The books in this series are all linked with R, either presenting a new package developed by the own authors of the book or describing how to applying statistical techniques with the different packages available in R. ggplot2 is an implementation in R of The Grammar of Graphics (Wilkinson 2005) a systematic approach to the specification of statistical graphics that was introduced in a book previously reviewed in the Journal of Statistical Software by Cox (2007). This implementation has been developed by Hadley Wickham, who is also the author of the book reviewed here.

Exploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other graphics, such as factor-loadings plots. To illustrate our proposed improvements, we introduce and describe an example based on real data on which a principal component analysis is appropriate. We hope to provide better graphical tools to help researchers determine the number of factors to retain.

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Police crash reports are often the main source for official data in many countries. However, with the exception of fatal crashes, crashes are often underreported in a biased manner. Consequently, the countermeasures adopted according to them may be inefficient. In the case of bicycle crashes, this bias is most acute and it probably varies across countries, with some of them being more prone to reporting accidents to police than others. Assessing if this bias occurs and the size of it can be of great importance for evaluating the risks associated with bicycling. This study utilized data collected in the COST TU1101 action "Towards safer bicycling through optimization of bicycle helmets and usage". The data came from an online survey that included questions related to bicyclists' attitudes, behaviour, cycling habits, accidents, and patterns of use of helmets. The survey was filled by 8655 bicyclists from 30 different countries. After applying various exclusion factors, 7015 questionnaires filled by adult cyclists from 17 countries, each with at least 100 valid responses, remained in our sample. The results showed that across all countries, an average of only 10% of all crashes were reported to the police, with a wide range among countries: from a minimum of 0.0% (Israel) and 2.6% (Croatia) to a maximum of a 35.0% (Germany). Some factors associated with the reporting levels were type of crash, type of vehicle involved, and injury severity. No relation was found between the likelihood of reporting and the cyclist's gender, age, educational level, marital status, being a parent, use of helmet, and type of bicycle. The significant under-reporting - including injury crashes that do not lead to hospitalization - justifies the use of self-report survey data for assessment of bicycling crash patterns as they relate to (1) crash risk issues such as location, infrastructure, cyclists' characteristics, and use of helmet and (2) strategic approaches to bicycle crash prevention and injury reduction.

Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software.Undoubtedly, one of the most interesting topics related to traffic and transportation issues is road safety [16]. This is highly complex and goes beyond the number of deaths and injuries in crashes within a certain period. Road fatalities have a huge impact, not only in terms of the number of casualties, but also in terms of the costs related to caring for the injured people, the disruption of traffic flows just after accidents, and so forth [17]. For this reason, it becomes increasingly important to deploy prevention policies to manage infrastructures and road flows as well as to provide a quick and efficient response in emergencies [18].Most studies analyze road safety by considering the different elements that are involved, such as the vehicle, driver, road, and infrastructure. However, they generally do it by isolating one of these elements separately or assessing the interaction of two elements at most. Some recent studies suggest the growing need for integrating the maximum number of elements that can affect driving performance to ensure a bigger picture of the whole driving scenario. Thus, more light is shed on why a certain incident happens and under what circumstances [19].In the last few years, the naturalistic driving method has gained more relevance. This research method aims to characterize the driving behavior of people in real-world situations. In order to do that, the different elements and factors involved in the driving performance are recorded using a large array of sensors and video cameras inconspicuously installed in the vehicle. This equipment allows data related to the driving performance to be collected at high temporal rates. These kinds of data are collected using massive and blind strategies. They are massive because their aim is to collec...