Most epidemiologists, sociologists, market research professionals and econo-mists are now regularly dealing with quantitative data obtained from ques-tionnaires. Sometimes these data are analyzed by a professional statistician,but most often it is the specialist in the particular eld of study who does thejob. In all situations, there is an absolute need to master statistics, a disciplineoften considered unpalatable because it lies at the intersection of two some-what impenetrable disciplines—mathematics and data processing.
                                        There is obviously a misunderstanding on this subject. If theoretical sta-tistics do rely on mathematics, the practice of statistics does not, or in allevents very little. Statistical practice is in fact like the process of translation,a process which, basically, proceeds from a hypothesis or a question formu-lated by a researcher to a series of variables linked by means of a statisticaltool. As with written material, there is almost always a loss or a differencebetween the meaning of the original text and the meaning of the translatedtext, and many versions can generally be suggested, each with their advan-tages and disadvantages.
                                        The objective of this book is to learn how to “translate” certain classicresearch questions into statistical formulations. As indicated in the title of thebook, the syntax of these statistical formulations is based on the well-knownR language. R has been chosen because it is accessed freely, and widely used,and above all because of the simplicity and the power of its structure: R helpsto think statistics clearly. But syntax is not the main point; understandingthe semantics is undoubtedly our real challenge. The semantics will emergeprogressively from examples and experience, and only very occasionallyfrom mathematical considerations.