surveillance: sts_ggplot – R documentation

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sts_ggplot

Time-Series Plots for "sts" Objects Using ggplot2

Description

A simple ggplot2 variant of stsplot_time, based on a “tidy” version of the "sts" object via tidy.sts. It uses a date axis and thus only works for time series indexed by dates or with a standard frequency (daily, weekly, or monthly).

Usage

autoplot.sts(object, population = FALSE, units = NULL,
             as.one = FALSE, scales = "fixed", width = NULL, ...)

Arguments

`object`	an object of class `"sts"`.
`population`	logical indicating whether `observed(object)` should be divided by `population(object)`. The `population` argument can also be a scalar, which is used to scale the denominator `population(object)`, i.e., `observed(object)` is divided by `population(object) / population`. For instance, if `population(object)` contains raw population numbers, `population = 1000` could be used to plot the incidence per 1000 inhabitants.
`units`	optional integer or character vector to select the units (=columns of `object`) to plot. The default (`NULL`) is to plot all time series.
`as.one`	logical indicating if all time series should be plotted in one panel with `geom_line`. By default, the time series are plotted in separate panels (using `geom_col`).
`scales`	passed to `facet_wrap` (for `as.one=FALSE`). By default, all panels use a common `ylim` (and `xlim`).
`width`	bar width, passed to `geom_col`. Defaults to 7 for weekly time series.
`...`	unused (argument of the generic).

Value

a "ggplot" object.

Author(s)

Sebastian Meyer

Examples

## compare traditional plot() with ggplot2-based autoplot.sts()
if (requireNamespace("ggplot2")) {
    data("measlesDE")
    plot(measlesDE)
    autoplot.sts(measlesDE)
}

## weekly incidence: population(measlesDE) gives population fractions,
## which we need to multiply by the total population
if (surveillance.options("allExamples") && require("ggplot2")) {
    autoplot.sts(measlesDE, population = 1000000/82314906) +
        ylab("Weekly incidence [per 1'000'000 inhabitants]")
}

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

v1.19.1

GPL-2

Authors

Michael H<f6>hle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Saban<e9>s Bov<e9> [ctb], Ma<eb>lle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)

Initial release

2021-03-30