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CS代写|数据可视化代写Data visualization代考|Trying to Display Too Much Information

Another common mistake in building effective charts is trying to convey too much information on a single chart, which is a symptom of trying to communicate too many insights to the audience simultaneously.

Consider the case of Keeland Industries, an online company that provides replacement parts for automobiles. It provides both original-equipment manufacturer (OEM) replacement parts and replacement parts made by different manufacturers that are known as aftermarket replacement parts. Because Keeland sells the replacement parts online, it sells parts to customers throughout the United States. For sales tracking and performance measurement purposes, Keeland divides the United States into 12 regions. Keeland’s management team is most interested in comparing the OEM sales across the 12 regions to see which are performing best for OEM sales and in comparing the Aftermarket sales across the 12 regions to see which are performing best for Aftermarket sales.

Figure $3.30$ displays the OEM and Aftermarket sales by region as a clustered column chart. The clustered column chart makes it easy to compare OEM sales to Aftermarket sales in each region. However, if the potential markets for these different types of parts are different in each region, that comparison is not particularly useful.
Figure $3.31$ displays the OEM and Aftermarket sales by region as a stacked column chart. The stacked column chart makes it much easier to compare the total sales in the different regions. Therefore, a stacked column chart would be a good choice if the goal is to compare total sales (OEM plus Aftermarket) among the 12 regions. However, the stated goal of this visualization is to compare the OEM sales across regions separately from the Aftermarket sales. The best visualization to accomplish the stated goal is to use two separate column charts as shown in Figures $3.32$ and $3.33$.

CS代写|数据可视化代写Data visualization代考|Using Excel Default Settings for Charts

Microsoft Excel allows for the creation of a variety of charts and tables to visualize data. However, a common mistake is to use the default output from Excel without considering changes to the design and format of the visualizations it produces. Excel’s default settings are counter to many of the suggestions covered in this chapter (and the rest of this textbook) for creating good data visualizations. Consider Figure 3.34. This column chart, which was produced using Excel, shows revenues for eight retail store locations in Texas. The company is interested in comparing revenues by location, and specifically in examining the relative performance of the store located in Laredo because this store has recently had a change in management.

Figure $3.34$ suffers from several flaws that prevent it from being an effective data visualization. The data-ink ratio for Figure $3.34$ is low, so we should consider ways of decluttering the figure. Examining Figure $3.34$ shows that the chart uses ink in several ways that are not useful in conveying the data. The gridlines used in this chart are not particularly useful, so they can be removed. We see that Excel automatically titles the chart “Annual Revenue” and uses a legend with “Annual Revenue.” This is redundant information, and at least one of these labels should be removed. The following steps can be uscd to declutter the default chart produccd by Excel, increasc the data-ink ratio, and make the chart more meaningful to the audience.
Step 1. Click anywhere on the chart in the file RetailRevenueChart
Step 2. Click the Chart Elements button $+$
Deselect the check box for Gridlines
Deselect the check box for Legend
Steps 1 and 2 increase the data-ink ratio by decluttering the chart. We can further improve this chart by adding meaningful ink to the chart and making a few other modifications. For example, the revenue values shown in this chart are from the previous year and are in 1000s of dollars. None of this is clear from the chart. To make it easier for the audience to compare the relative amounts of annual revenue by location, we can sort the columns in decreasing order. Finally, because the audience is particularly interested in annual sales at the Laredo location, we can change the color of the column associated with Laredo to draw the audience’s attention to that part of the chart. The following steps create the finished column chart shown in Figure 3.35.

CS代写|数据可视化代写Data visualization代考|BINF7003

数据可视化代考

CS代写|数据可视化代写Data visualization代考|Trying to Display Too Much Information

建立有效图表的另一个常见错误是试图在单个图表上传达太多信息,这是试图同时向观众传达太多见解的症状。

以 Keeland Industries 为例,这是一家为汽车提供替换零件的在线公司。它提供原始设备制造商 (OEM) 替换零件和由不同制造商制造的替换零件,称为售后替换零件。由于 Keeland 在线销售替换零件,因此它向美国各地的客户销售零件。出于销售跟踪和绩效衡量的目的,Keeland 将美国划分为 12 个地区。Keeland 的管理团队最感兴趣的是比较 12 个地区的 OEM 销售情况,看看哪些地区的 OEM 销售表现最好,并比较 12 个地区的售后市场销售情况,看看哪些地区的售后市场销售表现最好。

数字3.30将按地区划分的 OEM 和售后市场销售额显示为簇状柱形图。簇状柱形图便于比较每个地区的 OEM 销售与售后市场销售。但是,如果这些不同类型零件的潜在市场在每个地区都不同,那么这种比较就不是特别有用。
数字3.31将按地区划分的 OEM 和售后市场销售额显示为堆积柱形图。堆积柱形图使比较不同地区的总销售额变得更加容易。因此,如果目标是比较 12 个地区的总销售额(OEM 加售后市场),堆叠柱形图将是一个不错的选择。但是,此可视化的既定目标是将跨区域的 OEM 销售与售后市场销售分开进行比较。实现既定目标的最佳可视化是使用两个单独的柱形图,如图所示3.32和3.33.

CS代写|数据可视化代写Data visualization代考|Using Excel Default Settings for Charts

Microsoft Excel 允许创建各种图表和表格来可视化数据。但是,一个常见的错误是使用 Excel 的默认输出,而不考虑对其生成的可视化的设计和格式进行更改。Excel 的默认设置与本章(以及本教科书的其余部分)中关于创建良好数据可视化的许多建议背道而驰。考虑图 3.34。这个使用 Excel 生成的柱形图显示了德克萨斯州八个零售店位置的收入。该公司有兴趣按地点比较收入,特别是检查位于拉雷多的商店的相对业绩,因为这家商店最近发生了管理层变动。

数字3.34它存在几个缺陷,使其无法成为有效的数据可视化。图的数据墨水比3.34低,所以我们应该考虑整理图形的方法。检查图3.34显示图表以多种方式使用墨水,这些方式在传达数据时无用。此图表中使用的网格线不是特别有用,因此可以将其删除。我们看到 Excel 自动将图表命名为“年收入”,并使用带有“年收入”的图例。这是多余的信息,至少应该删除这些标签中的一个。可以使用以下步骤来整理Excel生成的默认图表,增加数据墨水比,使图表对观众更有意义。
步骤 1. 单击文件 RetailRevenueChart 图表上的任意位置
步骤 2. 单击图表元素按钮+
取消选中网格线复选框
取消选中图例复选框
步骤 1 和 2 通过整理图表来增加数据墨水比率。我们可以通过在图表中添加有意义的墨水并进行一些其他修改来进一步改进此图表。例如,此图表中显示的收入值来自上一年,单位为 1000 美元。从图表上看,这些都不是很清楚。为了让观众更容易按地点比较年收入的相对金额,我们可以按降序对列进行排序。最后,由于观众对拉雷多地点的年销售额特别感兴趣,我们可以更改与拉雷多相关的列的颜色,以将观众的注意力吸引到图表的那部分。以下步骤创建完成的柱形图,如图 3.35 所示。

CS代写|数据可视化代写Data visualization代考

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