Forum Discussion
DAX Parameter Selected Value
Hi Anonymous
I have tested your problem and your code seems to be fine. The key to the problem lies in the creation of visual objects.
I suggest you create two visual objects that show the data corresponding to different parameters.
Select "Gender"
Select "Age"
Select "line chart", "Year" in the x axis, "SelectedCategoryProportion" into the y axis.
It should be noted that "Gender" and "Age" are placed into the "Small multiples" of the two visual objects respectively.
At the same time, you can adjust the visual object to two rows and one column.
Regards,
Nono Chen
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Super UserCan you provide some sample data or pbix file? The cause cannot be determined because the model and context are not clear.
Thanks for looking into this. Please below table for your rererence:
Age
Gender Proportion Year Gap age YoungProportion MatureProportion MaleProportion FemaleProportion Mature Female 7.20% 2015/16 8.60% 0.157723577 0.071726893 0.080260304 0.071726893 Mature Male 8.00% 2015/16 8.80% 0.168731563 0.080260304 0.080260304 0.071726893 Mature Female 8.40% 2016/17 8.50% 0.16861167 0.083595922 0.07884097 0.083595922 Mature Male 7.90% 2016/17 8.60% 0.165222415 0.07884097 0.07884097 0.083595922 Mature Other 0.00% 2016/17 0.00% 0 0 0.07884097 0.083595922 Young Female 15.80% 2015/16 8.60% 0.157723577 0.071726893 0.168731563 0.157723577 Young Male 16.90% 2015/16 8.80% 0.168731563 0.080260304 0.168731563 0.157723577 Young Female 16.90% 2016/17 8.50% 0.16861167 0.083595922 0.165222415 0.16861167 Young Male 16.50% 2016/17 8.60% 0.165222415 0.07884097 0.165222415 0.16861167 Young Other 0.00% 2016/17 0.00% 0 0 0.165222415 0.16861167 Thanks for looking into this. Please below table for your rererence:
Age
Gender Proportion Year Gap age YoungProportion MatureProportion MaleProportion FemaleProportion Mature Female 7.20% 2015/16 8.60% 0.157723577 0.071726893 0.080260304 0.071726893 Mature Male 8.00% 2015/16 8.80% 0.168731563 0.080260304 0.080260304 0.071726893 Mature Female 8.40% 2016/17 8.50% 0.16861167 0.083595922 0.07884097 0.083595922 Mature Male 7.90% 2016/17 8.60% 0.165222415 0.07884097 0.07884097 0.083595922 Mature Other 0.00% 2016/17 0.00% 0 0 0.07884097 0.083595922 Young Female 15.80% 2015/16 8.60% 0.157723577 0.071726893 0.168731563 0.157723577 Young Male 16.90% 2015/16 8.80% 0.168731563 0.080260304 0.168731563 0.157723577 Young Female 16.90% 2016/17 8.50% 0.16861167 0.083595922 0.165222415 0.16861167 Young Male 16.50% 2016/17 8.60% 0.165222415 0.07884097 0.165222415 0.16861167 Young Other 0.00% 2016/17 0.00% 0 0 0.165222415 0.16861167