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,
S, W wilNULL, 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, 6, 8 m/sTRUE (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 via key.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 title lawindRose 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.
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