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, ...)

Arguments

mydata

A data frame containing fields ws and wd

ws

Name of the column representing wind speed.

wd

Name of the column representing wind direction.

ws2

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.

wd2

see ws2.

ws.int

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 Note, this argument is superseded in pollutionRose.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.

angle

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

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.

bias.corr

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.

cols

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

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").

width

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.

seg

For pollutionRose seg determines with width of the segments. For example, seg = 0.5 will produce segments 0.5 * angle.

auto.text

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.

breaks

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.

offset

The size of the 'hole' in the middle of the plot, expressed as a percentage of the polar axis scale, default 10.

normalise

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.

max.freq

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.

paddle

Either TRUE (default) or FALSE. If TRUE plots rose using `paddle' style spokes. If FALSE plots rose using `wedge' style spokes.

key.header

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.

key.footer

see key.footer.

key.position

Location where the scale key is to plotted. Allowed arguments currently include “top”, “right”, “bottom” and “left”.

key

Fine control of the scale key via drawOpenKey. See drawOpenKey for further details.

dig.lab

The number of signficant figures at which scientific number formatting is used in break point and key labelling. Default 5.

statistic

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.

pollutant

Alternative data series to be sampled instead of wind speed. The windRose default NULL is equivalent to pollutant = "ws".

annotate

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").

angle.scale

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

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.

Value

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.

Details

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.

Note

windRose and pollutionRose both use drawOpenKey to produce scale keys.

References

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 also

See drawOpenKey for fine control of the scale key.

See polarFreq for a more flexible version that considers other statistics and pollutant concentrations.

Examples

# 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) # }
# pollutionRose of nox pollutionRose(mydata, pollutant = "nox")
## source apportionment plot - contribution to mean
# NOT RUN { 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")