  Traditional synthetic data generation methods rely on model-based approaches
that tune the parameters of a model rather than focusing on the structure of
the data itself. In contrast, Scagnostics is an exploratory graphical method
that captures the structure of bivariate data using graph-theoretic measures.
This paper presents a novel data generation method, scatteR, that uses
Scagnostics measurements to control the characteristics of the generated
dataset. By using an iterative Generalized Simulated Annealing optimizer,
scatteR finds the optimal arrangement of data points that minimizes the
distance between current and target Scagnostics measurements. The results
demonstrate that scatteR can generate 50 data points in under 30 seconds with
an average Root Mean Squared Error of 0.05, making it a useful pedagogical tool
for teaching statistical methods. Overall, scatteR provides an entry point for
generating datasets based on the characteristics of instance space, rather than
relying on model-based simulations.
