Pollution rose variation of the traditional wind rose plot

The traditional wind rose plot that plots wind speed and wind direction by different intervals. The pollution rose applies the same plot structure but substitutes other measurements, most commonly a pollutant time series, for wind speed.

Usage

pollutionRose(
  mydata,
  pollutant = "nox",
  key.title = pollutant,
  key.position = "right",
  breaks = 6,
  paddle = FALSE,
  seg = 0.9,
  normalise = FALSE,
  plot = TRUE,
  key = NULL,
  ...
)

Arguments

mydata: A data frame containing fields ws and wd
pollutant: Mandatory. A pollutant name corresponding to a variable in a data frame should be supplied e.g. pollutant = "nox".
key.title: Used to set the title of the legend. The legend title is passed to quickText() if auto.text = TRUE.
key.position: Location where the legend is to be placed. Allowed arguments include "top", "right", "bottom", "left" and "none", the last of which removes the legend entirely.
breaks: Most commonly, the number of break points for pollutant concentrations. The default, 6, attempts to breaks the supplied data at approximately 6 sensible break points. However, breaks can also be used to set specific break points. For example, the argument breaks = c(0, 1, 10, 100) breaks the data into segments <1, 1-10, 10-100, >100.
paddle: Either TRUE or FALSE. If TRUE plots rose using 'paddle' style spokes. If FALSE plots rose using 'wedge' style spokes.
seg: seg determines with width of the segments. For example, seg = 0.5 will produce segments 0.5 * angle.
normalise: If TRUE each wind direction segment is normalised to equal one. This is useful for showing how the concentrations (or other parameters) contribute to each wind sector when the proportion of time the wind is from that direction is low. A line showing the probability that the wind directions is from a particular wind sector is also shown.
plot: When openair plots are created they are automatically printed to the active graphics device. plot = FALSE deactivates this behaviour. This may be useful when the plot data is of more interest, or the plot is required to appear later (e.g., later in a Quarto document, or to be saved to a file).
key: Deprecated; please use key.position. If FALSE, sets key.position to "none".
...: Other arguments passed on to windRose().

Value

an openair object. Summarised proportions can be extracted directly using the $data operator, e.g. object$data for output <- windRose(mydata). This returns a data frame with three set columns: cond, conditioning based on type; wd, the wind direction; and calm, the statistic for the proportion of data unattributed to any specific wind direction because it was collected under calm conditions; and then several (one for each range binned for the plot) columns giving proportions of measurements associated with each ws or pollutant range plotted as a discrete panel.

Details

pollutionRose() is a windRose() wrapper which brings pollutant forward in the argument list, and attempts to sensibly rescale break points based on the pollutant data range by by-passing ws.int.

By default, pollutionRose() will plot a pollution rose of nox using "wedge" style segments and placing the scale key to the right of the plot.

It is possible to compare two wind speed-direction data sets using pollutionRose(). There are many reasons for doing so e.g. to see how one site compares with another or for meteorological model evaluation. In this case, ws and wd are considered to the the reference data sets with which a second set of wind speed and wind directions are to be compared (ws2 and wd2). The first set of values is subtracted from the second and the differences compared. If for example, wd2 was biased positive compared with wd then pollutionRose will show the bias in polar coordinates. In its default use, wind direction bias is colour-coded to show negative bias in one colour and positive bias in another.

Examples

# pollutionRose of nox
pollutionRose(mydata, pollutant = "nox")


# source apportionment plot - contribution to mean
if (FALSE) { # \dontrun{
pollutionRose(mydata, pollutant = "pm10", type = "year", statistic = "prop.mean")
} # }