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polarFreq primarily plots wind speed-direction frequencies in ‘bins’. Each bin is colour-coded depending on the frequency of measurements. Bins can also be used to show the concentration of pollutants using a range of commonly used statistics.


  pollutant = "",
  statistic = "frequency", = 1,
  wd.nint = 36,
  grid.line = 5,
  breaks = seq(0, 5000, 500),
  cols = "default",
  trans = TRUE,
  type = "default",
  min.bin = 1,
  ws.upper = NA,
  offset = 10,
  border.col = "transparent",
  key.header = statistic,
  key.footer = pollutant,
  key.position = "right",
  key = TRUE,
  auto.text = TRUE,



A data frame minimally containing ws, wd and date.


Mandatory. A pollutant name corresponding to a variable in a data frame should be supplied e.g. pollutant = "nox"


The statistic that should be applied to each wind speed/direction bin. Can be “frequency”, “mean”, “median”, “max” (maximum), “stdev” (standard deviation) or “weighted.mean”. The option “frequency” (the default) is the simplest and plots the frequency of wind speed/direction in different bins. The scale therefore shows the counts in each bin. The option “mean” will plot the mean concentration of a pollutant (see next point) in wind speed/direction bins, and so on. Finally, “weighted.mean” will plot the concentration of a pollutant weighted by wind speed/direction. Each segment therefore provides the percentage overall contribution to the total concentration. More information is given in the examples. Note that for options other than “frequency”, it is necessary to also provide the name of a pollutant. See function cutData for further details.

Wind speed interval assumed. In some cases e.g. a low met mast, an interval of 0.5 may be more appropriate.


Number of intervals of wind direction.


Radial spacing of grid lines.


The user can provide their own scale. breaks expects a sequence of numbers that define the range of the scale. The sequence could represent one with equal spacing e.g. breaks = seq(0, 100, 10) - a scale from 0-10 in intervals of 10, or a more flexible sequence e.g. breaks = c(0, 1, 5, 7, 10), which may be useful for some situations.


Colours to be used for plotting. Options include “default”, “increment”, “heat”, “jet” and RColorBrewer colours --- see the openair openColours function for more details. 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")


Should a transformation be applied? Sometimes when producing plots of this kind they can be dominated by a few high points. The default therefore is TRUE and a square-root transform is applied. This results in a non-linear scale and (usually) a better representation of the distribution. If set to FALSE a linear scale is used.


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.


The minimum number of points allowed in a wind speed/wind direction bin. The default is 1. A value of two requires at least 2 valid records in each bin an so on; bins with less than 2 valid records are set to NA. Care should be taken when using a value > 1 because of the risk of removing real data points. It is recommended to consider your data with care. Also, the polarPlot function can be of use in such circumstances.


A user-defined upper wind speed to use. This is useful for ensuring a consistent scale between different plots. For example, to always ensure that wind speeds are displayed between 1-10, set = 10.


offset controls the size of the ‘hole’ in the middle and is expressed as a percentage of the maximum wind speed. Setting a higher offset e.g. 50 is useful for statistic = "weighted.mean" when is greater than the maximum wind speed. See example below.


The colour of the boundary of each wind speed/direction bin. The default is transparent. Another useful choice sometimes is "white".

key.header, key.footer

Adds additional text/labels to the scale key. For example, passing options key.header = "header", key.footer = "footer" adds addition text above and below the scale key. These arguments are passed to drawOpenKey via quickText, applying the auto.text argument, to handle formatting.


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.


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.


Other graphical parameters passed onto lattice:xyplot and cutData. For example, polarFreq passes the option hemisphere = "southern" on to cutData to provide southern (rather than default northern) hemisphere handling of type = "season". Similarly, common axis and title labelling options (such as xlab, ylab, main) are passed to xyplot via quickText to handle routine formatting.


As well as generating the plot itself, polarFreq 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 <- polarFreq(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.


polarFreq is its default use provides details of wind speed and direction frequencies. In this respect it is similar to windRose, but considers wind direction intervals of 10 degrees and a user-specified wind speed interval. The frequency of wind speeds/directions formed by these ‘bins’ is represented on a colour scale.

The polarFreq function is more flexible than either windRose or polarPlot. It can, for example, also consider pollutant concentrations (see examples below). Instead of the number of data points in each bin, the concentration can be shown. Further, a range of statistics can be used to describe each bin - see statistic above. Plotting mean concentrations is useful for source identification and is the same as polarPlot but without smoothing, which may be preferable for some data. Plotting with statistic = "weighted.mean" is particularly useful for understanding the relative importance of different source contributions. For example, high mean concentrations may be observed for high wind speed conditions, but the weighted mean concentration may well show that the contribution to overall concentrations is very low.

polarFreq also offers great flexibility with the scale used and the user has fine control over both the range, interval and colour.


~put references to the literature/web site here ~

See also

See Also as windRose, polarPlot


David Carslaw


# basic wind frequency plot

# wind frequencies by year
if (FALSE) polarFreq(mydata, type = "year")

# mean SO2 by year, showing only bins with at least 2 points
if (FALSE) polarFreq(mydata, pollutant = "so2", type = "year", statistic = "mean", min.bin = 2)

# weighted mean SO2 by year, showing only bins with at least 2 points
if (FALSE) polarFreq(mydata, pollutant = "so2", type = "year", statistic = "weighted.mean",
min.bin = 2)

#windRose for just 2000 and 2003 with different colours
if (FALSE) polarFreq(subset(mydata, format(date, "%Y") %in% c(2000, 2003)),
type = "year", cols = "jet")

# user defined breaks from 0-700 in intervals of 100 (note linear scale)
if (FALSE) polarFreq(mydata, breaks = seq(0, 700, 100))

# more complicated user-defined breaks - useful for highlighting bins
# with a certain number of data points
if (FALSE) polarFreq(mydata, breaks = c(0, 10, 50, 100, 250, 500, 700))

# source contribution plot and use of offset option
if (FALSE) polarFreq(mydata, pollutant = "pm25", statistic
="weighted.mean", offset = 50, = 25, trans = FALSE)