Tag: data visualization

Minitab 17: think Mini Cooper, not Minnie Mouse

As it has been 3 or 4 years since the previous version, the new release of Minitab 17 statistical package is surely cause for rejoicing, merriment, and an extra biscuit with a strong cup of tea.

At one of the centres where I work, the data analysts sit at the same lunch table, but are known by their packages, the Stata people, the SAS person, the R person, the SPSS person and so on. No Minitab person as yet, but maybe there should be. Not only for its easy to use graphics, mentioned in a previous post, but for its all round interface, programmability (Minitab syntax looks a little like that great Kemeny-Kurtz language from 1964 Dartmouth College, BASIC, but more powerful), and a few new features (Poisson regression for relative risks & counted data, although alas no negative binomial regression for trickier counted data), and even better graphics.

Bubble plots, Outlier tests, and the Box-Cox transformation (another great collaboration from 1964), Minitab was also one of the first packages to include Exploratory Data Analysis (e.g. box plots and smoothed regression), for when the data are about as well-behaved as the next door neighbours strung out on espresso coffee mixed with red cordial.

Not as much cachet for when the R and SAS programmers come a-swaggering in, but still worth recommending for those who may not be getting as much as they should be out of SPSS, particularly for graphics, yet find the other packages a little too high to climb.

http://www.minitab.com/en-us/

Visual Trees: The Book of Trees by Manuel Lima

Apart from the iconic and mysterious Australian  Nullarbor http://www.nullarbornet.com.au/ (literally ‘no’, or actually very few, trees) and the baddest and saddest of outer suburban concrete jungles, trees are a major part of our daily life. Trees produce shade and oxygen, and provide inspiration for dreaming scientists watching apples fall. ‘Tree of life’. ‘Family trees’. Tree branches have also long provided a metaphor for branches of knowledge and classification systems.

In his excellent new book ‘The book of trees: visualizing branches of knowledge’,

https://www.papress.com/html/book.details.page.tpl?isbn=9781616892180

Manual Lima, Designer and Fellow of the Royal Society of Arts (http://www.visualcomplexity.com/vc/) examines the role of trees in history, religion, philosophy, biology, computer science, data visualization, information graphics and data analysis / statistics.

Covering various types of tree graphs, including radial trees, sunbursts, Ben Schneiderman’s Treemaps and Voronoi Treemaps, Lima’s treatise provides inspirational historical and contemporary pictures, including timely applications such as looking at the words that appear with ‘I’ and ‘you’ in Google texts.

Statistical applications covered are mainly confined to Icicle plots or trees, used in applications such as cluster analysis, or the grouping observations into related classes, ‘taxa’ or clusters such as disease categories.

Not published in the Northern hemisphere until April 2014, the book is available now in Melbourne, Australia for around $50, e.g. www.ngvshop.ngv.vic.gov.au (the online search does not work) or http://metropolisbookshop.com.au

Accompanied by sources of information on how to construct such diagrams (e.g. http://www.flowingdata.com ) Lima’s new book will serve as an accessible and constant source of information on visualizing trees for new, as well as existing, ‘arborists’.

‘Velut arbor aevo’

‘May the Tree Thrive’!

 

Treemap software

http://www.cs.umd.edu/hcil/treemap/

http://www.treemap.com/

http://www.tableausoftware.com/

SecretSource: of Minitab and Dataviz

When the goers go and the stayers stay, when shirts loosen and tattoos glisten, it’s time for the statisticians and the miners and the data scientists to talk, and walk, Big Iron.

R. S-Plus. SAS. Tableau. Stata. GnuPlot. Mondrian. DataDesk. Minitab.   MINITAB?????? Okay, we’ll leave the others to get back to their arm wrasslin’, but if you want to produce high quality graphs, simply, readily and quickly, then Minitab could be for you.

A commercialized version of Omnitab, Minitab appeared in Philadelphia in 1972 and has long been associated with students learning stats, but also now with business, industrial and medical/health quality management and six sigma, etc. There’s some  other real ‘rough and tumble’ applications involving Minitab – DR Helsell’s ‘Statistics for Censored Environmental Data using Minitab and R’ (Wiley 2012), for instance.

IBM SPSS and Microsoft Excel can produce good graphs (‘good’ in the ‘good sense’ of John Tukey , Edward Tufte, William Cleveland, Howard Wainer, Stephen Few & Nathan Yau etc etc), with the soft pedal down and ‘caution switches’ on, but Minitab is probably going to be easier.

For example, the Statistical Consulting Centre at the University of Melbourne uses Minitab for most of its graphs (R for the trickiest ones). As well as general short courses on Minitab, R, SPSS and GenStat there’s a one day course in Minitab graphics in November, which I’ve done and can recommend.

More details on the Producing Excellent Graphics Simply (PEGS) course using Minitab at Melbourne are at

http://www.scc.ms.unimelb.edu.au/pegs.html

student and academic pricing for Minitab is at http://onthehub.com/

What, I wonder, would Florence Nightingale have used for graphic software if she was alive today???

2014 Books: Medical Illuminations and another Trout in the Milk

The first cab off the rank for 2014 is Howard Wainer’s ‘Medical Illuminations: Using Evidence, Visualization & Statistical Thinking to Improve Healthcare’, Oxford University Press,  2014. It costs around $40 Australian.

Dr Wainer has written several great graphics books, including 2005’s ‘Graphic Discovery: a Trout in the Milk and Other Visual Adventures’, Princeton University Press.

The new book has more of a medical theme, including extremely useful chapters on medical prediction, the importance of showing diabetes patients real-time  and understandable information on their blood sugar levels, and the over-use of pie charts.

Although not mentioned in the above books, Florence Nightingale, Nursing pioneer and first female Fellow of what was to become the Royal Statistical Society, developed and used graphs and charts (admittedly an early form of pie chart). Ms Nightingale used such graphs to clearly show Queen Victoria, who wasn’t a statistician and wouldn’t have appreciated heaps and heaps of tables, the very real problems that soldiers were facing in the Crimean War due to poor sanitation.

Since then, much medical data is routinely collected and statistically analysed, but there is still a long way to go in terms of portraying and illuminating that information to medical staff and the patients and carers themselves.  Books like Medical Illuminations, supplemented by general info on the ‘how’ of graphic presentation using readily available software (Wainer’s texts focus mainly on the ‘who’, ‘what’ and ‘why’), will help to achieve such an important goal.

Recommended, for non-statisticians and statisticians alike!

Oxford University Press website: http://www.oup.com.au/titles/academic/medicine/9780199668793