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Plot standardised indices

Source: R/plot_sei.R
plot_sei.Rd

Plot a time series or histogram of standardised indices.

Usage

plot_sei(
  x,
  type = c("ts", "hist", "bar"),
  title = NULL,
  lab = "Std. Index",
  xlims = NULL,
  ylims = NULL,
  n_bins = 30
)

Arguments

x

vector or xts object containing the indices to be plotted.

type

type of plot (either time series "ts", histogram "hist", or barplot "bar").

title

optional title of the plot.

lab

axis label.

xlims, ylims

lower and upper limits of the axes.

n_bins

the number of bins to show in the histogram.

Value

A ggplot object displaying the standardised index values.

Details

The plot_sei() function can be used to plot either a time series (if type = "ts") or a histogram (if type = "hist" or type = "bar") of the values in x.

A time series can only be displayed if x is an xts time series.

The argument lab is a string containing the label of the x-axis if type = "hist" or type = "bar" and the y-axis if type = "ts".

The options type = "hist" and type = "bar" both display histograms of the data x. With type = "hist", plot_sei() is essentially a wrapper of geom_histogram(), while type = "bar" is a wrapper of geom_bar(). The latter can provide more flexibility when plotting bounded data, whereas the former is easier to use when superimposing densities on top.

Author

Sam Allen, Noelia Otero

Examples

data(data_supply)
# consider hourly German energy supply data in 2019
supply_de <- subset(data_supply, country == "Germany", select = c("date", "PWS"))
supply_de <- xts::xts(supply_de$PWS, order.by = supply_de$date)
supply_de_std <- std_index(supply_de, timescale = "hours")

plot_sei(supply_de, title = "German renewable energy production in 2019")

plot_sei(supply_de_std, title = "German SREPI in 2019")


plot_sei(supply_de, type = "hist", title = "German renewable energy production in 2019")

plot_sei(supply_de_std, type = "hist", title = "German SREPI in 2019")


# type = "hist" and type = "bar both output a histogram of the index values
# type = "hist" can be useful to superimpose densities on top of the histogram
z <- seq(-3.5, 3.5, length.out = length(supply_de_std))
plot_sei(supply_de_std, type = "hist", title = "German SREPI in 2019") +
 ggplot2::geom_line(ggplot2::aes(x = z, y = dnorm(z)), col = "blue")


# type = "bar" can be useful when the index values are bounded
supply_de_std <- std_index(supply_de, timescale = "hours", index_type = "prob11")
plot_sei(supply_de_std, type = "hist", xlims = c(-1, 1), title = 'type = "hist"')
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).

plot_sei(supply_de_std, type = "bar", xlims = c(-1, 1), title = 'type = "bar"')