uwp barcode scanner c#

uwp barcode scanner c#

uwp barcode scanner c#

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uwp barcode scanner c#,
uwp barcode scanner c#,
uwp barcode scanner c#,
uwp barcode scanner c#,
uwp barcode scanner c#,
uwp barcode scanner c#,
uwp barcode scanner c#,
uwp barcode scanner c#,
uwp barcode scanner c#,

Figure B-7. A SQL Server 2008 R2 gauge compared to a bullet graph rendered with the same measure and range values. Looking at Figure B-7, you might be thinking to yourself that there is not much of a difference between the two visualizations. The dashboard gauge looks nicer and has a factor of glitz. Conversely, the bullet graph looks like a simpler visualization that may have come out of Microsoft Excel. You may also find the bullet graph slightly easier to read, because the axes are linear and not circular. However, the design of the bullet graph has some additional benefits over a traditional dashboard gauge: Smaller form factor: the form factor of the bullet graph is much smaller than the gauge. Surfacing one or two gauges usually is not a problem. However, when you want to visualize several metrics on a single dashboard a smaller form factor has distinct advantages. Qualitative ranges do not rely on colors: As you have learned in the previous sections about the bullet graph, you know that the bullet graph relies on different shades of a single color to define the good, satisfactory, and bad ranges. This has multiple advantages. First, color blind users are not affected by different colors. If you are a color blind, you can still make out the strength/hue of a color. This allows a color blind person to determine which range the featured or comparative measures fall in. Since bullet graphs do not rely on a rich color palette, these controls can be printed using black and white printers and still deliver analytical insight effectively.

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The code sample for this recipe builds on the sample from Recipe 4-4. That recipe has a simple spending analysis application for a family; the expenditures for different categories are maintained in a DataGrid and also graphed in a bar graph as a percentage of the total. The application allows you to change the spending in each category to different values and watch the graph change accordingly. It also lets you drag any bar in the graph using your mouse and watch the corresponding value change in the DataGrid, maintaining the same total. To adapt that sample to this recipe, you break it into two separate applications. The application named 7.8 HomeExpenseWorksheet encapsulates the DataGrid-based worksheet portion of the sample, whereas the 7.8 HomeExpenseGraph application encapsulates the bar-graph implementation. You then use local-connection-based messaging between the two applications to implement the necessary communication. Figure 7-19 shows the applications hosted on the same page.

Rendering Performance: The bullet graph control is a much simpler control than a traditional dashboard gauge. There are far less components. Furthermore, rendering components such as shadows, 3D effects, and borders does not take place. The simplicity of the bullet graph control has a dramatic impact on rendering performance when compared to a traditional dashboard gauge. Word-Sized Rendering: In the data visualization chapters, you learned about the analytical significance of word-sized charts (sparklines). This type of analytical insight can be presented in a bullet graph. By turning off the scale and quantitative ranges you can shrink down the size of a bullet graph and still deliver business intelligence insight in a word-sized visualization. More substance and less filler: If you look at Figure B-7 in detail, you can appreciate how much simpler the bullet graph control is over its gauge counterpart. This makes the implementation much easier on the eyes. Furthermore, this allows the user to focus on the gaining insight since every component symbolizes an important range, measure, or value.

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