PORTAL USER GUIDE

Bar Chart

The bar chart is a type of quantitative graph consisting of a series of rectangular bars which represent values by their height proportional to the represented quantities or values. The purpose is to show comparisons among categories.

SET UP

To show the Bar 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

Inputs

Once you have selected these, open the Bar Chart parameter input window (Tools → Charts → Bar Chart) and enter the parameters as listed below.

  • Dataset Input: Select the dataset that you would like to run the Bar 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.
  • Show Gridlines: This inserts gridlines into your graph. Tick this box.
  • Legend: This will include a legend in the output of the bar chart. Untick this box.
  • 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.

 

Outputs

Once you have run the tool, click on the Display Output button in the pop-up window that appears. The resulting bar 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 there are four SA3s with 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.

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