Recurrence plots and recurrence quantification analysis are powerful tools to study the behavior of dynamical systems. What we learn through these tools is typically determined by the choice of a distance threshold in the phase space, which introduces arbitrariness in the definition of recurrence. Not only does symbolic recurrence overcome this difficulty, but also it offers a richer representation that book-keeps the recurrent portions of the phase space. Using symbolic recurrences, we can construct recurrence plots, perform quantification analysis, and examine causal links between dynamical systems from their time-series. Although previous efforts have demonstrated the feasibility of such a symbolic framework on synthetic data, the study of real time-series remains elusive. Here, we seek to bridge this gap by systematically examining a wide range of experimental datasets, from firearm prevalence and media coverage in the United States to the effect of sex on the interaction of swimming fish. This work offers a compelling demonstration of the potential of symbolic recurrence in the study of real-world applications across different research fields while providing a computer code for researchers to perform their own time-series explorations.