r-------------{ Environment (Disease) Fig. 1. A schematic presentation of the interplay between the external environment, pathogen and animal, which influences resistance to infectious disease. Disturbance in equilibrium results in infection and disease skin and the mucous membranes of the respiratory tract. These tissues are in contact with the environment, and direct injury to them facilitate entry of pathogenic microorganisms through these important natural barriers. Sunburn and frostbite are examples of such adverse effects. Climatic factors such as heat and cold may also act as physiological stress factors which affect the specific and non-specific responses of the body to infection. 1.1.2 Pathogen Survival Climatic factors may affect dispersal, spread and survival of pathogenic micro organisms in the environment. This is also true for arthropod vectors such as mosquitos and ticks (Smith 1970; Ferguson and Branagan 1972). The density of the animal population is an important factor determining the concentration of patho gens in the environment. Population density can be influenced by weather condi tions, as animals respond to heat and cold by typical changes in behaviour. For example, in cold weather they tend to huddle together. This behaviour results in increased population density, which in turn involves an increased risk of the spread of airborne infections."

R, an Open Source software, has become the "de facto" statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels - from simple to advanced - and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. "Statistics and Data with R" presents an accessible guide to data manipulations, statistical analysis and graphics using R.

Assuming no previous knowledge of statistics or R, the book includes: A comprehensive introduction to the R language. An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results. Over 300 examples, including detailed explanations of the R scripts used throughout. Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences. A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods. Two extensive indexes that include references to every R function (and its arguments and packages used in the book and to every introduced concept.

An accompanying Wiki website, http: //turtle.gis.umn.eduincludes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the excercises presented in the book. Visitors are invited to download/upload data andscripts and share comments, suggestions and questions with other visitors. Students, researchers and practitioners will find this to be both a valuable learning resource in statistics and R and an excellent reference book.