How can I decide proper FFT length(size)?
But I want to know more about 21,53 Hz. exactly FR.


This represents the bandwidth (how wide in Hz a bin is) of a particular bin. The smaller this value is, the better FR you have. In order  to get the bin center frequency, you can use the following formula

bin_center_freq = FR * bin_number;


So, for, say, bin at index 20 , we get

bin_center_freq =   21,53*20 = 430,66 Hz (Note that the bin at index 0 represents DC (0 Hz)


When we have a bin center frequency, we can easily calculate the lower and upper frequencies for that bin.

bin_lower_freq = bin_center_freq-(FR/2) = 419,89 Hz

and

bin_upper_frequency = bin_center_freq-(FR/2) = 441,42 Hz


Note that the real frequency resolution (our ability to separate close-by frequencies) will still depend on the type of the window used for the FFT (Blackman, Gaussian, Hamming, rectangular etc) as well as on the spectral content present (whether we have components that are both close in frequency and amplitude etc) in the signal so you shouldn't assume
that a particular bin contains only frequencies belonging to that bin(due to the spectral leakage, every bin will also have energy contributions from neighbouring bins).

So, what is the right FFT size?

It really depends on what you want to do with your signal. If you have a signal containing 2 sine waves close in frequency and amplitude (e.g. one 220 Hz, and the other 225Hz), you should choose a relatively long FFT length such that
the FR is less then 5 (f1-f2). But since windowing also affects the result, make your FFT length even longer to compensate for the windowing effect. You can read up more on window functions here ( https://en.wikipedia.org/wiki/Window_function )