windRose(mydata, ws = "ws", wd = "wd", ws2 = NA, wd2 = NA,
ws.int = 2, angle = 30, type = "default", bias.corr = TRUE, cols = "default",
grid.line = NULL, width = 1, seg = NULL, auto.text = TRUE, breaks
= 4, offset = 10, max.freq = NULL, paddle = TRUE, key.header =
NULL, key.footer = "(m/s)", key.position = "bottom", key = TRUE,
dig.lab = 5, statistic = "prop.count", pollutant = NULL, annotate
= TRUE, border = NA, ...)
pollutionRose(mydata, pollutant = "nox", key.footer = pollutant,
key.position = "right", key = TRUE, breaks = 6, paddle = FALSE,
seg = 0.9, ...)
ws
and wdws2.pollutionRose. See breaks
below.width.type 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. angle does not divide
exactly into 360 a bias is introduced in the frequencies
when the wind direction is already supplied rounded to
the nearest 10 degrees, as is often the case. For
example, if angle = 22.5, N, E, NULL, as in default, this is assigned by
windRose based on the available data range.
However, it can also be forced to a specific value, e.g.
grid.line = 10.paddle = TRUE, the adjustment
factor for width of wind speed intervals. For example,
width = 1.5 will make the paddle width 1.5 times
wider.pollutionRose seg determines
with width of the segments. For example, seg = 0.5
will produce segments 0.5 * angle.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 windRose or pollutant in
pollutionRose. For windRose and the
ws.int default of 2 m/s, the default, 4, generates
the break points 2, 4, TRUE (default) or
FALSE. If TRUE plots rose using `paddle'
style spokes. If FALSE plots rose using `wedge'
style spokes.windRose(mydata, key.header = "ws") adds
the addition text as a scale header. Note: This argument
is passed to drawOpenKey vkey.footer.drawOpenKey. See drawOpenKey for further
details.statistic to be applied to
each data bin in the plot. Options currently include
windRose default NULL
is equivalent to pollutant = "ws".TRUE then the percentage calm
and mean values are printed in each panel.pollutionRose other parameters that
are passed on to windRose. For windRose
other parameters that are passed on to
drawOpenKey, lattice:xyplot and
cutData. Axis and windRose and
pollutionRose also return an object of class
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 <- windRose(mydata),
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
summarise. See openair.generics for
further details.
Summarised proportions can also 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.
windRose data are summarised by direction,
typically by 45 or 30 (or 10) degrees and by different wind
speed categories. Typically, wind speeds are represented by
different width "paddles". The plots show the proportion
(here represented as a percentage) of time that the wind is
from a certain angle and wind speed range.By default windRose will plot a windRose in using
"paddle" style segments and placing the scale key below the
plot.
The argument pollutant uses the same plotting
structure but substitutes another data series, defined by
pollutant, for wind speed.
The option statistic = "prop.mean" provides a
measure of the relative contribution of each bin to the
panel mean, and is intended for use with
pollutionRose.
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.
This paper seems to be the original?
Droppo, J.G. and B.A. Napier (2008) Wind Direction Bias in Generating Wind Roses and Conducting Sector-Based Air Dispersion Modeling, Journal of the Air & Waste Management Association, 58:7, 913-918.
drawOpenKey for fine control of the scale
key.See polarFreq for a more flexible version
that considers other statistics and pollutant
concentrations.
# load example data from package data(mydata)
# basic plot
windRose(mydata)
# one windRose for each year
windRose(mydata,type = "year")
# windRose in 10 degree intervals with gridlines and width adjusted
windRose(mydata, angle = 10, width = 0.2, grid.line = 1)
# pollutionRose of nox
pollutionRose(mydata, pollutant = "nox")
## source apportionment plot - contribution to mean
pollutionRose(mydata, pollutant = "pm10", type = "year", statistic = "prop.mean")
## example of comparing 2 met sites
## first we will make some new ws/wd data with a postive bias
mydata$ws2 = mydata$ws + 2 * rnorm(nrow(mydata)) + 1
mydata$wd2 = mydata$wd + 30 * rnorm(nrow(mydata)) + 30
## need to correct negative wd
id <- which(mydata$wd2 < 0)
mydata$wd2[id] <- mydata$wd2[id] + 360
## results show postive bias in wd and ws
pollutionRose(mydata, ws = "ws", wd = "wd", ws2 = "ws2", wd2 = "wd2")Run the code above in your browser using DataLab