stat_pop_etho: Compute and display a population aggregate for a variable of...

In ggetho: Visualisation of High-Throughput Behavioural (i.e. Ethomics) Data

Compute and display a population aggregate for a variable of interest

Description

This function displays the temporal (time on the x axis) trend of variable of interest, on the y axis as a line with confidence interval as a shaded area.

Usage

stat_pop_etho(
  mapping = NULL,
  data = NULL,
  geom = "smooth",
  position = "identity",
  ...,
  method = mean_se,
  method.args = list(),
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping	Set of aesthetic mappings created by 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)).
geom	The geometric object to use to display the data, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")
position	Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
...	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.
method	function used to compute the aggregate and confidence intervals. It should return (y, ymin and ymax). The default is ggplot2::mean_se, which computes the mean + or - standard error. ggplot2::mean_cl_boot can be used instead to generate bootstrap confidence interval instead.
method.args	List of additional arguments passed on to the modelling function defined by method.
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().

Value

A ggplot layer.

References

• The relevant rethomic tutorial section

See Also

• ggetho to generate a plot object

• stat_tile_etho to show variable of interest as colour intensity

• stat_ld_annotations to show light and dark phases on the plot

• ggplot2::stat_smooth to understand how to change the type of confidence interval, line colour and so forth

Other layers: geom_peak(), stat_bar_tile_etho(), stat_ld_annotations()

Examples

library(behavr)
metadata <- data.frame(id = sprintf("toy_experiment | %02d", 1:4),
                   age=c(1, 5, 10, 20),
                   condition=c("A", "B"))
dt <- toy_activity_data(metadata, 3)
# We build a plot object
pl <-  ggetho(dt, aes(y = asleep))
# A standard plot of the whole population:
pl + stat_pop_etho()
# We can also split by condition, and display the two population on different facets:
pl + stat_pop_etho() + facet_grid(condition ~ .)
## Not run: 
# Instead, we can use different colour for separate conditions:
pl <-  ggetho(dt, aes(y = asleep, colour = condition))
pl + stat_pop_etho()

# Sometimes, we also have numeric condition (e.g. age)
pl <-  ggetho(dt, aes(y = asleep, colour = age))
pl + stat_pop_etho()
# We could want to aggregate several days of data to one circadian day (i.e. time wrapping)
# here, we also plot the invert of moving (!moving)
pl <-  ggetho(dt, aes(y = !moving), time_wrap = hours(24))
pl + stat_pop_etho()

## End(Not run)

ggetho documentation built on Nov. 24, 2022, 5:09 p.m.