Respiratory rate (RR) is one of the most important physiological parameters. In recent years, the RR estimation from PPGs widely used in smart devices has been promoted. The effect of respiration on PPGs manifests in three ways: BW (intensity variation), AM (amplitude variation), FM (frequency variation). In addition to sophisticated RR estimation methods, reliable results can be achieved with simple and efficient methods implementable in wearables. The BW signal (respiratory signal estimation, RS) can be obtained by linear filtering of the PPG. The RR estimation is based on BW extremes (sBW), BW autocorrelation extremes (aBW) and their spectra (SBW, ABW). Estimation of the AM RS requires PPG extremes detection and interpolation. The RR estimation is based on extremes of the AM signal (sAM), its autocorrelation (aAM) and their spectra (SAM, AAM). The fusion of RR estimates leads to more robust results. To test the algorithms, the annotated BIDMC and CapnoBase Datasets were used. RR estimates were made for 60 s sections. The simplest and the most accurate method for both datasets is the RR estimation based on sBW (RsBW). The median absolute error was 0.40 (0.16 1.09 interquartile range 25-75th) bpm for the 60s window, mean absolute error was 1.42 bpm.