This is a detailed set of notes for a workshop on Analysing spatial point patterns in R, presented by the author in Australia and New Zealand since 2006. The goal of the workshop is to equip researchers with a range of practical techniques for the statistical analysis of spatial point patterns. Some of the techniques are well established in the applications literature, while some are very recent developments. The workshop is based on spatstat, a contributed library for the statistical package R, which is free open source software. Topics covered include: statistical formulation and methodological issues; data input and handling; R concepts such as classes and methods; exploratory data analysis; nonparametric intensity and risk estimates; goodness-of-fit testing for Complete Spatial Randomness; maximum likelihood inference for Poisson processes; spatial logistic regression; model validation for Poisson processes; exploratory analysis of dependence; distance methods and summary functions such as Ripley’s K function; simulation techniques; non-Poisson point process models; fitting models using summary statistics; LISA and local analysis; inhomogeneous K-functions; Gibbs point process models; fitting Gibbs models; simulating Gibbs models; validating Gibbs models; multitype and marked point patterns; exploratory analysis of multitype and marked point patterns; multitype Poisson process models and maximum likelihood inference; multitype Gibbs process models and maximum pseudolikelihood; line segment patterns, 3-dimensional point patterns, multidimensional space-time point patterns, replicated point patterns, and stochastic geometry methods. Analysing spatial point patterns in R