![Commonly used aesthetics in data visualization: position, shape, size, color, line width, line type. Some of these aesthetics can represent both continuous and discrete data (position, size, line width, color) while others can usually only represent discrete data (shape, line type).](img/171cbd0fc5aa63677c7b342755b11199.jpg)
<caption>Table 2.1: Types of variables encountered in typical data visualization scenarios.</caption><colgroup><colwidth="21%"><colwidth="18%"><colwidth="20%"><colwidth="39%"></colgroup>
要查看这些不同类型数据的具体示例,请查看表 [2.2](aesthetic-mapping.html#tab:data-example) 。它显示了数据集的前几行,它们提供了美国四个地点的日常温度法线(30 年窗口的平均日常温度)。此表包含五个变量:月,日,位置,站 ID 和温度(以华氏度为单位)。月是有序因子,日是离散数值,位置是无序因子,站 ID 同样是无序因子,温度是连续数值。
要查看这些不同类型数据的具体示例,请查看表 [2.2](aesthetic-mapping.html#tab:data-example) 。它显示了数据集的前几行,它们提供了美国四个地点的日常温度法线(30 年窗口中的平均日常温度)。此表包含五个变量:月,日,位置,站点 ID 和温度(以华氏度为单位)。月是有序因子,日是离散数值,位置是无序因子,站点 ID 同样是无序因子,温度是连续数值。
<caption>Table 2.2: First 12 rows of a dataset listing daily temperature normals for four weather stations. Data source: NOAA.</caption>
![Scales link data values to aesthetics. Here, the numbers 1 through 4 have been mapped onto a position scale, a shape scale, and a color scale. For each scale, each number corresponds to a unique position, shape, or color and vice versa.](img/be47af5e107ba940309be44590dcd739.jpg)
![Daily temperature normals for four selected locations in the U.S. Temperature is mapped to the y axis, day of the year to the x axis, and location to line color. Data source: NOAA.](img/6cef3423ab58eeef6e4beafc2446230f.jpg)
![Fuel efficiency versus displacement, for 32 cars (1973–74 models). This figure uses five separate scales to represent data: (i) the x axis (displacement); (ii) the y axis (fuel efficiency); (iii) the color of the data points (power); (iv) the size of the data points (weight); and (v) the shape of the data points (number of cylinders). Four of the five variables displayed (displacement, fuel efficiency, power, and weight) are numerical continuous. The remaining one (number of cylinders) can be considered to be either numerical discrete or qualitative ordered. Data source: Motor Trend, 1974.](img/13590d652ef7ad30e2c706d1d7918fb7.jpg)
Stone, M., D. Albers Szafir, and V. Setlur. 2014. “An Engineering Model for Color Difference as a Function of Size.” In 22nd Color and Imaging Conference. Society for Imaging Science and Technology.