🕶 Science, Human Experience, Experiments, and Data

Where does Data come from?

Let us look at the slides. Click on the icon above:

knitr::include_url("10-Nature-of-Data/10-Nature-of-Data.html")

Why Visualize?

We can digest information more easily when it is pictorial
Our Working Memories are both short-term and limited in capacity. So a picture abstracts the details and presents us with an overall summary, an insight, or a story that is both easy to recall and easy on retention.
Data Viz includes shapes that carry strong cultural memories and impressions for us. These cultural memories help us to use data viz in a universal way to appeal to a wide variety of audiences. (Do humans have a gene for geometry?)
It helps sift facts and mere statements: for example:

Figure 1: Source https://www.deccanherald.com/national/india-is-known-as-the-rape-capital-of-the-world-rahul-783495.html

Figure 2: Source https://datareveals.org/crime-data/

What are Data Types??

https://www.youtube.com/watch?v=dwFsRZv4oHA

In more detail:

How do we Spot Data Variable Types?

By asking questions!

Pronoun	Answer	Variable / Scale	Example	What Ope rations?
What, Who, Where, Whom, Which	Name, Place, Animal, Thing	Qua litative / N ominal	Name	Count no. of cases Mode
How, What Kind, What Sort	A Manner / Method, Type or Attribute from a list, with list items in some ” order**” ( e.g. good, better, improved, best..)	Qua litative / O rdinal	Socio -economic status (“low income, middle income, high income) education level (“high school”, “B S”,” M S”,“PhD”) income level (“less than 50K”, “50K-100K”, “over 100K”) S atisfaction rating ( “extremely dislike”, “dislike”, “neutral”, “like”, “extremely like”).	Median Per centiles
How Many / Much / Heavy? Few? Seldom? Often? When?	Quantities with Scale. Differences are meaningful, but not products or ratios	Quan titative / In terval	pH SAT score (200-800), - Credit score (300-850). - Year of Starting in College	Mean Standard D eviation
How Many / Much / Heavy? Few? Seldom? Often? When?	Quantities, with Scale and a Zero Value. Differences and Ratios /Products are meaningful. (e.g Weight )	Quan titative / R atio**	Weight,le ngth,Height Temperature in Kelvin Enzyme activity, dose amount, reaction rate, flow rate,co ncentration Pulse Survival time	Cor relation Coeff of V ariation

As you go from Qualitative to Quantitative data types in the table, I hope you can detect a movement from fuzzy groups/categories to more and more crystallized numbers. Each variable/scale can be subjected to the operations of the previous group. In the words of S.S. Stevens ,

the basic operations needed to create each type of scale is cumulative: to an operation listed opposite a particular scale must be added all those operations preceding it.

What Are the Parts of a Data Viz?

How to pick a Data Viz?

Most Data Visualizations use one or more of the following geometric attributes or aesthetics. These geometric aesthetics are used to represent qualitative or quantitative variables from your data.

Figure 3: From Claus Wilke, Fundamentals of Data Visualization

What does that mean? We can think of simple visualizations as combinations of these aesthetics. Some examples:

Aesthetic #1	Aesthetic #2	Shape
Position X = Quant Variable	Position Y = Quant Variable	Points/Circles with Fixed Size
Position X = Qual Variable	Position Y = Count of Qual var)	Columns
Position X = Qual Variable	Position Y = Qual Variable	Rectangles, with area proportional to joint(X,Y) count
Position X = Qu alitative Variable	Position Y = Rank Ordered Quant Variable	Box + Whisker, Box length proportional to Inter-Quartile Range, whisker-length proportional to upper and lower quartile resp.
Position X = Quant Variable	Postion Y = Quant V ariable + Qual Var
Quant Variable	Shape = Line with Quant Variable