The article introduces the qgraph package for R, which provides an interface to visualize data through network modeling techniques. The package can represent various matrices used in statistics, such as correlation matrices, covariance matrices, factor loadings, and regression parameters. qgraph is particularly useful in psychometrics, where it automates the production of graphs based on theoretical constructs hypothesized to be networks of causally coupled variables. The package supports both exploratory and confirmatory factor analysis, using sem and lavaan packages, and automatically visualizes the output. The article demonstrates the use of qgraph by applying it to data from the NEO-PI-R, a widely used personality questionnaire. It covers various visualization techniques, including correlation matrices, concentration graphs, and factorial graphs, and discusses how to interpret these visualizations. The package is designed to be accessible to both inexperienced and experienced R users, offering a unified interface for visualizing and analyzing complex statistical patterns.The article introduces the qgraph package for R, which provides an interface to visualize data through network modeling techniques. The package can represent various matrices used in statistics, such as correlation matrices, covariance matrices, factor loadings, and regression parameters. qgraph is particularly useful in psychometrics, where it automates the production of graphs based on theoretical constructs hypothesized to be networks of causally coupled variables. The package supports both exploratory and confirmatory factor analysis, using sem and lavaan packages, and automatically visualizes the output. The article demonstrates the use of qgraph by applying it to data from the NEO-PI-R, a widely used personality questionnaire. It covers various visualization techniques, including correlation matrices, concentration graphs, and factorial graphs, and discusses how to interpret these visualizations. The package is designed to be accessible to both inexperienced and experienced R users, offering a unified interface for visualizing and analyzing complex statistical patterns.