PORTAL USER GUIDE
The Pie Chart is a diagram which displays data in which a circle is divided into segments or slices or pies which are proportional or relative to the quantities they represent. This tool can only be used for numerical (as opposed to categorical) variables.
To show the Pie Chart in action, we will look at the distribution of occupied houses in Melbourne.
- Select Greater Melbourne as your area.
- Select ABS – Index of Household Advantage and Disadvantage (IHAD) (SA3) 2016 as your dataset, selecting the following variables:
- SA3 Code
- SA3 Name
- Occupied Private Dwellings
- Create classes using the Classifiers tool (Tools → Statistical Analysis → Classifiers) on the above dataset to classify the number of houses in each SA3 by five equal breaks.
- Dataset Input: Select ABS – Index of Household Advantage and Disadvantage (IHAD) (SA3) 2016
- Variable(s): Select Occupied Private Dwellings
- Number Of Classes: Type 5
- Type: Select Equal
Once you have selected these, open the Pie Chart parameter input window (Tools → Charts → Pie Chart) and enter the parameters as listed below.
- Dataset Input: Select the dataset that you would like to run the Pie Chart on. Select the output of the Classifiers tool.
- Variable: The variable holding the data you would like to plot. Select the resulting column name from our classification occ_dwell_class_sjh.
- Use Variable Titles: Uses the variable as the title for the classes. Untick this box.
- Chart Title: If you would like to name your chart, you can here, the default is Pie Chart. Leave blank.
- Legend: This will include a legend in the output of the pie chart. Tick this box.
- Legend Label Format: Select a format for the legend. This can be percentage, frequency or proportion. Select percentage.
- Greyscale: Check this box if you would like your graph in greyscale. Untick this box to keep the chart in colour.
Once you have entered your parameters, click Run Tool.
Once you have run the tool, click on the Display Output button in the pop-up window that appears. The resulting pie chart below will be displayed.
We can see in our chart the count of SA3s in Greater Melbourne containing the classified breaks of our data. For example, class 1 contains between 8230 and 20272 houses, and 10% of SA3s contain this quantity of houses. The ranges for our classes can be found with the variables occ_dwell_lower_sjh and occ_dwell_lower_sjh in our output from the Classifiers tool.