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Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, however, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method is a compact end-to-end deep learning model which does not require feature engineering and can use low-cost raw photoplethysmography (PPG) as input signal. RRWaveNet was tested subject-independently and compared to baseline in three datasets (BIDMC, CapnoBase, and WESAD) and using three window sizes (16, 32, and 64 seconds). RRWaveNet outperformed current state-ofthe-art methods with mean absolute errors at optimal window size of 1.66 ± 1.01, 1.59 ± 1.08, and 1.92 ± 0.96 breaths per minute for each dataset. In remote monitoring settings, such as in the WESAD dataset, we apply transfer learning to two other ICU datasets, reducing the MAE to 1.52 ± 0.50 breaths per minute, showing this model allows accurate and practical estimation of RR on affordable and wearable devices. Our study shows feasibility of remote RR monitoring in the context of telemedicine and at home.