#MakeoverMonday | Data Visualization | Tableau

#Makeover Monday wk 29 – NBA Team Salaries vs Cap

For #MakeoverMonday week 29, Andy shared the viz from What’s the Cap? and it was designed by whatsthecap. It was about NBA Team salaries and Salary cap since 1985-86. 

Here is the original viz :

Screen Shot 2018-07-15 at 12.46.55 pm.png

Data is available on data.world and source of data was Celtics Hub

Whats good?

  • Interactive tooltips to know the max, min and average salaries and actual salary cap
  • Clean time-series data with 3 distinct lines for highest, average and lowest payroll with bar chart to plot the salary cap which depicts the trend over last 32 seasons
  • Clear Y-axis labeling
  • Use of different colors for each line and bar with legend to distinguish each other

Here is what I did:

  • First of all I used the Andy’s Franchise mapping, since the teams have moved cities and changed their names over last 30 years.
  • I wanted to show all the teams which helps in comparisons and I finalized on bar chart.
  • Added indicator line for salary cap to see how many franchises adhere to salary cap and how many spent above salary cap
  • I wanted to compare franchises salary vs salary cap each season hence I introduced the filter for season
  • Added tooltip and text label to show the variance in Teams salary vs salary cap

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

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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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#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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#MakeoverMonday | Data Visualization | Tableau

MakeoverMonday wk26: London Cycle Hire Usage

For #MakeoverMonday week 26, it was huge dataset about London’s Cycle hire usage (Boris Bikes) :

Here is the original viz was created by Sophie Sparkles:

Screen Shot 2018-06-21 at 12.19.45 pm.png

To access the entire data from 2012 to 2018, one can use Exasol data source shared here or one can download the dataset for 2017 from data.world

Here is what I did:

  • I wanted to see correlation between weekday and hourly data hence use heat map with bar charts to see the data.
  • Added highest and lowest hour highlight
  • Use filter of year to filter for the year and use the map to see all the pick up points in London
  • added interactivity on graphs and maps to provide option to filter on any pick up point and analyze the data

Here is the Gif of the visualisation I created (download on the gif to download the Tableau file):

Use following steps to connect to database post of file:

  • Navigate to the sign-up page to register
  • You will receive an email with your credentials
  • Download the EXASOL driver (Tableau 32 bit; Tableau 64 bit; Tableau Mac OS)
  • A connection prompt will appear once you start dragging fields onto your worksheet. Enter your credentials (from the above email) in the connection prompt
  • start your analysis

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#MakeoverMonday | Tableau

MakeoverMonday wk25: US Influenza Report

For #MakeoverMonday week 25, Dataset was about influenza cases in the US which was visualized in line chart:

Here is the original viz by CDC:

Data is available on data.world week 25:

Here is what I did:

  • Filtered the data to use from season 2006-07 to 2017-18 (Season 2009-10 had few duplicate weeks in data shared)
  • Used highlight table to show number of patients for each week in season
  • Added tooltip to show number of patients compared to total patients reported

Click on the viz to use the interactive version.

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