ggetho (version 0.3.5)

geom_peak: Visualise peaks in a power spectrum or periodogram

Description

This function draws points on the x-y coordinates of selected peaks and write their (y) value on the bottom of the plot.

Usage

geom_peak(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", ..., na.rm = TRUE, show.legend = NA,
  inherit.aes = TRUE, peak_rank = 1, conversion = hours)

Arguments

mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

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. A function can be created from a formula (e.g. ~ head(.x, 10)).

stat

The statistical transformation to use on the data for this layer, as a string.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

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. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

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().

peak_rank

numerical vector specifying the rank(s) of peak(s) to draw

conversion

function to convert values of x to a specific unit. The default, hours, will write x (time) in decimal hours.

Value

A ggplot layer.

Details

In the input data, peaks are encoded as an additional column/aesthetic with values corresponding to peak ranks (and 0 when the point is not a peak). In other word, the mapping must provide x, y and peak. Only peaks matching peak_rank will be drawn (see example).

References

• The relevant rethomic tutorial section

See Also

• ggperio to create a periodogram

• zeitgebr::find_peaks to automatically add a peak column on periodogram data

Other layers: stat_bar_tile_etho, stat_ld_annotations, stat_pop_etho

Examples

# NOT RUN {
# We make a data frame by hand with five rows
# There are two peaks: in position 4 and 2

df <- data.frame(x = hours(1:5),
                 y = c(1, 2, 0, 4, 1),
                 peak = c(0, 2, 0, 1, 0))
#  We draw the plot as a line
pl <-  ggplot(df, aes(x, y, peak = peak)) +
                  geom_line() +
                  scale_x_hours()
pl
# Now we could add the peak values as an extra layer:
# The first peak
pl + geom_peak()
# The first ans second peak
pl + geom_peak(peak_rank = 1:2)
# The second only
pl + geom_peak(peak_rank = 2)

# Just like with other geoms,
# we can change colour, size, alpha, shape, ... :
pl + geom_peak(colour = "red", size = 10, alpha = .5, shape = 20)

## In the context of circadian analysis,
# Using the zeitgebr package:
# }
# NOT RUN {
require(zeitgebr)
# We make toy data
metadata <- data.table(id = sprintf("toy_experiment|%02d", 1:40),
                       region_id = 1:40,
                       condition = c("A", "B"),
                       sex = c("M", "M", "F", "F"))
dt <- toy_activity_data(metadata, seed = 107)
# We shift period of the group "A" by 0.01
dt[, t := ifelse(xmv(condition) == "A", t, t * 1.01)]
# We  compute a periodogram for each individual
per_dt <- periodogram(moving, dt, FUN = chi_sq_periodogram)
per_dt <- find_peaks(per_dt)
out <- ggperio(per_dt, aes(y = power - signif_threshold, colour = condition, peak = peak)) +
                    stat_pop_etho() +
                    facet_wrap( ~ id, labeller = id_labeller)
out
out + geom_peak(colour="black")
# }