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
.
geom_acf(mapping = NULL, data = NULL, position = "identity",
na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, lag.max = NULL,
type = "correlation", level = 0.95, ...)
The data to be displayed in this layer. There are three options:
If NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot
.
A data.frame
, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify
for which variables will be created.
A function
will be called with a single argument,
the plot data. The return value must be a data.frame.
, and
will be used as the layer data.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Logical. If TRUE
, missing values are removed.
default is the "correlation" and other options are "covariance" and "partial".
logical. Should this layer be included in the legends?
NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.
If FALSE
, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. borders
.
An integer indicating the maximum lag at which to calculate the acf.
A character string giving the type of the acf to be computed. The
A numeric defining the confidence level. If NULL
, no significant
line to be drawn.
other arguments passed on to layer
. These are
often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"
or size = 3
. They may also be parameters
to the paired geom/stat.
# NOT RUN {
library(dplyr)
fstaff <- pedestrian %>%
filter(Sensor_ID == 13)
# use ggplot2
fstaff %>%
ggplot(aes(x = ..lag.., y = Hourly_Counts)) +
geom_acf()
# }
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