The traditional wind rose plot that plots wind speed and wind direction by different intervals. The pollution rose applies the same plot structure but substitutes other measurements, most commonly a pollutant time series, for wind speed.
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, normalise = FALSE, 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, angle.scale =
315, border = NA, ...)
pollutionRose(mydata, pollutant = "nox", key.footer = pollutant,
key.position = "right", key = TRUE, breaks = 6, paddle = FALSE,
seg = 0.9, normalise = FALSE, ...)
A data frame containing fields ws and
wd
Name of the column representing wind speed.
Name of the column representing wind direction.
The user can supply a second set of wind speed and wind direction values with which the first can be compared. See details below for full explanation.
see ws2.
The Wind speed interval. Default is 2 m/s but for
low met masts with low mean wind speeds a value of 1 or 0.5 m/s
may be better. Note, this argument is superseded in
pollutionRose. See breaks below.
Default angle of “spokes” is 30. Other
potentially useful angles are 45 and 10. Note that the width of
the wind speed interval may need adjusting using 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.
“season”, “year”, “weekday” and so on. For
example, type = "season" will produce four plots --- one
for each season.
It is also possible to choose type as another variable in
the data frame. If that variable is numeric, then the data will
be split into four quantiles (if possible) and labelled
accordingly. If type is an existing character or factor
variable, then those categories/levels will be used directly.
This offers great flexibility for understanding the variation of
different variables and how they depend on one another.
Type can be up length two e.g. type = c("season",
"weekday") will produce a 2x2 plot split by season and day of
the week. Note, when two types are provided the first forms the
columns and the second the rows.
When 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 will include 3 wind sectors and all other angles will be
two. A bias correction can made to correct for this problem. A
simple method according to Applequist (2012) is used to adjust
the frequencies.
Colours to be used for plotting. Options include
“default”, “increment”, “heat”,
“jet”, “hue” and user defined. For user defined
the user can supply a list of colour names recognised by R (type
colours() to see the full list). An example would be
cols = c("yellow", "green", "blue", "black").
Grid line interval to use. If 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. grid.line can
also be a list to control the interval, line type and colour.
For example grid.line = list(value = 10, lty = 5, col =
"purple").
For 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.
For pollutionRose seg determines with
width of the segments. For example, seg = 0.5 will
produce segments 0.5 * angle.
Either 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 ‘2’ in NO2.
Most commonly, the number of break points for wind
speed in 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/s. For
pollutionRose, the default, 6, attempts to breaks the
supplied data at approximately 6 sensible break points. However,
breaks can also be used to set specific break points. For
example, the argument breaks = c(0, 1, 10, 100) breaks
the data into segments <1, 1-10, 10-100, >100.
The size of the 'hole' in the middle of the plot, expressed as a percentage of the polar axis scale, default 10.
If TRUE each wind direction segment of a
pollution rose is normalised to equal one. This is useful for
showing how the concentrations (or other parameters) contribute
to each wind sector when the proprtion of time the wind is from
that direction is low. A line showing the probability that the
wind directions is from a particular wind sector is also shown.
Controls the scaling used by setting the maximum value for the radial limits. This is useful to ensure several plots use the same radial limits.
Either TRUE (default) or FALSE. If
TRUE plots rose using `paddle' style spokes. If
FALSE plots rose using `wedge' style spokes.
Adds additional text/labels above and/or below
the scale key, respectively. For example, passing
windRose(mydata, key.header = "ws") adds the addition
text as a scale header. Note: This argument is passed to
drawOpenKey via quickText, applying the auto.text
argument, to handle formatting.
see key.footer.
Location where the scale key is to plotted. Allowed arguments currently include “top”, “right”, “bottom” and “left”.
Fine control of the scale key via drawOpenKey.
See drawOpenKey for further details.
The number of signficant figures at which scientific number formatting is used in break point and key labelling. Default 5.
The statistic to be applied to each data
bin in the plot. Options currently include “prop.count”,
“prop.mean” and “abs.count”. The default
“prop.count” sizes bins according to the proportion of
the frequency of measurements. Similarly, “prop.mean”
sizes bins according to their relative contribution to the mean.
“abs.count” provides the absolute count of measurements
in each bin.
Alternative data series to be sampled instead of
wind speed. The windRose default NULL is equivalent to
pollutant = "ws".
If TRUE then the percentage calm and mean
values are printed in each panel together with a description of
the statistic below the plot. If " " then only the
stastic is below the plot. Custom annotations may be added by
setting value to c("annotation 1", "annotation 2").
The wind speed scale is by default shown at a
315 degree angle. Sometimes the placement of the scale may
interfere with an interesting feature. The user can therefore
set angle.scale to another value (between 0 and 360
degrees) to mitigate such problems. For example
angle.scale = 45 will draw the scale heading in a NE
direction.
Border colour for shaded areas. Default is no border.
For 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
labelling options (xlab, ylab, main) are
passed to xyplot via quickText to handle routine
formatting.
As well as generating the plot itself, windRose and
pollutionRose also return 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 <- 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.
For 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.
Applequist, S, 2012: Wind Rose Bias Correction. J. Appl. Meteor. Climatol., 51, 1305-1309.
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.
See drawOpenKey for fine control of the
scale key.
See polarFreq for a more flexible version that
considers other statistics and pollutant concentrations.
# NOT RUN {
# 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
# }
# NOT RUN {
windRose(mydata, angle = 10, width = 0.2, grid.line = 1)
# }
# NOT RUN {
# pollutionRose of nox
pollutionRose(mydata, pollutant = "nox")
## source apportionment plot - contribution to mean
# }
# NOT RUN {
pollutionRose(mydata, pollutant = "pm10", type = "year", statistic = "prop.mean")
# }
# NOT RUN {
## 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")
# }
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