Data analysis is about finding the stories hidden in the mass of information that make up a data set. Usually we are interested in understanding the major patterns and the relationships that hold true for most of the data. But sometimes we need to look at the weird observations, the mavericks, the ones that don’t follow the crowd and behave differently. These observations are called ‘anomalies’.

I will describe a principled statistical approach to identifying anomalies in diverse data sets, from simple numerical data to complex high-dimensional data objects. The ideas will be illustrated using the {weird} package for R applied to several data analysis problems, including spotting over-priced wines, identifying errors in the records of the Old Faithful Geyser, and uncovering forgotten epidemics in 19th century France.