#workoutwednesday | Data Visualization | Tableau

#WorkoutWednesday wk24: Do customers spend more on their First or Second Purchase

This week’s #workoutwednesday by Ann Jackson was to prepare dataset using Tableau Prep then to design visualisation to show customers spend more on their first or second purchase.

The list of full requirements can be found here but on a high level:

  • Create a dataset using Tableau prep which will return customer level first and second order information with regards to sales, no. of categories and no. of products sold in the order
  • Dataset will have 9 columns i.e. Customer id, 1st Purchase Date, 2nd Purchase Date, 1st Purchase Sum Sales, 2nd Purchase Sum Sales, 1st Purchase # Categories, 2nd Purchase # Categories, 1st Purchase # Products, 2nd Purchase # Products
  • Create scatter plot with First purchase sales vs second purchase sales with 2 strip plots
  • Sheets aligned in dashboard using float method

It was good reason to try out Tableau prep for first time and create the dataset for the challenge. Here is how my workflow looked like in Tableau prep:

developing the viz was pretty straight forward with scatter and strip plot but the tricky part was to arrange all the sheets into float order with pixel perfect output. Took a bit of time to arrange but was able to make it at last. Overall, enjoyed working on Tableau Prep. I will be writing on Tableau prep in coming days to explain how it works.

Here is my output for the challenge (Click on Image for interactive version)

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#MakeoverMonday | Data Visualization | Tableau

MakeoverMonday wk24: Tourism density index

For Makeovermonday week 24, it was about Tourism density index i.e. how many tourists come to country compare to population.

Here is the original viz by Intrepid Travel:

Data is available on data.world week 24:

Here is what I did:

  • Got the idea of ratio from Eva’s Blog and tried to show number of tourists per 100 local people
  • Used rows and columns method using groups in dataset (inspired by Andy’s output)
  • Make it easier to see for each country given in dataset

Click on the viz to use the interactive version.

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#workoutwednesday | Data Visualization | Tableau

#Workout Wednesday 23 – Sales comparison in the current period to the previous

This week’s #workoutwednesday by Rody Zakovich was about playing with dates. challenge was about comparing current period with previous period of same range of days.

Requirements:

  • User can select a Start and End date for the Current Period
  • The “Previous Period” contains the same number of days as the Current Period, and ends the Day before the Start Date
  • Both the “Current” and “Previous” Periods must be on the same Date Axis (No dual Axis!)
  • The Current Period must be distinguishable from the previous period
  • Must show the Current Period Range, as well as, the Prior Period Range

This week data uses the superstore dataset

Here is my solution with the requirements given:

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#MakeoverMonday | Data Visualization | Tableau

Makeover Monday wk23: UK Gender pay gap Female vs Male

This week in #makeovermonday, it was tough and sensitive topic on Gender pay gap in UK.

Data was shared by Gov.uk

Here is the original report and how it looks:

Graphic displaying the mean bonus pay gap for HMRC

 

Data is available on data.world week 23.

Here is what I did:

  • First attempt I tried to create few calculations and pivot the data within Tableau to create bar charts which did not yield any productive output
  • I tried working with scatter plots and various other form of visualisation but was missing something
  • Final attempt was to create a gantt chart to show difference in male to female ratio in different pay scale quartile.

Below is the screenshot of Tableau file (click on Image for interactive version):

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#workoutwednesday | Data Visualization | Tableau

#Workout Wednesday 22 – Using Table calcs

This week’s #workoutwednesday was about a problem that can only be solved using table calculations. Idea was to find which city contributes the most sales to each states.

Requirements

  • Use only table calculations
  • The bar length is the total sales of each state
  • City must be included in the view.
  • Display only one mark per state.
  • Label each bar by the city with the highest sales, sales for that city, and the total sales for that state.
  • No level of detail calculations allowed.

This week uses the superstore dataset.  You can get it here at data.world

Below is my attempt to design solution with the above requirements:

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