For #MakeoverMonday week 32, The makeover viz was about how much countries spends on Research & Development and the original viz was shared on HowMuch.net

Here is the original viz :

Original Image

Data is available on data.world and source of data was UNESCO Institute for Statistics

Whats good?

  • Clear Legends on specifying what color in the circle means in terms of spends
  • Design is eye-catching with too much of information for an individual to understand
  • Source of data is mentioned
  • Title which is self explanatory about the viz

Here is what I did:

  • After trying various methods to visualize this data, I finalize one approach of showing Top 10 countries by spends
  • Additionally I wanted show trend on spends by this countries and also compare 2 years i.e. 2015 vs 2014 to show the increase in spends by countries on R&D (Exception was India as dataset didn’t have 2012 to 2014 data)
  • While building the viz and trying to figure out visualization I came across this viz from Rody Zakovich and tried to replicate the similar design.

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

Click here for Tableau file

Thanks Eva Murray and Andy Kriebel for this workout.

Happy Data Visualisation!!!!

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For #MakeoverMonday week 31, this week’s makeover viz was about The Big Mac Index by The Economist. This index was based on “Big Mac Hamburger” price across various countries where it is sold to analyse the purchasing power parity.

Here is the original viz :

Data is available on data.world and source of data was The Economist

Whats good?

  • Clear Legends showing price of Jul’17 and Jan’18 along with Price of Big Mac price in US Dollar
  • Zero reference line to see the difference and sorting the countries with latest prices
  • Sub-titles explaining the overvalued and undervalued with signs along with caveats

Here is what I did:

  • Rather then using price of last 2 years, I selected price of last 10 years starting Jul’2009 and used only July prices for all the years
  • Designed an jitter plot to show the plot the country wise price variation in comparison to US price
  • Interactive tooltip to show Big Mac price in various countries with there overvalued and undervalued against US dollar and highlight option to select a country for trend

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

Click here for Tableau file

Thanks Eva Murray & Andy Kriebel for this workout.

Happy Data Visualisation!!!!

Thanks for visiting this post. Please do let me know your feedback or if you have any questions about the blog do not hesitate to contact me on twitter (@Desaimithun)

Do subscribe to Tabvizexplorer.com to keep receive regular updates.

For #MakeoverMonday week 29, Andy shared the viz from What’s the Cap? and it was designed by whatsthecapIt 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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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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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):

Thanks for visiting blog. Please do let me know your feedback and if any particular topic you would like me to write on.

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