Plot an incidence timeseries

Usage

plot_incidence(
  modelled = i_incidence_model,
  raw = i_incidence_data,
  ...,
  mapping = .check_for_aes(modelled, ...),
  events = i_events
)

Arguments

modelled

An optional estimate of the incidence time series. If modelled is missing then it is estimated from raw using a poisson_locfit_model. In this case parameters window and deg may be supplied to control the fit.

A dataframe containing the following columns:

time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a time_period
incidence.fit (double) - an estimate of the incidence rate on a log scale
incidence.se.fit (positive_double) - the standard error of the incidence rate estimate on a log scale
incidence.0.025 (positive_double) - lower confidence limit of the incidence rate (true scale)
incidence.0.5 (positive_double) - median estimate of the incidence rate (true scale)
incidence.0.975 (positive_double) - upper confidence limit of the incidence rate (true scale)

Any grouping allowed.

modelled can also be the output from normalise_incidence in which case the plot uses the per capita rates calculated by that function

raw

The raw count data

A dataframe containing the following columns:

count (positive_integer) - Positive case counts associated with the specified time frame
time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

Any grouping allowed.

...

Named arguments passed on to geom_events

event_label_size

how big to make the event label

event_label_colour

the event label colour

event_label_angle

the event label colour

event_line_colour

the event line colour

event_fill_colour

the event area fill

hide_labels

do not show labels at all

guide_axis

a guide axis configuration for the labels (see ggplot2::guide_axis and ggplot2::dup_axis). This can be used to specify a position amongst other things.

...

Named arguments passed on to ggplot2::scale_x_date

name

The name of the scale. Used as the axis or legend title. If waiver(), the default, the name of the scale is taken from the first mapping used for that aesthetic. If NULL, the legend title will be omitted.

breaks

One of:

NULL for no breaks
waiver() for the breaks specified by date_breaks
A Date/POSIXct vector giving positions of breaks
A function that takes the limits as input and returns breaks as output

date_breaks

A string giving the distance between breaks like "2 weeks", or "10 years". If both breaks and date_breaks are specified, date_breaks wins. Valid specifications are 'sec', 'min', 'hour', 'day', 'week', 'month' or 'year', optionally followed by 's'.

labels

One of:

NULL for no labels
waiver() for the default labels computed by the transformation object
A character vector giving labels (must be same length as breaks)
An expression vector (must be the same length as breaks). See ?plotmath for details.
A function that takes the breaks as input and returns labels as output. Also accepts rlang lambda function notation.

date_labels

A string giving the formatting specification for the labels. Codes are defined in strftime(). If both labels and date_labels are specified, date_labels wins.

minor_breaks

One of:

NULL for no breaks
waiver() for the breaks specified by date_minor_breaks
A Date/POSIXct vector giving positions of minor breaks
A function that takes the limits as input and returns minor breaks as output

date_minor_breaks

A string giving the distance between minor breaks like "2 weeks", or "10 years". If both minor_breaks and date_minor_breaks are specified, date_minor_breaks wins. Valid specifications are 'sec', 'min', 'hour', 'day', 'week', 'month' or 'year', optionally followed by 's'.

limits

One of:

NULL to use the default scale range
A numeric vector of length two providing limits of the scale. Use NA to refer to the existing minimum or maximum
A function that accepts the existing (automatic) limits and returns new limits. Also accepts rlang lambda function notation. Note that setting limits on positional scales will remove data outside of the limits. If the purpose is to zoom, use the limit argument in the coordinate system (see coord_cartesian()).

expand

For position scales, a vector of range expansion constants used to add some padding around the data to ensure that they are placed some distance away from the axes. Use the convenience function expansion() to generate the values for the expand argument. The defaults are to expand the scale by 5% on each side for continuous variables, and by 0.6 units on each side for discrete variables.

oob

One of:

Function that handles limits outside of the scale limits (out of bounds). Also accepts rlang lambda function notation.
The default (scales::censor()) replaces out of bounds values with NA.
scales::squish() for squishing out of bounds values into range.
scales::squish_infinite() for squishing infinite values into range.

guide

A function used to create a guide or its name. See guides() for more information.

position

For position scales, The position of the axis. left or right for y axes, top or bottom for x axes.

sec.axis

sec_axis() is used to specify a secondary axis.

timezone

The timezone to use for display on the axes. The default (NULL) uses the timezone encoded in the data.

na.value

Missing values will be replaced with this value.

Named arguments passed on to poisson_locfit_model

d

input data

A dataframe containing the following columns:

count (positive_integer) - Positive case counts associated with the specified time frame
time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

Ungrouped.

...

not used and present to allow proportion model to be used in a group_modify

window

a number of data points defining the bandwidth of the estimate, smaller values result in less smoothing, large value in more. The default value of 14 is calibrated for data provided on a daily frequency, with weekly data a lower value may be preferred. - (defaults to 14)

deg

polynomial degree (min 1) - higher degree results in less smoothing, lower values result in more smoothing. A degree of 1 is fitting a linear model piece wise. - (defaults to 1)

frequency

the density of the output estimates as a time period such as 7 days or 2 weeks. - (defaults to "1 day")

predict

result is a prediction dataframe. If false we return the locfit models (advanced). - (defaults to TRUE)

mapping

a ggplot2::aes mapping. Most importantly setting the colour to something if there are multiple incidence timeseries in the plot

events

Significant events or time spans

A dataframe containing the following columns:

label (character) - the event label
start (date) - the start date, or the date of the event
end (date) - the end date or NA if a single event

Any grouping allowed.

A default value is defined.

Value

a ggplot object

Examples

# example code

tmp = ggoutbreak::england_covid %>%
  time_aggregate(count=sum(count)) %>%
  normalise_count(56489700, population_unit=1000, normalise_time="1 year")

tmp2 = tmp %>% poisson_locfit_model()

if(interactive()) {
  plot_incidence(tmp2,tmp,colour="blue",size=0.25)
}