter-hexbin.png

Hexgonal binning generally provides a better overview of the distribution of your data than discrete circles and can adequately represent large amounts of data

Warning

Hexagonal binning is incompatible with live in-database processing. If you use in-database processing and want to enable hexagonal binning, you will need to switch to DSS Charts Engine.