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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:
The R code for the figure is as follows:
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 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
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SATURDAY, JULY 31 Monte Carlo and Bayesian Computation with R (CE_03C) Section for Statistical Programmers and Analysts, Section on Bayesian Statistical Science 4 Maria Rizzo, author of Statistical Computing with R teaches this course with Jim Albert, co-editor of Statistical Thinking in Sports. Causal Inference (CE_01C) 4 Miguel Hernan and James Robins, co-authors of the forthcoming Causal Inference, teach this course. SUNDAY, AUGUST 1 Statistical Issues in Approval of Follow-on Biologics - Invited – Papers 47 Biopharmaceutical Section, ENAR 4 Shein-Chung Chow, series editor for Chapman & Hall/CRC Biostatistics Series, chairs this session. Bayesian Ecology: Hierarchical Modeling for Ecological Processes (CE_10C) 4 Instructed by Alan E. Gelfand, co-author of Handbook of Spatial Statistics and Hierarchical Modeling and Analysis for Spatial Data. Statistical Methods for Spatial Longitudinal/Functional Data Invited – Papers 52 Section on Statistical Computing, IMS, International Chinese Statistical Association, Section on Nonparametric Statistics, Section on Physical and Engineering Sciences, Section on Statistics and the Environment, WNAR 4 Sudipto Banerjee, co author of Hierarchical Modeling and Analysis for Spatial Data and Linear Algebra and Matrix Computations for Statistics, presents Hierarchical Spatial Models for Predicting Forest Variables over Large Heterogeneous Domains with Andrew Finley. MONDAY, AUGUST 2 Medallion Lecture - Invited – Papers 146 IMS, International Chinese Statistical Association 4 Xiao-Li Meng, co-editor of Handbook of Markov Chain Monte Carlo: Methods and Applications, presents What Can We Do When EM Is Not Applicable? Self Consistency: A General Recipe for Semi-parametric and Non-parametric Estimation with Incomplete and Irregularly Spaced Data. JASA, Theory and Methods Invited Session - Invited – Papers 152 JASA, Theory and Methods 4 Bradley Efron, co-author of An Introduction to the Bootstrap, presents Correlated z-values and the Accuracy of Large-scale Statistical Estimates. TUESDAY, AUGUST 3 Analysis of Longitudinal Data Using Antedependence Models (CE_17C) 4 Instructed by Dale Zimmerman, co-author of Antedependence Models for Longitudinal Data. Bayesian Adaptive Methods for Clinical Trials (CE_19C) 4 Taught by Bradley P. Carlin, Scott Berry, and J. Jack Lee, coauthors of Bayesian Adaptive Methods for Clinical Trials, with Donald Berry, co-authors of Bayesian Biostatistics
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Alan E. Gelfand is the James B. Duke Professor of Statistical Science at Duke University. He is Chair of the Department of Statistical Science and Professor of Environmental Science. Author of 200 plus papers (more than 70 in the area of spatial statistics), he is internationally known for his contributions to applied statistics, Bayesian computation, and Bayesian inference. He is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Professor Gelfand is a former president of the International Society for Bayesian Analysis and in 2006 he received the Parzen Prize for a lifetime of research contribution to statistics. His primary research focus for the past 13 years has been in the area of statistical modeling for spatial and space-time data. A frequent contributor to CRC Press publications, he is co-author of the bestselling Hierarchical Modeling and Analysis for Spatial Data (new edition scheduled for 2011). He is also lead editor for the newly released Handbook of Spatial Statistics (March 2010). Through a collection of more than 60 papers Professor Gelfand has advanced methodology, using the Bayesian paradigm, to associate fully model-based inference with spatial and space-time data displays. His chief areas of application include environmental exposure, spatio-temporal ecological processes, and climatological modeling.
Now chair of Columbia University’s highly respected Department of Statistics, David Madigan uses his position to strongly advocate for the field of statistics validity as a versatile and independent science. Since receiving his Ph.D. from Trinity College in Dublin, Madigan has proven himself a prolific and ardent researcher, first at the University of Washington and then at Rutgers. Working for such companies as AT&T Inc., Soliloquy Inc., and SkillSoft, Inc, provided him with the opportunities to apply his science to modern problem solving. Decidedly Bayesian in his approach, he has over 100 publishing credits writing about a number of ways statistics intersects with other fields, including drug discovery and wireless technology. Professor Madigan is an elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is the current Editor-inChief of Statistical Science, a peer review journal published by the Institute of Mathematical Statistics. He is also the editor of the Chapman & Hall/CRC Computer Science and Data Analysis Series. Madigan stresses the idea that statistics is no more a branch of mathematics than any other field that uses mathematical tools. He believes that the field should be producing outstanding scientists as well as outstanding mathematicians. While he recognizes that some specialization is of value, Professor Madigan has stated that specialization along applied versus theoretical lines does a disservice to the science, as that distinction reinforces the concept of a theoretical statistician developing mathematical artifacts without reference to any scientific enquiry, while the lower-browed applied statistician conducts the intellectually less rigorous job of implementing theory. For the professor, the complete statistician must concern him or herself with both, and fortunately, students at Columbia are provided with an excellent role model of such a scientist.
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Series: Handbooks of Modern Statistical Methods From nonparametric regression to Monte Carlo simulation, more robust techniques and models have emerged in the field of statistics that embrace recent computational developments. Now, statisticians are applying these contemporary methods to address problems in psychology, economics, epidemiology, ecology, and more. Reflecting the intense development of recent years, Chapman & Hall/CRC Handbooks of Modern Statistical Methods documents those resources, emphasizing statistical methods and models likely to endure into the future. The series is edited by Professor Garrett Fitzmaurice, Department of Biostatistics, Harvard School of Public Health. The latest entry in the series, the Handbook of Spatial Statistics (March 2010) expertly compiles diverse research and applications of the past 20 years. In bringing together the field’s most prominent researchers, including editors Alan E. Gelfand (featured on our Pioneers page), Peter J. Diggle, Montserrat Fuentes, and Peter Guttorp, this volume provides a comprehensive and integrated
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SATURDAY, JULY 31 Monte Carlo and Bayesian Computation with R (CE_03C) Section for Statistical Programmers and Analysts, Section on Bayesian Statistical Science 4 Maria Rizzo, author of Statistical Computing with R teaches this course with Jim Albert, co-editor of Statistical Thinking in Sports. Causal Inference (CE_01C) 4 Miguel Hernan and James Robins, co-authors of the forthcoming Causal Inference, teach this course. SUNDAY, AUGUST 1 Statistical Issues in Approval of Follow-on Biologics - Invited – Papers 47 Biopharmaceutical Section, ENAR 4 Shein-Chung Chow, series editor for Chapman & Hall/CRC Biostatistics Series, chairs this session. Bayesian Ecology: Hierarchical Modeling for Ecological Processes (CE_10C) 4 Instructed by Alan E. Gelfand, co-author of Handbook of Spatial Statistics and Hierarchical Modeling and Analysis for Spatial Data. Statistical Methods for Spatial Longitudinal/Functional Data Invited – Papers 52 Section on Statistical Computing, IMS, International Chinese Statistical Association, Section on Nonparametric Statistics, Section on Physical and Engineering Sciences, Section on Statistics and the Environment, WNAR 4 Sudipto Banerjee, co author of Hierarchical Modeling and Analysis for Spatial Data and Linear Algebra and Matrix Computations for Statistics, presents Hierarchical Spatial Models for Predicting Forest Variables over Large Heterogeneous Domains with Andrew Finley. MONDAY, AUGUST 2 Medallion Lecture - Invited – Papers 146 IMS, International Chinese Statistical Association 4 Xiao-Li Meng, co-editor of Handbook of Markov Chain Monte Carlo: Methods and Applications, presents What Can We Do When EM Is Not Applicable? Self Consistency: A General Recipe for Semi-parametric and Non-parametric Estimation with Incomplete and Irregularly Spaced Data. JASA, Theory and Methods Invited Session - Invited – Papers 152 JASA, Theory and Methods 4 Bradley Efron, co-author of An Introduction to the Bootstrap, presents Correlated z-values and the Accuracy of Large-scale Statistical Estimates. TUESDAY, AUGUST 3 Analysis of Longitudinal Data Using Antedependence Models (CE_17C) 4 Instructed by Dale Zimmerman, co-author of Antedependence Models for Longitudinal Data. Bayesian Adaptive Methods for Clinical Trials (CE_19C) 4 Taught by Bradley P. Carlin, Scott Berry, and J. Jack Lee, coauthors of Bayesian Adaptive Methods for Clinical Trials, with Donald Berry, co-authors of Bayesian Biostatistics
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Advances in Functional Data Analysis - Invited – Papers 272 Section on Nonparametric Statistics 4 Raymond Carroll, co-author of Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition presents Generalized Functional Latent Feature Models with Single-Index Interactions with Yehua Li and Naisyin Wang. His co-author Ciprian Crainiceanu presents Longitudinal Functional Principal Component Analysis. Statistical Analysis of Complex Networks - a SAMSI Preview Invited – Papers 384 Section on Statistical Computing 4 Mike West, author of Time Series: Modeling, Computation, and Inference examines Issues in Model Emulation/Evaluation in Dynamic Network Studies in Systems Biology. Statistical Methods Used in Defense and Non-defense Applications - Invited – Panel 326 Section on Statistics in Defense and National Security, International Chinese Statistical Association 4 Max Morris, author of Design of Experiments: An Introduction Based on Linear Models presents Statistical Methods Used in Defense and Non-defense Applications with collaborators. WEDNESDAY, AUGUST 4 Graphics Packages for R, Recent Advances and Future Directions - Invited – Papers 446 Section on Statistical Graphics, Committee on Applied Statisticians, Section for Statistical Programmers and Analysts, Section on Government Statistics, Section on Statistical Computing 4 Organized and chaired by Daniel B. Carr, co-author of Visualizing Data Patterns with Micromaps. Section on Survey Research Methods PM Roundtable Discussion (fee event) 549 4 Join Brady West, co-author of Applied Survey Data Analysis and Linear Mixed Models: A Practical Guide Using Statistical Software in a discussion about Fitting Multilevel Models to Complex Sample Survey. THURSDAY, AUGUST 5 Key Multiplicity Issues in Clinical Trials - Invited – Papers 645 Biopharmaceutical Section, Committee on Applied Statisticians, ENAR 4 Organized by Alex Dmitrienko, co-editor of Multiple Testing Problems in Pharmaceutical Statistics.
Alan E. Gelfand is the James B. Duke Professor of Statistical Science at Duke University. He is Chair of the Department of Statistical Science and Professor of Environmental Science. Author of 200 plus papers (more than 70 in the area of spatial statistics), he is internationally known for his contributions to applied statistics, Bayesian computation, and Bayesian inference. He is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Professor Gelfand is a former president of the International Society for Bayesian Analysis and in 2006 he received the Parzen Prize for a lifetime of research contribution to statistics. His primary research focus for the past 13 years has been in the area of statistical modeling for spatial and space-time data. A frequent contributor to CRC Press publications, he is co-author of the bestselling Hierarchical Modeling and Analysis for Spatial Data (new edition scheduled for 2011). He is also lead editor for the newly released Handbook of Spatial Statistics (March 2010). Through a collection of more than 60 papers Professor Gelfand has advanced methodology, using the Bayesian paradigm, to associate fully model-based inference with spatial and space-time data displays. His chief areas of application include environmental exposure, spatio-temporal ecological processes, and climatological modeling.
Now chair of Columbia University’s highly respected Department of Statistics, David Madigan uses his position to strongly advocate for the field of statistics validity as a versatile and independent science. Since receiving his Ph.D. from Trinity College in Dublin, Madigan has proven himself a prolific and ardent researcher, first at the University of Washington and then at Rutgers. Working for such companies as AT&T Inc., Soliloquy Inc., and SkillSoft, Inc, provided him with the opportunities to apply his science to modern problem solving. Decidedly Bayesian in his approach, he has over 100 publishing credits writing about a number of ways statistics intersects with other fields, including drug discovery and wireless technology. Professor Madigan is an elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is the current Editor-inChief of Statistical Science, a peer review journal published by the Institute of Mathematical Statistics. He is also the editor of the Chapman & Hall/CRC Computer Science and Data Analysis Series. Madigan stresses the idea that statistics is no more a branch of mathematics than any other field that uses mathematical tools. He believes that the field should be producing outstanding scientists as well as outstanding mathematicians. While he recognizes that some specialization is of value, Professor Madigan has stated that specialization along applied versus theoretical lines does a disservice to the science, as that distinction reinforces the concept of a theoretical statistician developing mathematical artifacts without reference to any scientific enquiry, while the lower-browed applied statistician conducts the intellectually less rigorous job of implementing theory. For the professor, the complete statistician must concern him or herself with both, and fortunately, students at Columbia are provided with an excellent role model of such a scientist.
Bayesian Nonparametric Modeling of Longitudinal and Survival Data - Invited – Papers 553 Section on Nonparametric Statistics, Business and Economic Statistics Section, IMS, Section on Bayesian Statistical Science, Section on Health Policy Statistics, Section on Risk Analysis, WNAR 4 Wesley Johsnon, co-author of Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents Bayesian Nonparametric Longitudinal Data Analysis with Embedded Autoregressive Structure: Application to Hormone Data with Fernando Quintana.
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Effective Use of Instructional Technology - Invited – Papers 647 Section on Statistical Education, Section on Statistical Computing 4 Nicholas Jon Horton, co-author of SAS and R: Data Management, Statistical Analysis, and Graphics and Using R for Data Management, Statistical Analysis, and Graphics, presents Guiding Student Project Workflow Using Reproducible Statistical Analysis Tools.
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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:
The R code for the figure is as follows:
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 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
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Build Your Library Without Breaking Your Bank Save 50% on these classics: Statistical Thinking in Sports, Jim Albert and Ruud H. Koning Adaptive Design Methods in Clinical Trials, Shein-Chung Chow and Mark Chang Introduction to Statistical Methods for Clinical Trials, Thomas D. Cook and David L. DeMets Randomization Tests, Fourth Edition, Eugene Edgington and Patrick Onghena An Introduction to the Bootstrap, Bradley Efron and Robert J. Tibshirani Markov Chain Monte Carlo, Dani Gamerman and Hedibert F. Lopes Bayesian Methods, Jeff Gill story continued on pg. 7 (Great Deals)