To appreciate all that slicers can do, we need to see one in action. This means having at least one standard visual in a page so that you can see the result of applying a slicer. To test a slicer

You can recognize a slicer by the small squares to the left of each element in the list. This way you know that it is not just a single-column table. Figure 21-2 shows a slicer using the Color field.

To apply a slicer and use it to filter data on a page, click a single element in the slicer or Ctrl-click multiple elements.

All the objects in a Power BI Desktop page are filtered to reflect the currently selected slicer list. In addition, each element in the slicer list that is active (and consequently used to filter data by that element) now has a small rectangle to its left, indicating that this element is selected.

Figure 21-3 shows what happens when the slicer defined previously is applied to the visualization shown in Figure 21-2.

When you apply a slicer, think filter. That is, if you select a couple of elements from a slicer based on the CountryName field, as well as three elements in another slicer based on the Color fields, you are forcing the two slicers (filters) to limit all the data displayed in the page to two countries that have any of the three colors that you selected. The core difference between a slicer and a filter is that a slicer is always visible—and that you have to select or unselect elements, not ranges of values.

If you experiment, you will also see that you cannot create a slicer from numeric fields in the source data. A slicer has to be based on a text field. If you need slicers based on ranges of data, then you will need to prepare these ranges in the data model. The CarAgeBucket field is an example of this. Chapters 11 and 12 explain how to add these sorts of fields to a data model.

To clear a slicer and stop filtering on the selected data elements in a view, click the Clear Slicer icon at the top right of the slicer. This icon is pointed out in Figure 21-3.

Any filters applied by the slicer to the view are now removed. You will see that each element in the slicer list now has a small empty rectangle to its left, indicating that this element is not selected. No data is now filtered out of the report.

To delete a slicer and remove all filters that it applies for a view, select the slicer and press the Delete key. Alternatively, click the context menu button (the ellipses) that appears at the top right when you hover the mouse pointer over a slicer, and select Remove.

Any filters applied by the slicer to the view, as well as the slicer itself, are now removed.

You can even copy and paste slicers if you wish. This is very useful when you are copying slicers across different Power BI Desktop reports or between report pages.

If all you want to do is replace the field that is used in a slicer with another field, then it is probably simplest to do this:

The current slicer field is replaced by the new field and the slicer updates to display the contents of the field that you added. Alternatively, you can delete the slicer and re-create it.

When you start applying slicers to your Power BI Desktop reports, you rapidly notice one important aspect of the Power BI Desktop filter hierarchy. A slicer can only display data that is not specifically excluded by a report- or page-level filter. For instance, if you add a Color filter at page level and select only certain colors in this filter, you are only able to create a slicer that also displays this subset of colors. The slicer is dynamic and reflects the elements that can be displayed once any report and page filters have been applied—just like any other visual. Consequently, adding or removing elements in a filter causes these elements to appear (or disappear) in a slicer that is based on the filtered field.

If you wish, you can apply a filter specifically to a slicer. This allows you to restrict the elements that appear in a slicer. If you want to do this, then you must apply the filter after step 3 in the previous example and before you convert the table to a slicer. However, be aware that the slicer itself does not give you any indication that it is being filtered; that is, there is no visual-level filter displayed in the Filters well.

Conversely, to remove a filter from a slicer, you need to switch it back to a table and remove the filter, and then switch back to a slicer.

To help you to make the best possible use of report real estate, slicers can be configured to appear vertically (as in Figure 21-2) or horizontally. To switch a slicer’s orientation:

As you can see in Figure 21-6, a horizontal slicer does not have the small check boxes that a vertical slicer does. Consequently, when a horizontal slicer element is selected, the entire element is in “reverse video.”

You probably also noticed that Power BI Desktop alters the number of rows of text as well as the width of text elements when you resize the slicer. It follows that you are best advised to experiment when altering slicer height and width in order to get the effect that suits you best.

For Power BI Desktop, the outline is the line that separates the title from the items in a slicer. The following discusses how you can format this particular element.

If you are not happy with the way that Power BI Desktop lets you select items in a slicer, then you can adjust the way that you interact with this particular visual. While the Power BI default mode of interaction is probably sufficient in most cases, it certainly does no harm to know that there are other ways of selecting elements in slicers.

If you want to place a slicer with total accuracy on a dashboard canvas, then you can set the X and Y (horizontal and vertical) coordinates for the slicer. You can also specify its exact height and width. The following explains how:

Should you want to add some visual pizzazz to a slicer, you can tweak the display of the slicer header. Here is an example of how to do this:

Slicer items are the individual elements that make up the list of data that appear in a slicer, based on the underlying field. These, too, can be formatted to focus the attention of the reader . Here’s an example:

You can also format the following aspects of a slicer:

However, since all of these are identical to formatting attributes that apply to tables and charts (and that you have seen in previous chapters), I will not repeat the description of them.

Finally, Figure 21-8 shows you a slicer with many of the formatting options that you just saw.

The elements in a slicer will be displayed in alphabetical order. If you wish, you can reverse this sort order, as follows:

The slicer elements will now be displayed in reverse alphabetical order.

Slicers are amazingly useful, but they do have one drawback: they can take up a lot of valuable screen space. So the development team at Microsoft has come up with an elegant solution. This is to use dropdown slicers, where the slicer elements only appear when you click a popup menu.

To convert an existing slicer to a dropdown slicer:

Slicer data can be exported just like the data in any other visual:

To begin with, let’s see how a chart can be used to act as a slicer to filter data interactively for any or all of the visualizations in a Power BI Desktop report. Initially, let’s assume that we are aiming to produce a report using two objects:

Let’s create a Net Margin by Color table. It is principally used to show the effect that using a chart as a slicer in a Power BI Desktop report has on other objects. As an added extra, you will apply a filter to the page to demonstrate that filters and slicers work together, as described.

To see how to use a chart as a slicer, click any column in the chart of parts costs by make. I will choose Jaguar in this example.

The Power BI Desktop report will look something like Figure 21-12.

You will see that not only is the make that you selected highlighted in the chart (the bars for other makes are dimmed), but that the figures in the table also change. They, too, only display the cost plus spares (for each color) for the selected make.

Any bar chart, pie chart, or column chart can act like a slicer in this way, as can funnel charts, treemaps, scatter charts, bubble charts, and maps. The core factor is that for a simple slice effect, you need to use a chart that contains only one axis; that is, there will only be a single axis in the source data and no color or legend. What happens when you use more evolved charts to slice, filter, and highlight data is explained next.

So far, we have seen how a chart can become a slicer for all the visualizations in a dashboard page. However, you can also use another aspect of Power BI Desktop interactivity to make data series in charts stand out from the crowd when you are presenting your findings. This particular aspect of data presentation is called highlighting .

Once again, highlighting is probably best appreciated with a practical example. First, we will create a stacked bar chart of parts and labor costs by CountryName; and then we will use it to highlight the various costs inside the chart.

After highlighting has been applied, the chart will look like Figure 21-13.

To remove the highlighting, all you have to do is click a second time on the same element in the legend. Or, if you prefer, you can click another legend element to highlight this aspect of the visualization instead. Yet another way to remove highlighting is to click inside the chart, but not on any data element.

Highlighting data in this way should suit any type of bar or column chart as well as line charts. It can also be useful in pie charts where you have added data to both the Axis and Legend boxes, which, after all, means you have multiple elements in the chart just as you can have with bar, column, and line charts. You might find it less useful with scatter charts.

Cross-chart highlighting adds an interesting extra aspect to chart highlighting and filtering. If you use one chart as a filter, the other chart is updated to reflect the effect of selecting this new filter not only by excluding any elements (slices, bars, or columns) that are filtered out, but also by showing the proportion of data excluded by the filter.

As an example of this, create a pie chart of cost price by color and a column chart of direct costs by vehicle type. We will then cross-filter the two charts and see the results. The steps to follow are

Often when developing a visualization whose main objective, after all, is to help you to see through the fog of data into the sunlit highlands of comprehension, profit, or indeed, whatever is the focus of your analysis, you may feel that you cannot see the forest for the trees. This is where Power BI Desktop’s ability to highlight data in a chart visualization can be so effective.

Let’s take a visualization that contains a lot of information; in this example, it is a bubble chart of vehicle types. Indeed, in this example, an audience might think that there is so much data that it is difficult to see the bubbles for specific makes of car, and so analyze the uniqueness for sales data by make. Power BI Desktop has a solution to isolate a data series in such a chart. To see this in action and to make the details clearer, you need to do as follows:

This technique needs a few comments:

Column charts and bar charts can also be used to filter a Power BI Desktop report on two elements simultaneously. The only limitation is that you can only have one set of numeric data as the values for the chart. If the bar or column chart is a stacked bar, then you can click any of the sections in the stacked bar. In addition, if the chart is a clustered bar or column, you can click any of the columns in a group to slice by the elements represented in that section.

If this limitation is not a problem, then this is how you can use bar or column charts (whether they are clustered, stacked, or 100% stacked) to apply double filters to a report:

Once tweaked to clarify the appearance of the chart, the net result should look like Figure 21-19.

Clicking any segment of a bar filters and highlights other visualizations on the same report for that country and car age range. An example of this is given in Figure 21-20, where the car age range of 21–25 has been selected for the USA bar.

Clicking any car age range in the legend will filter by car age range only. You see this in Figure 21-21, where the legend item for 16-20 has been selected.

So in fact, you can choose to filter on a single element or multiple elements, depending on whether you use the chart or the legend as the filter source.

The timeline slicer lets you drag the upper and lower limits of a date range. You can also define the date element (year, quarter, month, week, or day) to use interactively. You can see this in Figure 21-27.

A timebrush slicer allows you to highlight a section of a time-ordered dataset. You can see this in Figure 21-28.

A chicklet slicer is a slicer that you can arrange and format in a multitude of ways. You can see a simple example in Figure 21-29.

This particular slicer really does add a multitude of options. I strongly recommend that you take a look at all that it can do.