The R-wave frequency peak should be the largest peak in the absolute value of the Fourier transform (after removing any d-c offset that may be present). Use the max function (with two outputs) to detect it and its index (into the frequency vector).
Use the second (index) output of max to get the associated frequency index of the R-wave peak.
The frequency vector of the Fourier transform will be in units of cycles/(time unit).
To convert this to (time units)/cycle, simply invert it.
That should produce the average heart rate, based on R-wave frequency.
EDIT — (9 Jan 2020 at 22:48)
Using the data you posted to your other Question:
EKG = readmatrix('Anonymous ECG.csv');
L = numel(EKG);
Fs = 350/2; % Units: Hz
Ts = 1/Fs; % Units: Sec
tv = linspace(0,L,L)*Ts; % Units: Sec
Fn = Fs/2;
FTEKG = fft(EKG)/L;
Fv = linspace(0, 1, fix(L/2)+1)*Fn;
Iv = 1:numel(Fv);
figure
plot(tv , EKG)
grid
[Pks,Locs] = findpeaks(EKG,tv, 'MinPeakHeight',200);
MeanHr1 = 60/mean(diff(Locs)); % Units: 1/Min
figure
plot(Fv, abs(FTEKG(Iv))*2)
grid
[MaxFTEKG,Idx] = max(abs(FTEKG(Iv))*2);
MeanHR2 = 60/Fv(Idx); % Units: 1/Min
Note that ‘MeanHR1’ and ‘MeanHr2’ are not the same because the broadband noise present in the Fourier-transformed data (that cannot be removed witha frequency-selective filter) may obscure the actual R-wave peak in the Fourier-transformed data. They are close, however, although I suspect that ‘Fs’ is wrong (probably should be 350), because the heart rates are about half of what they should be.
