polarAnnulus(mydata, pollutant = "nox",
resolution = "fine", local.time = FALSE,
period = "hour", type = "default", 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 "normal" and "fine" (the default).TRUE. Emissions
activity tends to occur at local time e.g. rush hour is
at 8 am every day. When the clocks go forward in spring,
the emissions are effectively releasedtype 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. "season", "colours() to
see the full list). An exampleTRUE
(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 pointtype = "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 orTRUE.
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 reasogam 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 affecteTRUE 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 pkey.header = "header", key.footer = "footer1" adds
addition text above and below the scale key. These
arguments are passed to drawOpenKeykey.header."top", "right", "bottom" and
"left".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
(rathepolarAnnulus 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. See openair.generics
for further details.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