Using Shiny

Mitch Mooney (link) has created a Shiny application for netball. He has aggregated and curated 12,500 data points from publicly available sources.

Shiny is an R package that makes it possible to build interactive web apps straight from R.

Mitch’s Shiny application is a remarkable resource for netball and it provides us with an important example of how to collect and share data. I see it (link) as a great way to support user interaction and inquiry. It is for me a powerful exercise in reader-receptivity

There are thirty-one teams in Mitch’s database going back to 2013.

I share Mitch’s interest in Shiny as a way of making data public and encouraging reflection on those data. Many years ago, I was introduced to Wolfgang Iser (1991) and reader-receptivity criticism. Wolfagang suggested then:

By putting the response-inviting structures of literary text under scrutiny, a theory of aesthetic response provides guidelines for elucidating the interaction between text and reader.

He adds:

If a literary text does something to its readers, it also simultaneously reveals something about them. Thus literature turns into a divining rod, locating our dispostions, desires, inclinations, and eventually our overall make up.

It is this divining rod of dispositions that attracts me to Shiny and the sharing in which Mitch has engaged.

I have looked at Shiny for some time as a way to share data. Recently, I looked at goalkeeper heights at the FIFA Women’s World Cup in France (link). I have also looked at the esquisse package to share data (link).

My interest in Shiny was stimulated by the discovery of the New Zealand Tourism Dashboard (link), “a one-stop shop for all information about tourism”. The dashboard brings together a range of tourism data produced by Ministry of Business, innovation and Employment and Statistics New Zealand into an easy-to-use tool. Information available is presented using dynamic graphs and data tables.

New Zealand government departments maintain fifteen web applications built with RStudio’s Shiny framework.Their main purpose is to make public data more available and accessible for non-specialist users (link).

I see Mitch’s contribution to this sharing as very important and I am delighted he has shared his link to the netball data.

Netball Quad Series September 2018

I used BoxPlotR to visualise the scores by quarter in the 2018 Netball Quad Series that concluded today.

The four teams in the series were: Australia, England, New Zealand and South Africa.

The centre lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots; data points are plotted as open circles. n = 6 sample points. Winning teams in light blue, losing teams in light green.

Photo Credit

Australian Diamonds (Twitter)

2018 Netball #QuadSeries in South Africa

The Netball Quad Series concluded at the weekend in South Africa. The teams in the tournament were: Australia, England, New Zealand and South Africa.

Champion Data provided data for the tournament. I have used their data for my secondary analysis.

My record of the six games as a box plot (using BoxPlotR) for winning teams per quarter (light green) and losing teams per quarter (light blue):

The centre lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots; data points are plotted as open circles. n = 6 sample points.

Photo Credits

Molokwane hails 2018 Netball Quad Series (SABC, Twitter)

One more quarter (Samsung Diamonds, Twitter)