Data preparation

Analyzing Data in Tableau

Lis Sulmont

Head of Curriculum Expansion at DataCamp

Data preparation

Ask yourself...

  • Do any fields need to be refined?

  • Are there calculated fields we can create to more effectively tell our data story?

  • Does the data contain fields that will allow for summaries or grouping at a higher level?

  • Are there sufficient categorical fields to slice and dice your data?

Analyzing Data in Tableau

Divvy bike station

1 Photo credit: Arvell Dorsey Jr. from Chicago, IL, United States
Analyzing Data in Tableau

Divvy dataset: stations table

divvy_bikestation.jpg

  • id: ID attached to each station
  • name: station name
  • latitude: station latitude
  • longitude: station longitude
  • docks: number of docks at the station
Analyzing Data in Tableau

Divvy dataset: trips table

  • Trips taken between Jan - June, 2019
  • trip id: ID attached to each trip
  • bikeid: ID attached to each bike
  • tripduration: time of trip in seconds
  • starttime: day and time trip started (CST)
  • endtime:day and time trip ended (CST)
  • from station id: station ID of trip start
  • from_station_name: station name of start
  • to station id: station ID of trip end

map.png

  • to station name: station name of end
  • usertype: customer or subscriber
  • birthyear: birth year of rider
  • gender: gender of rider
Analyzing Data in Tableau

Dimension and measure recap

Dimensions:

  • Categorical or qualitative data

Measures:

  • Numerical data that can be aggregated

We want to move fields strategically between these two types:

  • Move numeric fields that shouldn't be aggregated to the Dimensions section
Analyzing Data in Tableau

Let's practice!

Analyzing Data in Tableau

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