Abstracting the data

Why abstract the data?

• Different attribute types 👉 different representations
• Different dataset types 👉 different idioms available

What do you need to abstract?

Types of Datasets

Tables

Temporal

• Tabular + time-related attribute
• Aggregations
• Cyclical
• Events or intervals
• Seasonality patterns

Networks

• Nodes + links
• Attributes on both
• Network idioms
• Network analytics

How are networks stored?

• Two files
• JSON file
• Adjacency matrix
• Generated from any data

Trees

• Hierarchical data
• A subset of networks
• Deserves its own class!
• Tree idioms
• Can be created out of tables

How are trees stored?

• Table with path attribute
• JSON file
• Generated from any data

Geometry

• Location data (usually latitutde/longitude)
• 2D or 3D
• Inherent shape
• Points, routes, shapes

Fields

• Continuous
• Sampled
• Example: brain scan

Data availability

• Static 👉 data don't change
• Dynamic 👉 data are constantly updating (e.g., stock prices)
• For dynamic data, you will need a data endpoint

Types of Attributes

Categorical

• No order
• E.g., names, countries, types
• Must be represented with visual channels that don't convey order

Ordinal

• Has implicit order
• But, you can't do arithmetic
• Can even be numerical
• E.g., t-shirt sizes, school years, rankings

Quantitative

• Also ordered
• You can perform arithmetic
• Can be divergent or sequential
• E.g., age, temperature, earnings

Ordering direction

Sequential

• The is a full range with a clear minimum
• You can perform arithmetic
• E.g., age, goals ⚽️, price 💰

Divergent

• There is a middle point
• And two opposite directions
• Sometimes the middle point is not zero
• E.g., temperature, earnings, political affiliation index

Cyclical

• There is a cycle in the data
• No starting point (?)
• Can be represented with cyclical channels
• E.g., Days of the week, hours in the day

Data Aggregation

Aggregating Spatial data

• County
• City
• State
• Country
• Zipcode? Can span multiple counties 🤷
• Custom areas

Aggregating Temporal data

• 2020 June 27 5:25PM
• 2020 June 27 5:00PM
• 2020 June 27
• 2020 June
• 2020 Q1
• 2020

Aggregating Temporal data

• 2020 June 27 5:25PM
• Week 26 of the year
• 5:00 - 6:00 PM
• Saturday
• June

What we learned

What we learned

• Abstracting the data
• Types of datasets
• Types of attributes
• Sequential, diverging, cyclical
• Data aggregation (temporal and geo)

Practice

Introduction to JS

JSON vs. CSV

price,brand,model,title_status,mileage,color,lot
21500,ford,f-150,clean vehicle,76876,gray,167783132
7500,nissan,door,clean vehicle,58126,black,167598085
10700,chevrolet,trax,clean vehicle,44280,red,167792666
23100,dodge,charger,clean vehicle,12265,silver,167735423
33800,dodge,durango,clean vehicle,15003,black,167734879
4140,ford,door,clean vehicle,40747,white,167656519
13900,nissan,rogue,clean vehicle,38760,white,167762102
15700,ford,cutaway,clean vehicle,75862,white,167780452
1150,ford,door,salvage insurance,123349,red,167652717
26100,ford,f-150,clean vehicle,32149,white,167741409
14000,ford,fusion,clean vehicle,50513,white,167749355
22500,ford,door,clean vehicle,43646,black,167780692

[
  {
    "price": 21500,
    "brand": "ford",
    "model": "f-150",
    "title_status": "clean vehicle",
    "mileage": 76876,
    "color": "gray",
    "lot": 167783132
  },
  {
    "price": 7500,
    "brand": "nissan",
    "model": "door",
    "title_status": "clean vehicle",
    "mileage": 58126,
    "color": "black",
    "lot": 167598085
  },
  {
    "price": 10700,
    "brand": "chevrolet",
    "model": "trax",
    "title_status": "clean vehicle",
    "mileage": 44280,
    "color": "red",
    "lot": 167792666
  },
  {
    "price": 23100,
    "brand": "dodge",
    "model": "charger",
    "title_status": "clean vehicle",
    "mileage": 12265,
    "color": "silver",
    "lot": 167735423
  },
  {
    "price": 33800,
    "brand": "dodge",
    "model": "durango",
    "title_status": "clean vehicle",
    "mileage": 15003,
    "color": "black",
    "lot": 167734879
  },
  {
    "price": 4140,
    "brand": "ford",
    "model": "door",
    "title_status": "clean vehicle",
    "mileage": 40747,
    "color": "white",
    "lot": 167656519
  },
  {
    "price": 13900,
    "brand": "nissan",
    "model": "rogue",
    "title_status": "clean vehicle",
    "mileage": 38760,
    "color": "white",
    "lot": 167762102
  },
  {
    "price": 15700,
    "brand": "ford",
    "model": "cutaway",
    "title_status": "clean vehicle",
    "mileage": 75862,
    "color": "white",
    "lot": 167780452
  },
  {
    "price": 1150,
    "brand": "ford",
    "model": "door",
    "title_status": "salvage insurance",
    "mileage": 123349,
    "color": "red",
    "lot": 167652717
  },
  {
    "price": 26100,
    "brand": "ford",
    "model": "f-150",
    "title_status": "clean vehicle",
    "mileage": 32149,
    "color": "white",
    "lot": 167741409
  },
  {
    "price": 14000,
    "brand": "ford",
    "model": "fusion",
    "title_status": "clean vehicle",
    "mileage": 50513,
    "color": "white",
    "lot": 167749355
  },
  {
    "price": 22500,
    "brand": "ford",
    "model": "door",
    "title_status": "clean vehicle",
    "mileage": 43646,
    "color": "black",
    "lot": 167780692
  }
]

Array functions (map, filter, sort)

Nesting, Folding, Pivoting, etc. in JS

Nesting, Folding, Pivoting, etc. in Tableau?