#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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#MakeoverMonday | Data Visualization | Tableau

#Makeover Monday wk21: EPL Prediction vs Actual

This week in #makeovermonday challenge was Sports data i.e. English Premier League data which looks into predictions and actual outcomes of the season 17-18.

Data was shared by the Guardian

Here is the original report and how it looks:

 

Data is available on data.world week 21.

Here is what I tried to do:

  • Used gann and circle chart from tableau to display the results of actual vs prediction and highlighted both of them using different colors to make it self explanatory.
  • This will help to make inference about how many prediction were on target and how many off target.

Below is the Tableau file:

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#workoutwednesday | Data Visualization | Tableau

#WorkoutWednesday Week 14 – Frequency Matrix

This week’s #workoutwednesday was about building frequency matrix using color to represent the frequency intensity.

Requirements

  • Use sub-categories
  • Dashboard size is 1000 x 900; tiled; 1 sheet
  • Distinctly count the number of orders that have purchases from both sub-categories
  • Sort the categories from highest to lowest frequency
  • White out when the sub-category matches and include the number of orders
  • Calculate the average sales per order for each sub-category
  • Identify in the tooltip the highest average spend per sub-category (see Phones & Tables)
  • If it’s the highest average spend for both sub-categories, identify with a dot in the square
  • Match formatting & tooltips – special emphasis on tooltip verbiage

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

Below is my attempt to meet the above requirements:

Thanks for reading 🙂

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#workoutwednesday | Data Visualization | Tableau

#WorkoutWednesday Week 13 – Color and ordering

This week’s #workoutwednesday was about comparing last 2 periods sales for a sub-category using date functions with customize tooltips.

Requirements

  • Dashboard size should be 1000×600.
  • Sub-Category on the Rows Shelf, and Year of Order Date on the Columns Shelf
  • There are 3 measures, Sales, % of Total (in year) and % Difference
  • Only the Sales Measure has color (which colors you use don’t matter, just that Sales is the only measure colored).
  • Users can select a Year and a Measure and sort the table in Descending order by that measure in that year.

Dataset used was superstore dataset available with Tableau

Below is my attempt to meet the above requirements:

Thanks for reading 🙂

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