The goal of fftab is to make working with fft’s in R easier and more consistent. It follows the tidy philosophy by working with tabular data rather than lists, vectors, and so on. Typical signal processing operations can thus often be accomplished in a single dplyr::mutate call or by a call to similar functions. Some examples are shown here.
fftpipe package takes a approach similar to that of fftab.healthyR.ts package includes a tidy_fft function.You can install the development version of fftab from GitHub with:
# install.packages("pak")
pak::pak("thk686/fftab")
Using fftab with ggplot.
fftab(sunspot.month, norm = TRUE) |> to_rect(.keep = "all") |> to_polr(.keep = "all") |> print(n = 5) -> ssm.fft #> # A tibble: 3,177 × 6 #> .dim_1 fx re im mod arg #> <dbl> <cpl> <dbl> <dbl> <dbl> <dbl> #> 1 0 51.96+0.00i 52.0 0 52.0 0 #> 2 0.00378 4.37+4.99i 4.37 4.99 6.63 0.852 #> 3 0.00755 -0.86+5.08i -0.860 5.08 5.15 1.74 #> 4 0.0113 -2.65-5.70i -2.65 -5.70 6.29 -2.01 #> 5 0.0151 -4.64-0.59i -4.64 -0.586 4.68 -3.02 #> # ℹ 3,172 more rows
ggplot(fortify(sunspot.month)) +
geom_line(aes(x = Index, y = Data)) +
ylab("Sunspot count") +
xlab("Year") +
theme_bw() ->
p1
xlocs <- c(1, 0.1, 0.01)
xlabs <- c("1", "10", "100")
ssm.fft |>
dplyr::filter(.dim_1 > 0) |>
ggplot() +
geom_point(aes(x = .dim_1, y = mod)) +
geom_smooth(aes(x = .dim_1, y = mod)) +
scale_y_continuous(trans = "log", labels = function(y) signif(y, 1)) +
scale_x_continuous(trans = "log", breaks = xlocs, labels = xlabs) +
xlab("Cycle duration (years)") +
ylab("Mean amplitude") +
theme_bw() ->
p2
print(p1 / p2)
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