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Viewing the distribution of measure values in a dimension with a distribution plot

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Viewing the distribution of measure values in a dimension with a distribution plot

This example shows how to make a distribution plot to view the distribution of measure values in a dimension, using weather data from Qlik DataMarket as an example.

Distribution plot.

Dataset

In this example, we'll use weather data loaded from the Weather for more than 2500 cities worldwide data source in Qlik DataMarket. The dataset is based on the following selections in Qlik DataMarket:

  • Location: Sweden > Gällivare Airport
  • Date: All time
  • Measurement: Average of the 24 hourly temperature observations in degrees Celsius

The dataset that is loaded contains a daily average temperature measurement from a weather station in the north of Sweden during the time period of 2010 to 2017.

Note: DataMarket is not available in Qlik Sense Enterprise on Kubernetes.

Measure

We use the average temperature measurement in the dataset as the measure, by creating a .measure in Master items with the name Temperature degrees Celsius, and the expression Avg([Average of the 24 hourly temperature observations in degrees Celsius]).

Visualization

We add a distribution plot to the sheet and set the following data properties:

  • Dimension: Date (date) and Year (year). The order is important, Date needs to be the first dimension.
  • Measure: Temperature degrees Celsius, the measure that was created as a master item.
Distribution plot with the dimensions Date (date) and Year (year) and the measure Temperature degrees Celsius.

Distribution plot.

Discovery

The distribution plot visualizes the distribution of the daily temperature measurements. The visualization is sorted by year, and each point represents a temperature measurement.

In the visualization we can see that the year 2012 has the lowest extreme temperature measurement, close -40 degrees Celsius. We can also see that the year 2016 seems to have the largest distribution of temperature measurements. With this many points in the distribution plot, it can be hard to spot clusters and outliers, but the year 2017 has two low temperature measurements that stand out. You can hover the mouse pointer over a point and view the details.