This chapter presents Frequency-Based Transition Network Analysis (FTNA), a novel method to model the relational dynamics and the transitions between states or events based on the frequency of occurrence of transitions. Compared to TNA based on Markov models, FTNA is well-suited when the research focus is on describing, summarizing, or visually analyzing the observed data without the probabilistic assumptions and constraints. Compared to process mining, FTNA leverages statistical techniques such as pruning, bootstrapping and permutation to validate and compare models. Moreover, FTNA employs networks as a lens to represent and analyze transitions, which provides a rich family of metrics and analyses such as centrality measures, communities and patterns. In this chapter, we offer an introduction to the method and its main features, along with a step-by-step tutorial in R using a case study in group collaboration.