polarAnnulus(mydata, pollutant = "nox", resolution = "fine",
local.time = FALSE, 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 usinFALSE. 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 released intype 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
TRUE
(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 totype = "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 endTRUE.
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.
gam 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 moTRUE 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 pollukey.header = "header", key.footer = "footer1" adds
addition text above and below the scale key. These
arguments are passed to drawOpenKey viakey.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 thpolarAnnulus
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