#workoutwednesday | Data Visualization | Tableau

#WorkoutWednesday wk28: Profit Variance

This week #workoutwednesday challenge was set up by Luke Stanke, it was about scaling the data into same level using log axis. Whenever we want to analyse the spread of data we end up having skewed bars with one or two group are at higher levels while others are with tiny bars which makes it difficult to understand their behavior. Here, we will be scaling the axes with log to analyze the per-order profit across sub-categories.

Requirements

  • Dashboard size is 500 x 700; tiled; 4 sheets
  • Use the superstore dataset. Focus on the Sub-category of the “Technology” category.
  • Show profit per order on the columns (x-axis).
  • Use a parameter to round the profit to the nearest $25, $50, $100, $250, $500, or $1000.
  • Show distinct count of orders on the rows (y-axis). Set your axis to run from 0 to 999.
  • Set the axis to be log.
  • Make sure to assign the highlight color to each sub-category.
  • Filter to orders with profits from -1500 through 1500.
  • Create bars and center them on the appropriate value. Set the width of the bar to the rounded value.
  • Add annotations so they only show when $100 is selected on the parameter.
  • Set the height of the Accessories, Copiers, and Office Supplies to 110 pixels. Set Phones to 160 pixels.
  • Match color, tooltips, and formatting. I’ll be paying attention to all parts.

Data for the workout can be downloaded from here

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

Requirement were very clear and also given few hints (I had to go through them) to derive the end results. Here the steps by step process of creating this Visualisation.

Step 1: Create the parameter with values list from $25 to $1000 (total 6) and create the calculated field with parameter as shown in below image:

Step 2: Create calculated field called RoundProfit as we need to round the profit values based on parameter value:

Duplicate the sub-categories field 3 times and rename them to sub-categories_1, sub-categories_2 and sub-categories_3

Step 3: As per requirement lets add filter for ‘Technology’ as category and drag RoundProfit as put the range between -1500 to 1500

Step 4: Drag Roundprofit field to column area and set type as ‘Dimension’ from the drop down option, Order id field into rows and set measures as distinct count, add dummy field in rows as ‘Accessories’

Based on above requirement, lets convert the y-axis to logarithmic and set range from 0 to 999.

Now, drag Param field (set to dimension) into size to set the size of bars and sub-category field into color along with manual sorting which is very important for our dashboard.

once, we are through with this steps, final thing which we will do it to set stack marks off from Analysis-> Stacks Marks -> Off

Duplicate the sheet for other 3 sub-categories and replace sub-categories field by duplicate which we created and we are through with the sheet creation. After bit and pieces of formatting we are ready with output.

Thanks Luke Stanke for this workout.

Happy Data Visualisation!!!!

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#MakeoverMonday | Data Visualization | Tableau

MakeoverMonday wk28 – Volcano Eruptions

For #MakeoverMonday week 28, this week it was time to work with Volcano eruption data. We have been hearing lot about disruptions caused by Volcano in last few years and this dataset was about all volcanoes with their geo spatial information and rock type.

Here is the original viz:

original visualization

Data is available on data.world and source of data was Global Volcano Program

Whats good?

  • Color selected to highlight the active and inactive volcanos
  • Label given to known volcanoes which were in news in last few years.
  • Sorting by size showing the elevation height of volcanoes and its type

Here is what I did:

  • Plotted terrain map with the location of each volcanos and if user selects any volcano then details section to state the details along with the tooltip.
  • Added dot plot using shape with their rock types to know about various rock type found in volcanoes
  • splitted the years mentioned into 5 buckets to show how volcanoes eruptions have evolved in last few years

Here is the Image of the visualisation I created (Click on image to get interactive version):

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#workoutwednesday | Data Visualization | Tableau

#WorkoutWednesday wk27: How do current year sales compare to previous years

Late post this week on #WorkoutWednesday was set by Ann Jackson and it was about using dot plots to compare current year sales with previous year sales. Generally, we use some form of bar chart to spot the trend over a period but it can get bit messy if we want to compare the same on multiple data points with sorted years. With Dot plots, we can have powerful yet simple visualisation technique to display comparative analysis on multiple data points. There are additional indicator as well to spot the current year values with different color for good and poor against average of sales for selected years.

Requirements

  • Dashboard size: 1250 x 900, 2 sheets, tiled
  • Each dot represents annual sales by subcategory
  • Color of dots:
    • Most recent year below average = pink
    • Most recent year above average = blue
    • Not most recent year = gray
  • Calculation for most recent year should work if data updates and 2018 gets added in (don’t hard code 2017!)
  • Match tooltip language – don’t hard code the number of years, build a calculation!
  • Match formatting: specifically horizontal line, banding, reference line
  • Don’t forget about the legend

Additional year filter so you can see what the tooltips and colors look like

Data for the workout can be downloaded from here

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

Lets go through step by step process of re-creating this dashboard and understand the process of generating similar dashboards in future for our business problems

Step 1: Import the dataset given in above link into Tableau file and create following 3 calculated fields first:

Yearly average sales: This is an dynamic filter to ensure right average amount is picked based on years selected:

Color: To allocate color for current year sales performance (blue or pink) and grey for other years

Tooltip: This is for dynamic tooltip on mouse hover over dots

And last field on Legend (dummy field): This is dummy field to show legend in circle

Now, we are ready to create the worksheets to meet the requirements mentioned above.

Step 2: Drag Category and sub-category from dimensions to rows then convert them into upper-case by using Upper() function:

Then drag sales field from measures to columns and change the chart type to circle

now, create a dummy field in columns using following text min(1) and select the chart type as bar then convert the chart into dual axis

Remove measure names from bar chart and change the axis in bar chart to fixed from 0 to 1 with size almost 0:

Drag upper(sub-category) field into Label for bar chart and change the alignment to top left

Step 3: Once we are through with Bar chart, we will now work on circle chart for dot plot.

Drag Order date to details and remove measure names from color:

Drag Color field which we created in step 1 to color:

Drag tooltip into tooltip and modify the tooltip to match the requirement:

Next is to send bar chart back, for that right click on min(1) on axis and select move marks to back:

Hide the upper(sub-category) field header from rows and apply necessary formatting required along with context filter of Order date and show filter with multiple drop down:

Step 4: create an new sheet for legend and drag legend field to column and colors

Now, combine both the sheets into Dashboard as shown in above complete dashboard image.

Happy Data Visualisation!!!!

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#MakeoverMonday | Data Visualization | Tableau

MakeoverMonday wk27: Rats Sightings in New York

For #MakeoverMonday week 27, this week it was time to work on Rats sightings in New York. It was interesting data about Rats sightings in New York City from 2010 till date with details about it.

Here is the original viz was created by Jowanza Joseph and it seemed to be from R:

Data is available on data.world and source of data was NYC Open Data

Whats good?

  • Simple chart with clear axis which makes it easy to understand
  • Cyclic nature of Rats sighting every year
  • Average line to give an indicator about steady growth in sightings

Here is what I did:

  • Use of Bar chart to represent sightings for each year and its growth from previous year
  • Added month wise and year wise heat map which shows the cyclic nature of sightings each year (around May to Aug)
  • Added Map to show the number of sighting based on zip code
  • Borough filter to see visualisation borough wise.

Here is the Image of the visualisation I created (Click on image to get interactive version):

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