Hours spent in congestion | European cities

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MOM wk20 IN A JAM

Interactive viz:

MOM wk20_In a JAM_congested cities

Description:

#Makeovermonday wk 20 – this weeks had a very simple, tiny weeny little dataset, and I like it when its like that!

Traffic jams struck me as a must for this dataset, so instantly wished to visualise this as a unit chart, I therefore made the necessary tweaks to the data source to allow for presentation of multiple cars to denote hours, to achieve this you need a row id for each category (city in this case) to reflect the number of cars you wish to show

for example, the original dataset lookedwas:

2018-05-17_2256

and it needs to look like this (city=London example):

2018-05-17_2256

Takeaway:

  • London tops out this selection of European cities where it is estimated that a commuter spends an average of 74 hours stuck in congestion (based on 240 commuting days)
  • That’s an average of 18.5 minutes per day, an equivalent of 2.1 days per year!

Source:

Euronews.com

Tag:

#Makeovermonday

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Waterfall chart take2 – Change in Expenditure by category UKHE

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Change in UK HE Expenditure by category_Waterfall cart

Interactive viz:
UK HE_Change in Expenditure 2015/6-2016/7

Description:

Having just created the viz for May’s #SWDChallenge I thought I would have a quick bash at another waterfall chart. This time I decided to present the waterfall as a change in Expenditure, I’ve added a few annotations to the chart to help explain the chart a little, and also included the full 2015/6 and 2016/7 volumes of expenditure per by category as a bar below the waterfall chart for ease of consumption.

Takeaway:

  • Staff costs make up the largest absolute change year on year @ £825k
  • Fundamental restructuring costs the largest percentage change @ 29.5%
  • BUT Interests and other finance costs the only category to show a reduction in expenditure between these two years (@ -£40k) from a sector view point, with a figure of £709k in 2016/7.

Source of dataviz: 

OC031 HESA Finance – Expenditure

Tag:

#VisualisingHE

#SWD Challenge MAY18

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UK_HE__Expenditure_by_type_overall

Description:

For May’s #SWDchallenge , Cole Knaflic challenged the community to create a Waterfall chart.

What did I do?

I decided to present the Expenditure by type of expenditure and location of HE provider, taken from HESA’s finance release.

A waterfall chart was a first for me, I’d never really come across too many of these charts before and certainly had never created one. Whilst they were a little more fiddly to create in Tableau than I had anticipated, they are pretty simple and after a quick google I found a really nice instructional blog post by evolytics which nicely guides you through the couple of steps required to create one.

in short:

  • create a bar
  • make it a running total
  • change the chart type to a gantt
  • create a negative measure calc [-(measure]
  • drop it on size
  • whola!

For the published interactive viz the dashboard is slightly larger and has more functionality than headliner #coffeetableviz used to submit to Coles May challenge, this is so that when you interact with the viz you can focus down on a particular expenditure type and or particular country and get a pretty clean viz

expenditure_by_type

Go on have a play……

Interactive viz:
UKHE_Expenditure by type_20167_HESA

 

Blog:

Coles May roundup to follow

Source of dataviz: 

https://www.hesa.ac.uk/data-and-analysis/publications/higher-education-2016-17#finance

Table 17.

Tag:

#VisualisingHE | #SWDChallenge

Daft Punk – Peak performing Singles

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Interactive viz:
Daft punk_The Singles_Peak chart positions

Hover over the singles titles to highlight the rings of success!

Description:

Daft Punks singles by peak chart position, the rings have a nod towards a vinyl disc and or tree rings, the idea is to visually highlight where Daft Punk have achieved the top chart slots (towards the centre of ring), bunching denotes concentration of common top chart rankings and gaps between rings visually displaying the gaps between stronger and weaker performing tracks.
Data gathered from extracting info from the official charts .com. Done by using google sheets smart little function ‘=importhtml’ then a little bit of ‘text to columns’ using Alteryx to get rid of the carriage returns and I had a nice dataset to play with.

Source: 

officialcharts.com

Tag:

#datathatinterestsme

David Bowie – Peak performing Singles

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Interactive viz:
The Singles of David Bowie_Peak chart position

Hover over the rings to see the singles and peak chart position, or for full details and search ability use the highlighter drop down in the bottom left corner of the viz.

Description:

Bowie’s best charting singles by decade, the decade rings have a nod towards a vinyl disc and or tree rings, the idea is to visually highlight where Bowie achieved the top chart slots (towards the centre of ring) or towards the lower end chart positions (bench-marked by his standards).
The colours chosen in the viz tease out the core pallet from arguably Bowie’s most iconic image (taken by Brian Duffy) to tie the viz together.
Data gathered from extracting info from the official charts .com. Done by using google sheets smart little function ‘=importhtml’ then a little bit of ‘text to columns’ using Alteryx to get rid of the carriage returns and I had a nice dataset to play with.

Source: 

officialcharts.com

Tag:

#datathatinterestsme

#SWD Challenge APR18

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Non Academic staff balance by gender

Interactive viz:
UKHE non academic staff split by gender

Description:

For April’s #SWDchallenge , Cole Knaflic challenged the community to create a SQUARE AREA GRAPH or WAFFLE CHART in its more common branding.

What did I do?

I decided to present the gender balances between staff categories in non academic roles in UK Higher Education.

Staff categories are defined by the standard occupational classification coding (SOC2010)

Major groups

  • 1 Managers, directors and senior officials
  • 2 Professional occupations
  • 3 Associate professional and technical occupations
  • 4 Administrative and secretarial occupations
  • 5 Skilled trades occupations
  • 6 Caring, leisure and other service occupations
  • 7 Sales and customer service occupations
  • 8 Process, plant and machine operatives
  • 9 Elementary occupation

Takeaway:

The overall proportional split of non-academic staff by gender is 63/37, two staff categories [administrative and secretarial occupations and sales and customer service occupations] are distributed above this overall gender split (due to the volume underpinning of administrative and secretarial occupations), FEMALES are most under-represented in the skilled trades and process, plant and machine operative type occupations.

Blog:

April’s challenge

Source of dataviz: 

Figure 5 – All staff (excluding atypical) by equality characteristics 2016/17

Tag:

#datathatinterestsme

TOP10 Wine Producing Countries

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TOP10 Wine Producing Countries

Interactive viz:

TOP10 WINE producing Countries

Description:

#Makeovermonday dataset wk 14 2018 – A delightfully simple dataset this week.

  • Country
  • Year
  • Volume of production in Million hectolitres (mhl)

Takeaway:

  • ITALY #1, minor blip in 2014, but predicted and forecast to recover the crown of #1 wine producing country in the world.
  • Italy, France and Spain, consistently the TOP3 wine producing countries over this time period

Source:

International organisation of vine and wine

Tag:

#Makeovermonday