Week 2: Data Visualization - STAT 288 PDF

STAT 288 Data Visualization Dr Abdulla Eid Chapter 2, 3, and 4  CHAPTER 2 Data Visualization :Mapping Data Onto Aesthetics  Data Visualization Convert data values in a systematic and logical way into the visual elements that make up the final graphic All data visua...

STAT 288 Data Visualization Dr Abdulla Eid Chapter 2, 3, and 4  CHAPTER 2 Data Visualization :Mapping Data Onto Aesthetics  Data Visualization Convert data values in a systematic and logical way into the visual elements that make up the final graphic All data visualizations (pie, bar, histogram, etc) map data values into quantifiable features of the resulting graphic – these features are aesthetics Aesthetics and types of data  Aesthetics All aesthetics fall into one of two groups Continuous Continuous Discrete  Data Types quantitative/numerical continuous quantitative/numerical discrete Continuous qualitative/categorical unordered qualitative/categorical ordered date or time text  Example of data type Month Day Location Station ID Temperature Jan 1 Chicago USW00014819 25.6 The Table shows the first few rows of Jan 1 San Diego USW00093107 55.2 a dataset providing the daily Jan 1 Houston USW00012918 53.9 temperature (average daily Jan 1 Death Valley USC00042319 51.0 temperatures over a 30-year Jan 2 Chicago USW00014819 25.5 window) for four U.S. locations. Jan 2 San Diego USW00093107 55.3 Jan 2 Houston USW00012918 53.8 Jan 2 Death Valley USC00042319 51.2 This table contains five variables: Jan 3 Chicago USW00014819 25.3 month, day, location, station ID, and Jan 3 San Diego USW00093107 55.3 temperature (in degrees Fahrenheit). Jan 3 Death Valley USC00042319 51.3 Jan 3 Houston USW00012918 53.8  2.2 Scales map data values onto aesthetics To map data values onto aesthetics, we need to specify which data values correspond to which specific aesthetics values. For example, if our graphic has an x axis, then we need to specify which data values fall onto particular positions along this axis. Similarly, we may need to specify which data values are represented by particular shapes or colors. This mapping between data values and aesthetics values is created via scales. Example: Map Data to Aesthetics Another mapping 03 - SOCIAL MEDIA How many scales we are using? 5 03 - SOCIAL MEDIA  Position Scale It determines where in a graphic different data values are located. We cannot visualize data without placing different data points at different locations, even if we just arrange them next to each other along a line The combination of a set of position scales and their relative geometric arrangement is called a coordinate system CHAPTER 3 Coordinate System And Axes 3.1 Cartesian coordinates What can you say about these three visualization? 3.2 Non - Linear Scale 3.3 Coordinate systems with curved axes Example of Curves Coordinate System Different System (Subject specific) CHAPTER 4 Color Scales  Color Scale There are three fundamental use cases for color in data visualizations: –(i) we can use color to distinguish groups of data from each other –(ii) we can use color to represent data values –(iii) we can use color to highlight. 4.1 Color as a tool to distinguish  Qualitative Color Scale We frequently use color as a means to distinguish discrete items or groups that do not have an intrinsic order, such as different countries on a map or different manufacturers of a certain product. In this case, we use a qualitative color scale. Such a scale contains a finite set of specific colors that are chosen to look clearly distinct from each other while also being equivalent to each other. The second condition requires that no one color should stand out relative to the others. And, the colors should not create the impression of an order, as would be the case with a sequence of colors that get successively lighter. 4.2 Color to represent data values Sequential Color Color can also be used to represent data values, such as income, temperature, or speed. We use a sequential color scale. Such a scale contains a sequence of colors that clearly indicate –(i) which values are larger or smaller than which other ones –(ii) how distant two specific values are from each other. 4.3 Color as a tool to highlight  Accent Colors Color can also be an effective tool to highlight specific elements in the data. There may be specific categories or values in the dataset that carry key information about the story we want to tell. This effect can be achieved with accent color scales, which are color scales that contain both a set of subdued colors and a matching set of stronger, darker, and/or more saturated colors