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#' Autocorrelation for temporal data
#'
#' Since the data input is `data.frame`, it's better to sort the date-times
#' from early to recent and make implicit missing values explicit before using
#' `geom_acf`.
#'
#' @inheritParams ggplot2::geom_bar
#' @param lag.max An integer indicating the maximum lag at which to calculate the
#' acf.
#' @param type A character string giving the type of the acf to be computed. The
#' @param level A numeric defining the confidence level. If `NULL`, no significant
#' line to be drawn.
#' @param na.rm Logical. If `TRUE`, missing values are removed. default is the
#' "correlation" and other options are "covariance" and "partial".
#'
#' @rdname sugrrants-geom
#' @examples
#' library(dplyr)
#' fstaff <- hourly_peds %>%
#' filter(Sensor_ID == 13)
#'
#' # use ggplot2
#' fstaff %>%
#' ggplot(aes(x = ..lag.., y = Hourly_Counts)) +
#' geom_acf()
#'
#' @export
geom_acf <- function(mapping = NULL, data = NULL,
position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE,
lag.max = NULL, type = "correlation", level = 0.95, ...) {
layer(
stat = StatAcf, geom = GeomAcf, data = data, mapping = mapping,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(
lag.max = lag.max, type = type, level = level,
na.rm = na.rm, ...
)
)
}
#' @rdname sugrrants-geom
#' @format NULL
#' @usage NULL
#' @export
GeomAcf <- ggproto("GeomAcf", GeomBar,
draw_panel = function(data, panel_scales, coord) {
acf_hline <- transform(data, fill = NA, alpha = NA)
acf_bar <- transform(data, colour = NA)
has_hline <- !is.null(data$yintercept)
gList(
GeomBar$draw_panel(acf_bar, panel_scales, coord),
if (has_hline) GeomHline$draw_panel(acf_hline, panel_scales, coord)
)
},
draw_key = draw_key_acf,
required_aes = "y",
default_aes = c(
aes(x = ..lag.., colour = "#3182bd", linetype = "dashed"),
GeomBar$default_aes
)
)
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