Apply Triangular Filter

Applies a triangular smoothing filter — a rolling mean of width window_width applied twice. The composition of two boxcars is a triangular kernel, so the effective kernel width is 2 * window_width - 1 with peak weight at the centre.

Usage

filter_triangular(
  x,
  window_width = 5,
  min_obs = 1,
  align = c("center", "right", "left"),
  keep_na = TRUE
)

Arguments

x

Numeric vector to filter.

window_width

Integer width of each rolling-mean pass. The effective triangular kernel has width 2 * window_width - 1.

min_obs

Minimum number of non-NA values required per window per pass. Defaults to 1.

align

Window alignment, passed to filter_rollmean(). One of "center" (default), "right", or "left".

keep_na

Logical. If TRUE (default), positions that were NA in the input are NA in the output — gaps stay gaps. If FALSE, the values used to fill those gaps are kept, so the output has fewer NAs than the input and genuinely-missing stretches come back as interpolated estimates.

Value

Filtered numeric vector, same length as x.

Details

For align = "center", the underlying filter_rollmean() returns NA at the first and last (window_width - 1) %/% 2 positions of each pass, so the output has roughly window_width - 1 NA values at each edge.

keep_na = TRUE restores the positions that were NA in the input, but note that this filter fills gaps more aggressively than a single filter_rollmean() pass: because the second pass smooths the output of the first, values interpolated across a gap propagate outward. keep_na blanks the original gap positions; it does not undo that spread into neighbouring positions. Raise min_obs to suppress it.

Triangular smoothing is sometimes useful as a lightweight alternative to a Gaussian kernel when the kernel shape is less critical than the simplicity of the implementation.

Examples

x <- c(1, 2, 3, 100, 5, 6, 7, 8, 9)
filter_triangular(x, window_width = 3)
#> [1]       NA 18.50000 24.33333 36.00000 26.33333 16.66667  7.00000  7.50000
#> [9]       NA