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# 统计代写|数据可视化代考DATA VISUALIZATION代考|CS7295 Data-Ink Ratio

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## 统计代写|数据可视化代考DATA VISUALIZATION代考|Data-Ink Ratio

The concepts of preattentive attributes and Gestalt principles are valuable in understanding features that can be used to visualize data and how visualizations are processed by the mind. However, it is easy to overuse any of the features and diminish the effectiveness of the feature to differentiate and draw attention. A guiding principle for effective data visualizations is that the table or graph should illustrate the data to help the audience generate insights and understanding. The table or graph should not be so cluttered as to disguise the data or be difficult to interpret.

A common way of thinking about this principle is the idea of maximizing the data-ink ratio. The data-ink ratio measures the proportion of “data-ink” to the total amount of ink used in a table or chart, where data-ink is the ink used that is necessary to convey the meaning of the data to the audience. Non-data-ink is ink used in a table or chart that serves no useful purpose in conveying the data to the audience. Note in Figure 3.11a that the pie chart uses color and a legend to differentiate between the eight managers. The bar chart in this figure communicates the same information without either of these features, and so has a higher data-ink ratio.

Let us consider the case of Diaphanous Industries, a firm that produces fine silk clothing products. Diaphanous is interested in tracking the sales of one of its most popular items, a particular style of scarf. Table $3.1$ and Figure $3.20$ provide examples of a table and chart with low data-ink ratios used to display sales of this style of scarf. The data used in this table and figure represent product sales by day. Both of these examples are similar to tables and charts generated with Excel using common default settings. In Table 3.1, most of the gridlines serve no useful purpose. Likewise, in Figure 3.20, the gridlines in the chart add little additional information. In both cases, most of these lines can be deleted without reducing the information conveyed. However, an important piece of information is missing from Figure 3.20: titles for axes. Generally, axes should always be labeled in a chart. There are rare exceptions to this where both the meaning and unit of measure are obvious such as when the axis displays the names of months (i.e., “January,” “February,” “March,” etc.). For most charts, we recommend labeling the axes to avoid the possibility of misinterpretation by the audience and to reduce the cognitive load required by the audience.

## 统计代写|数据可视化代考DATA VISUALIZATION代考|Minimizing Eye Travel

Data visualizations should be easy to view and interpret by the audience. Charts and tables should reveal insights to the audience, while minimizing the cognitive load required of the audience. We can minimize cognitive load by using preattentive attributes and Gestalt principles as well as by increasing the data-ink ratio in our data visualizations. We can also minimize cognitive load by minimizing the eye travel required by the audience.
Consider the Office of Budget and Performance Improvement for the City of Springfield. This city office would like to compare the performance of the two police districts located in its city. One performance metric used by the city is clearance rate, which is the fraction of reported crimes that result in an arrest. Figure $3.25$ compares the clearance rates for property crimes in Springfield’s District 1 and District 2 over the last 6 months.

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。