Shaffer Jeffrey

The Big Book of Dashboards


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rel="nofollow" href="#x_9_i158"> Figure 1.26 Scatterplot simulating color vision deficiency for someone with protanopia.

      One color combination that is frequently overlooked is blue and purple together. In a RGB (red-green-blue) color model, purple is achieved by using blue and red together. If someone with CVD has issues with red, then he or she may also have issues with purple, which would appear to look like blue. Other color combinations can be problematic as well. For example, people may have difficulty with pink or red used with gray or gray used together with brown.

Figure 1.27 shows another scatterplot, this time using blue, purple, magenta, and gray. When applying deuteranopia simulation, the dots in the scatterplot appear to be a very similar color of gray.

Scatterplot shows sales versus discount for normal (highlighted in different colors) and deuteranopia CVD simulation (similar color of gray) for central, east, south, and west.

Figure 1.27 Scatterplot simulating color vision deficiency for someone with deuteranopia.

      It's important to understand these issues when designing visualizations. If color is used to encode data and it's necessary for readers to distinguish among colors to understand the visualization, then consider using color-blind-friendly palettes. Here are a few resources that you can use to simulate the various types of CVD for your own visualizations.

      Adobe Illustrator CC. This program offers a built-in CVD simulation in the View menu under Proof Setup.

      Chromatic Vision Simulator (free). Kazunori Asada's superb website allows users to upload images and simulate how they would appear to people with different form of CVD. See http://asada.tukusi.ne.jp/webCVS/

      NoCoffee vision simulator (free). This free simulator for the Chrome browser allows users to simulate websites and images directly from the browser.

      Common Chart Types

      In this book, you will see many different types of charts. We explain in the scenarios why many of the charts were chosen to fulfill a particular task. In this section, we briefly outline the most common chart types. This list is intentionally short. Even if you use only the charts listed here, you would be able to cover the majority of needs when visualizing your data. More advanced chart types seen throughout the book are built from the same building blocks as these. For example, sparklines, which are shown in Chapters 6, 8, and 9, are a kind of line chart. Bullet charts, used in Chapter 17, are bar charts with reference lines and shading built in. Finally, waterfall charts, shown in Chapter 24, are bar charts where the bars don't have a common baseline.

      Bar Chart

A bar chart (see Figure 1.28) uses length to represent a measure. Human beings are extremely good at seeing even small differences in length from a common baseline. Bars are widely used in data visualization because they are often the most effective way to compare categories. Bars can be oriented horizontally or vertically. Sorting them can be very helpful because the most common task when bar charts are used is to spot the biggest/smallest items.

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Figure 1.28 Bar chart.

      Time-Series Line Chart

Line charts (see Figure 1.29) usually show change over time. Time is represented by position on the horizontal x-axis. The measures are shown on the vertical y-axis. The height and slopes of the line let us see trends.

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Figure 1.29 Time-series line chart.

      Scatterplot

A scatterplot (see Figure 1.30) lets you compare two different measures. Each measure is encoded using position on the horizontal and vertical axes. Scatterplots are useful when looking for relationships between two variables.

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Figure 1.30 Scatterplot.

      Dot Plot

A dot plot (see Figure 1.31) allows you to compare values across two dimensions. In our example, each row shows sales by ship mode. The dots show sales for each ship mode, broken down by each segment. In the example, you can see that corporate sales are highest with standard class ship mode.

Dot plot shows ship mode (first class, same day, second class, standard class) versus sales (0 to 20,000 dollars, intervals of 10,000) for consumer, corporate, and home office.

Figure 1.31 Dot plot.

      Choropleth Map

A choropleth (also known as a filled) map (see Figure 1.32) uses differences in shading or coloring within predefined areas to indicate the values or categories in those areas.

Choropleth map shows sales ranging from 0 to 500, 000 dollars by using different shades of color.

Figure 1.32 Choropleth map.

      Symbol Map

A symbol map (see Figure 1.33) shows values in specific places. These could be the center points of large regions (e.g., the center of each U.S. state) or specific locations determined by an exact latitude/longitude measurement.

Symbol map shows circles ranging from small to large representing different values of sales in dollars ranging from 900, 100,000, 200,000, 300,000, 400,000, and 500,000.

Figure 1.33 Symbol map.

      Avoid pie charts

      Why isn't there a pie chart? Pie charts are common charts, but they are flawed. We don't recommend you use them. Check out the section titled “When Our Visual Processing System Betrays Us” for details.

      Table

Sometimes you do need to be able to look up exact values. A table (see Figure 1.34) is an acceptable way to show data in that situation. On most dashboards, a table shows details alongside summary charts.

Table shows 3 rows and 4 columns highlighted in different shades of color with values in dollars: In row 1- 111k, 131k, 138k, 154k In row 2- 132k, 117k, 157k, 215k In row 3- 77k, 68k, 79k, 106k

Figure 1.34 Table.

      Highlight Table

Adding a color encoding to your tables can transform them into highly visual views that also enable exact lookup of any value. (see Figure 1.35.)

Table shows 3 rows and 4 columns with values in dollars: In row 1- 111k, 131k, 138k, 154k In row 2- 132k, 117k, 157k, 215k In row 3- 77k, 68k, 79k, 106k

Figure 1.35 Highlight table.

      Bullet Graph

A bullet graph (see Figure 1.36) is one of the best ways to show actual versus target comparisons. The blue bar represents the actual value, the black line shows the target value, and the areas of gray shading are performance bands.