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(See related articel on page one)

Adapted from Graphics for Statistics and Data Analysis with R (April 2010) by Kevin J. Keen

An enlightening example from the Organisation for Economic Cooperation and Development

Use Rose Plots to make sense of multivariate data Compiled by the Statistics Directorate (see page one), the 2009 version of the OECD health data information system covers 1960 to 2008. The accompanying rose plot displays a subset of complete data for 15 member nations for 2004. This plot is a variation on the rose diagram, used by Florence Nightingale 150 years ago. In this example, statistics depicted are:

Beyond Presentation Using R’s Graphic Capabilities to Analyze Data

Conclusions from the Rose Plot: Note that the English-speaking nations are not all that similar to each other. Neither are the German-speaking nations of Austria and Germany. Note that the Czech and Slovak Republics with respect to investments in their national healthcare systems are also going their separate ways.

• Counts of physicians and nurses per 1,000 population, • MD and nursing graduates per 1,000 candidates • Number of total, acute, and psychiatric beds per 1,000 • Numbers of MRI’s and CT scanners are per million • Total expenditure reported per capita in U.S. dollars adjusted for purchasing power parity The rose plot is useful when there are a dozen or so variables but not more than four dozen or so observations. A rose plot is not intended for a quick glance. It is a tool for data analysis. Each variable in the rose plot is identified with a sector. The rose plot in the figure was generated by the function stars, part of the standard graphics package in the R software system.

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The R code for the figure is as follows:

The R function unlist is used to create a vector variable from a list structure. This is done initially to create a character vector for the names of the nations. It is used again to create a matrix from the OECD data. On calling the function stars, the argument draw.segments=TRUE creates a rose plot, also known as a segment diagram, instead of a star plot. Care has been taken in the first argument to the call of the function stars to draft the figure so that each statistic is proportional to the area of each sector, and not the length of its radial. Note that the statistic for each nation has been converted to a proportion of the minimum for each category for comparison among nations. The argument key.loc=c(6,11.35) is used to set the location of the legend. The argument cex=0.8 reduces the size of the character labels. The function stars can print labels alternatively on one of two lines below each rose. This is turned off by setting flip.labels=FALSE.

The one country that appears remarkable for being the most unremarkable is Canada, the country with the smallest rose. Canada avoids the large variable imbalances seen in the other countries. When it comes to healthcare spending and investment, Canadians come across as uniformly tightfisted. Contrary to popular belief, Canada does not have a single national healthcare authority. Instead, each of Canada’s provinces and territories has its own healthcare system. Canada’s federal government is the major contributor but not the sole source of funding. Something leaping out in the rose plot is the great investment by the U.S. and Austria in MRI equipment. Perhaps more outstanding is the greater number of psychiatric care beds for all countries in comparison to Australia, Canada, Italy, and the U.S. Could those nations be benefactors of the soothing influence of the wide open spaces? Could Italy be reaping the benefit of the Mediterranean diet? Could this be another example of the timehonored question of nature versus nurture?

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Originating as part of the Marshall Plan to expedite the rebuilding of Europe after the Second World War, the Organisation for Economic Cooperation and Development (OECD), as it is now known, serves a much broader mission today, helping governments foster prosperity through economic growth and financial stability. The group consists of 31 member-nations from across the world and provides consultation for another 70. Over the years, the OECD’s Statistics Directorate has grown to become one of the world’s premier statistical agencies. It offers the most comprehensive source of comparable statistics across its member countries on a wide variety of economic issues, including healthcare. The agency’s ability to make sense of widely variable data comparing health systems across the nations play an important role in informing healthcare policy. And that is where the power of R truly shines. Written by Kevin J. Keen, Graphics for Statistics and Data Analysis with R (April 2010) presents the basic principles of sound graphical design and applies these principles to important examples such as that of the OECD. This book reflects a growing trend toward the graphic analysis of data, which, while not a new approach, has received a dramatic boost from ever-improving software and new manuals from top researchers explaining their methods. In Visualizing Data Patterns with Micromaps (April 2010), Daniel B. Carr and Linda Williams Pickle draw on research from psychology, statistical graphics, computer science, and cartography to demonstrate the value of micromaps, which link statistical information to an organized set of small maps that can help you to simultaneously explore the statistical and geographic patterns in data. Multiple Comparisons Using R (August 2010) by Frank Bretz, Torsten Hothorn, and Peter Westfall provides a concise and accessible introduction to multiple comparison procedures. The book presents numerous worked examples implemented in R, which readers will be able to adapt for their own use. Additional data sets and R software are available on a supporting website.

Imagine what Florence Nightingale could have accomplished with the power of R See “This Issue’s Back Page Tip” on pg. 8

Summer 2010

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