In this paper, we propose a prior guided transformer for accurate radiology reports generation. In the encoder part, a radiograph is firstly represented by a set of patch features, which is obtained through a convolutional neural network and a traditional transformer encoder. Then an Additive Gaussian model is applied to represent the prior knowledge based on unsupervised clustering and sparse attention. In the decoder part, prior embeddings are acquired by probabilistically sampling from the radiograph prior. Then the visual features, language embeddings, and prior embeddings are fused by our proposed Prior Guided Attention to generate accurate radiology reports. Experiment results show that our method achieves better performance than state-of-the-art methods on two public radiology datasets, which proves the effectiveness of our prior guided transformer.