Basics of Reading Data


1. Understanding Basic Data Concepts

1-1. Data Sources

>> Common types of data sets

  • Spreadsheets:
    • e.g.: Excel, Google Sheets
    • the records are stored as single rows of data
  • Relational Databases:
    • store data in multiple tables
    • “relational”: logical connection between tables
    • users pull data from different tables using SQL
  • Cloud Data:
    • e.g.: AWS, Microsoft Azure, Salesforce
  • Other Types:
    • .kml, .shp, created in R

1-2. Data Field

>> A field = A column

>> Data Field automatically assigned a Role and a Type

  • Role: “Dimension” or “Measure
    • Dimension: qualitative fields / (categorical data) * blue in tableau
    • Measure: quantitative fields / (numerical data) * green in tableau
  • Type: String, Integer, Date, Date&Time, Boolean, Geographic, Mixed or cluster


2. Understanding Data Structure Details

2-1. Granularity and Aggregation in Tableau

>> Data granularity(数据粒度): the level of detail for a piece of data

  • Less granular: describe as an aggregation / aggregated data

>> move dimensions & measures in /out of a view --> level of details changes

  • Dimensions: break down aggregated total by category
  • Measures: aggregated as SUM (default), or average, median…

>> “SUM(Profit)/SUM(Sales)” VS "Profit/Sales"

Caution the trap of granularity when aggregating

  • “SUM(Profit) / SUM(Sales)” [correct]
    1. first sums the profits and sales to whatever the granularity of the view is
    2. then computes the ratio at that aggregation
  • “Profit / Sales” [incorrect]
    1. first compute the profit ratio at the lowest level of granularity
    2. then sum the ratio to the requested aggregation of the view

2-2. How data is represented in Tableau

1. Dimensions & Measures

  • Measures: aggregations

    - aggregated up to the granularity set by the dimensions in the view

  • Dimensions: categorical fields

    - set the granularity, or the level of detail


2. Discrete & Continuous

Discrete Continuous
Value have distinct, separate values take on any value in a range
Color Blue Green
Label
vs
Axis
Label

image-20210517161223334

Market(discrete)
Axis

image-20210517160710233

profit (continuous)
Color Color Palette

image-20210517161958731

SUM(Sales - discrete)
Color Gradient

image-20210517162028994

SUM(Sales - continuous)
Color
&
Maps
A Dimension on color – "Symbol Map"

image-20210517162753289
A Measure on color – "Filled Map"

image-20210517162915695
A Dimension on color – "Symbol Map"

image-20210517163002064
A Measure on color – "Filled Map"

image-20210517163033554
Dates image-20210517163347483 image-20210517163408062
Filtering List

image-20210517163617518
Range

image-20210517163720510


3. Reading Common Chart Types

3-1. Overview to reading charts

>> Elements of Charts

  • Quantitative Axis / Qualitative Axis
  • Marks (View화면에서 Data를 표현하는 도구)
  • Labels (축에 표시되는 값)
  • Filter (Side bar)
  • Legend (Side bar)
  • Tooltip (show details about the data when clicking a mark)

>> Appropriate Purpose

  • Bar Charts: Comparing categories of data

  • Line Charts: Viewing data over time

  • Scatter Plots: Viewing data relationships and outliers