polarAnnulus(mydata, pollutant = "nox", resolution = "fine",
local.tz = NULL, period = "hour", type = "default",
statistic = "mean", percentile = NA, limits = c(0, 100),
cols = "default", width = "normal", min.bin = 1,
exclude.missing = TRUE, date.pad = FALSE, force.positive = TRUE,
k = c(20, 10), normalise = FALSE, key.header = "",
key.footer = pollutant, key.position = "right", key = TRUE,
auto.text = TRUE, ...)date, wd and
a pollutant.pollutant =
"nox". There can also be more than one pollutant specified
e.g. pollutant = c("nox", "no2"). The main use of using two
ortype determines how the data are split
i.e. conditioned, and then plotted. The default is will produce a
single plot using the entire data. Type can be one of the built-in
types as detailed in cutData e.g. statistic = "percentile" or
statistic = "cpf" then percentile is used, expressed
from 0 to 100. Note that the percentile value is calculated in the
wind speed, wind direction coTRUE (the default)
removes points from the plot that are too far from the original data. The
smoothing routines will produce predictions at points where no data exist
i.e. they predict. By removing the points too far frtype = "trend" (default), date.pad = TRUE
will pad-out missing data to the beginning of the first year and the end
of the last year. The purpose is to ensure that the trend plot begins and
ends at the beginning or end of yearTRUE. Sometimes if smoothing
data with steep gradients it is possible for predicted values to be
negative. force.positive = TRUE ensures that predictions remain
postive. This is useful for several reasons. First, wgam for the temporal and
wind direction components, respectively. In some cases e.g. a trend plot
with less than 1-year of data the smoothing with the default values may
become too noisy and affected more by ouTRUE concentrations are normalised by dividing
by their mean value. This is done after fitting the smooth
surface. This option is particularly useful if one is interested in the
patterns of concentrations for several pollutants onkey.header = "header", key.footer
= "footer1" adds addition text above and below the scale key. These
arguments are passed to drawOpenKey via qukey.header.drawOpenKey. See
drawOpenKey for further details.TRUE (default) or FALSE. If
TRUE titles and axis labels will automatically try and format
pollutant names and units properly e.g. by subscripting the lattice:levelplot
and cutData. For example, polarAnnulus passes the option
hemisphere = "southern" on to cutData to provide southern
(rather than defaupolarAnnulus also
returns an object of class ``openair''. The object includes three main
components: call, the command used to generate the plot;
data, the data frame of summarised information used to make the
plot; and plot, the plot itself. If retained, e.g. using
output <- polarAnnulus(mydata, "nox"), this output can be used to
recover the data, reproduce or rework the original plot or undertake
further analysis.An openair output can be manipulated using a number of generic operations,
including print, plot and summary.
polarAnnulus function shares many of the properties of the
polarPlot. However, polarAnnulus is focussed on displaying
information on how concentrations of a pollutant (values of another
variable) vary with wind direction and time. Plotting as an annulus helps
to reduce compression of information towards the centre of the plot. The
circular plot is easy to interpret because wind direction is most easily
understood in polar rather than Cartesian coordinates.The inner part of the annulus represents the earliest time and the outer part of the annulus the latest time. The time dimension can be shown in many ways including "trend", "hour" (hour or day), "season" (month of the year) and "weekday" (day of the week). Taking hour as an example, the plot will show how concentrations vary by hour of the day and wind direction. Such plots can be very useful for understanding how different source influences affect a location.
For type = "trend" the amount of smoothing does not vary linearly
with the length of the time series i.e. a certain amount of smoothing per
unit interval in time. This is a deliberate choice because should one be
interested in a subset (in time) of data, more detail will be provided for
the subset compared with the full data set. This allows users to
investigate specific periods in more detail. Full flexibility is given
through the smoothing parameter k.
polarPlot, polarFreq,
pollutionRose and percentileRose# load example data from package
data(mydata)
# diurnal plot for PM10 at Marylebone Rd
polarAnnulus(mydata, pollutant = "pm10", main = "diurnal variation in pm10 at Marylebone Road")
# seasonal plot for PM10 at Marylebone Rd
polarAnnulus(mydata, poll="pm10", period = "season")
# trend in coarse particles (PMc = PM10 - PM2.5), calculate PMc first
mydata$pmc <- mydata$pm10 - mydata$pm25
polarAnnulus(mydata, poll="pmc", period = "trend",
main = "trend in pmc at Marylebone Road")Run the code above in your browser using DataLab