user3910073 - 1 month ago 14

R Question

I have a time series

`z`

`fs = 12`

`fft`

`y <- as.data.frame(fft(z))`

y$freq <- ..

y$y <- ifelse(y$freq>= 1/10 & y$freq<= 1/15,y$y,0)

zz <- fft(y$y, inverse = TRUE)/length(z)

plot zz in the time domain...

However, I don't know how to derive the frequencies of the fft and I don't know how to plot zz in the time domain. Can someone help me?

Answer

I have a function, that wraps `fft()`

a bit:

```
function(y, samp.freq, ...){
N <- length(y)
fk <- fft(y)
fk <- fk[2:length(fk)/2+1]
fk <- 2*fk[seq(1, length(fk), by = 2)]/N
freq <- (1:(length(fk)))* samp.freq/(2*length(fk))
return(data.frame(fur = fk, freq = freq))
}
```

`y`

is values of your signal, and `samp.freq`

is it's sample frequency. It's output is `data.frame`

with two columns - `fur`

is complex numbers we get after fast fourier transform (`Mod(fur)`

will be an amplitude, `Arg(fur)`

- a phase) and `freq`

is vector of corresponding frequencies.

But for frequency filtering I highly reccomend using signal package.

For example using Butterworth filter:

```
library('signal')
bf <- butter(2, c(low, high), type = "pass")
signal.filtered <- filtfilt(bf, signal.noisy)
```

In this case interval should be defined as c(Low.freq, High.freq) * (2/samp.freq), where Low.freq and High.freq - borders of frequency intervals. More information can be found in package documentation and octave reference guide.

Also, notice that with fft you can get only frequencies up to (sample frequency)/2.