View source: R/stat-pop-etho.R
stat_pop_etho | R Documentation |
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.
stat_pop_etho(mapping = NULL, data = NULL, geom = "smooth", position = "identity", ..., method = mean_se, method.args = list(), show.legend = NA, inherit.aes = TRUE)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
The geometric object to use display the data |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
... |
Other arguments passed on to |
method |
function used to compute the aggregate and confidence intervals.
It should return ( |
method.args |
List of additional arguments passed on to the modelling
function defined by |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
The relevant rethomic tutorial section
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
library(behavr) metadata <- data.frame(id = sprintf("toy_experiment | %02d", 1:20), 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 ~ .) # 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 aggreate 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()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.