Data augmentation is an effective way to improve the generalization of deep learning models. However, the underlying augmentation methods mainly rely on handcrafted operations, such as flipping and cropping for image data. These augmentation methods are often designed based on human expertise or repeated trials. Meanwhile, automated data augmentation (AutoDA) is a promising research direction that frames the data augmentation process as a learning task and finds the most effective way to augment the data. In this survey, we categorize recent AutoDA methods into the composition-, mixing-, and generation-based approaches and analyze each category in detail. Based on the analysis, we discuss the challenges and future prospects as well as provide guidelines for applying AutoDA methods by considering the dataset, computation effort, and availability of domain-specific transformations. It is hoped that this article can provide a useful list of AutoDA methods and guidelines for data partitioners when deploying AutoDA in practice. The survey can also serve as a reference for further study by researchers in this emerging research area.