The Basic Plots objects allows a user to understand and analyze their data/transformations through visual representation such as line charts and scatter plots. They provide interactive visuals with growing capabilities and features that enable an in-depth understanding of the nature of the data and its trends.
In this document, we will learn how Basic Plots object can be used to construct a Scatter Plot and Line Chart on our data in Astera.
A Scatter Plot is a graph on cartesian coordinates to show the relationship between two variables - X and Y. Each coordinate pair (x,y) is shown as a dot on the scatter plot diagram.
To get a Basic Plots object from the Toolbox, go to Toolbox > Visualization > Basic Plots and drag-and-drop the object onto the dataflow designer.
Auto-map the source fields by dragging and dropping the root node of the source object onto the Scatter node (input node) of the Basic Plots object.
Right-click on the object’s header and select Properties from the context menu.
A configuration window will open as shown below. This is the Layout Builder screen, where users have the option to change the Name or Data Type of the fields.
Click Next to go to the Properties screen.
Here, the users have the option to select and configure the plot type, and set plot properties. Click on the Plot Type drop-down, and select Scatter from the menu.
In the Plot Fields group box, select Height as the X-Axis Field, from the drop-down containing all the mapped fields.
Similarly, select LungCap as the Y-Axis Field from the drop-down that shows all mapped fields.
Check the Size and Category check-boxes if required, and select the Category and Size field respectively from the provided drop-down options.
Category – Defines different color schemes for different categories associated with the data points.
Size – Changes the size of the scatter points on the graphs, proportional to the numeric value of the selected field.
Check Show Trend Line to fit a trend line on the data if it is required. When checked, the user has multiple Trend Line Type options to choose from in the drop-down menu.
Linear: Trend line that varies linearly as per the changes in the X-Axis variable.
Exponential: Trend line varies exponentially as per the changes in the X-Axis variable. It starts with low momentum and accelerates later.
Logarithmic: Trend line varies logarithmically as per the changes in the X-Axis variable. The growth/decay accelerates initially and then damps.
Polynomial: Trend line varies as a function of nth degree polynomial as per the changes in the X-Axis variable.
Check this option and click Next.
A Labels screen will appear. Here, users can fill in the labels for Title, Subtitle, X-Axis, and Y-Axis. Click Next.
An Additional Options screen will appear, providing the General Plot Options and the option to Save Plot.
Scale Axis: Scales the x-axis and y-axis as per the starting values of respective tables.
Inverted Graph: Inverts the graph vertically (top-down). This option is disabled for the Scatter Plots object.
Data is Sorted: Sorts incoming data if it is unsorted.
Enable Data Zoom: Provides controls to zoom the data points with respect to both axes.
Save the plot with .html extension by selecting the Save Plot checkbox. Click OK to close the window.
To visualize the plot, right-click on the header of the Basic Plots object and select Visualize Data from the context menu.
A Visualization window will appear. This window can be resized by dragging the boundary while it is clicked. Alternatively, it can be undocked from the bottom pane and viewed on the best scale.
Observe that the graph is interactive, you can hover on the data points to display the coordinates.
Line Chart is a simple but effective graphing method to visualize the data under consideration. Here, we can plot the data of metal prices with time, stored in an Excel Workbook Source.
Follow steps 1 - 3 of the Scatter Plot example given above.
Set Plot Type to Line.
The Properties screen has changed as per the selected plot type, and an option to select the X-Axis Field has been added to the window.
Select Date as the X-Axis Field from the drop-down menu.
Click OK.
To visualize the plot, right-click on the Basic Plots object’s header, and select Visualize Data from the context menu.
A Visualization window will appear. This window can be resized by dragging the boundary while it is clicked. Alternatively, it can be undocked from the bottom pane and viewed to the best scale.
This is how you can use the Basic Plots object in Astera.
The Distribution Plots object allows users to visualize categorical data variables using mainstream plots such as bar charts, pie charts, histograms, and polygons with an interactive interface and several configuration options. It is a useful object to visualize a general profile of your dataset.
In Astera, users can plot these graphs on data with up to half a million categories and display it easily with the drill-down feature.
In this document, we will learn how the Distribution Plots object can be used to plot and visualize your data.
A bar chart is a pictorial representation of grouped data in the form of rectangular bars. The height of the bars depends on the aggregates of numeric fields, grouped together by distinct categories of a categorical variable.
Users can construct a simple bar chart as well as a stacked bar. For the following example, you can download the sample data file from here (hyperlink).
To get a Distribution Plots object from the Toolbox, go to Toolbox > Visualization > Distribution Plots and drag-and-drop the plot object onto the dataflow designer.
Auto-map the source fields by dragging and dropping the root node of the source object onto the Bar (input) node of the Distribution Plots object.
Right-click on the object’s header and select Properties from the context menu.
A configuration window will open as shown below. This is the Layout Builder, where users have the option to change the name or Data Type of the fields, apply expressions, and provide a Default Value in case of null and empty records.
Click Next. Here, users have the option to select Plot Type and define plot properties.
Plot Type is set as Bar by default. The drop-down menu of this options contains several plot types.
Set Aggregate by to Frequency. The drop-down menu of this option contains 5 aggregate functions.
In the Plot Fields group box, users can select a Data Field to apply the selected aggregate function. Only with Frequency aggregate type, the object automatically selects the first mapped field and disables the option, as shown below.
There are additional plot display properties under the Bar Properties group box.
Horizontal Bars – Changes the orientation of default vertical bars to horizontal bars.
Show Data Labels – Displays data labels inside bars when the chart is rendered.
Check these options and click Next.
A Labels screen will appear. Here, users can fill in the labels for Title, Subtitle, X-Axis, and Y-Axis.
Click Next. An Additional Options screen will open, providing the following controls.
General Plot Options
Scale Axis – Scales the x-axis and y-axis as per the starting values of respective tables.
Inverted Graph – Inverts the graph by displacing the axis.
Data is Sorted – Sorts incoming data in case it’s unsorted.
Enable Data Zoom – Provides controls to zoom on data points with respect to both axis
Save the plot with .html extension by selecting the Save Plot checkbox.
To visualize the plot, right-click on the Distribution Plots object’s header, and select Visualize Data from the context menu.
A Visualization window will open, displaying the bar chart. You can click on the bars and drill down to next level of categories.
On the same data, users can also plot Stacked Bar available in Plot Types drop-down menu.
A pie chart is a pictorial representation of grouped data in the form of sectors of a circle. The area of the sectors depends on the respective percentage proportion of categories in a data field.
Users can construct a simple pie chart, a doughnut chart, and a nested pie.
For the following example, you can download the sample data file from here (hyperlink). A simple pie chart is configured the same way as a bar chart. Therefore, in this example, we will create a nested pie that has different configuration settings.
Follow steps 1-3 of Bar Chart example.
Set Plot Type to Nested Pie.
In the Plot Fields group box, users can select an Inner Field and an Outer Field for respective inner and outer pies.
There are additional plot display properties under the Pie Properties group box.
Outer Chart Type – Provides two outer chart display options.
Doughnut:
Nightingale:
This concludes our discussion on using the Distribution Plots object in Astera.
