Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their choice of a modelling approach corresponding to reality. The book is targeted to non-mathematicians with limitedknowledge of nonlinear dynamics; in particular, professionals and graduate students in engineering and the biophysical and social sciences. The book makes readers active learners with hands-on computerexperiments in R code directing them through Nonlinear Time Series Analysis (NLTS). The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework--condensed from sound empirical practices recommended in the literature--that details a step-by-step procedure for applying NLTS in real-world data diagnostics.