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windroseMapStatic() creates a ggplot2 map using wind roses as markers. As this function returns a ggplot2 object, further customisation can be achieved using functions like ggplot2::theme() and ggplot2::guides(). See openair::polarPlot() for more information.


  data, = 2,
  breaks = 4,
  facet = NULL,
  latitude = NULL,
  longitude = NULL,
  crs = 4326,
  provider = "osm",
  cols = "turbo",
  alpha = 1,
  key = FALSE,
  facet.nrow = NULL,
  d.icon = 150,
  d.fig = 3,



A data frame. The data frame must contain the data to plot the directional analysis marker, which includes wind speed (ws), wind direction (wd), and the column representing the concentration of a pollutant. In addition, data must include a decimal latitude and longitude.

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.


Most commonly, the number of break points for wind speed in windRose. For windRose and the default of 2 m/s, the default, 4, generates the break points 2, 4, 6, 8 m/s. 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.


Used for splitting the input data into different panels, passed to the type argument of openair::cutData(). facet cannot be used if multiple pollutant columns have been provided.

latitude, longitude

The decimal latitude/longitude (or other Y/X coordinate if using a different crs). If not provided, will be automatically inferred from data by looking for a column named "lat"/"latitude" or "lon"/"lng"/"long"/"longitude" (case-insensitively).


The coordinate reference system (CRS) of the data, passed to sf::st_crs(). By default this is EPSG:4326, the CRS associated with the commonly used latitude and longitude coordinates. Different coordinate systems can be specified using crs (e.g., crs = 27700 for the British National Grid). Note that non-lat/lng coordinate systems will be re-projected to EPSG:4326 for plotting on the map.


The base map to be used. See rosm::osm.types() for a list of all base maps that can be used.


The colours used for plotting. See openair::openColours() for more information.


The alpha transparency to use for the plotting surface (a value between 0 and 1 with zero being fully transparent and 1 fully opaque).


Should a key for each marker be drawn? Default is FALSE.


Passed to the nrow argument of ggplot2::facet_wrap().


The diameter of the plot on the map in pixels. This will affect the size of the individual polar markers. Alternatively, a vector in the form c(width, height) can be provided if a non-circular marker is desired.


The diameter of the plots to be produced using openair in inches. This will affect the resolution of the markers on the map. Alternatively, a vector in the form c(width, height) can be provided if a non-circular marker is desired.


Arguments passed on to openair::polarAnnulus


Two plot resolutions can be set: “normal” and “fine” (the default).

Should the results be calculated in local time that includes a treatment of daylight savings time (DST)? The default is not to consider DST issues, provided the data were imported without a DST offset. Emissions activity tends to occur at local time e.g. rush hour is at 8 am every day. When the clocks go forward in spring, the emissions are effectively released into the atmosphere typically 1 hour earlier during the summertime i.e. when DST applies. When plotting diurnal profiles, this has the effect of “smearing-out” the concentrations. Sometimes, a useful approach is to express time as local time. This correction tends to produce better-defined diurnal profiles of concentration (or other variables) and allows a better comparison to be made with emissions/activity data. If set to FALSE then GMT is used. Examples of usage include = "Europe/London", = "America/New_York". See cutData and import for more details.


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", "site") will produce a 2x2 plot split by season and site. The use of two types is mostly meant for situations where there are several sites. Note, when two types are provided the first forms the columns and the second the rows.

Also note that for the polarAnnulus function some type/period combinations are forbidden or make little sense. For example, type = "season" and period = "trend" (which would result in a plot with too many gaps in it for sensible smoothing), or type = "weekday" and period = "weekday".


The statistic that should be applied to each wind speed/direction bin. Can be “mean” (default), “median”, “max” (maximum), “frequency”. “stdev” (standard deviation), “weighted.mean” or “cpf” (Conditional Probability Function). Because of the smoothing involved, the colour scale for some of these statistics is only to provide an indication of overall pattern and should not be interpreted in concentration units e.g. for statistic = "weighted.mean" where the bin mean is multiplied by the bin frequency and divided by the total frequency. In many cases using polarFreq will be better. Setting statistic = "weighted.mean" can be useful because it provides an indication of the concentration * frequency of occurrence and will highlight the wind speed/direction conditions that dominate the overall mean.


If statistic = "percentile" or statistic = "cpf" then percentile is used, expressed from 0 to 100. Note that the percentile value is calculated in the wind speed, wind direction ‘bins’. For this reason it can also be useful to set min.bin to ensure there are a sufficient number of points available to estimate a percentile. See quantile for more details of how percentiles are calculated.


The width of the annulus; can be “normal” (the default), “thin” or “fat”.


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 polarFreq function can be of use in such circumstances.


Setting this option to TRUE (the default) removes points from the plot that are too far from the original data. The smoothing routines will produce predictions at points where no data exist i.e. they predict. By removing the points too far from the original data produces a plot where it is clear where the original data lie. If set to FALSE missing data will be interpolated.


For type = "trend" (default), date.pad = TRUE will pad-out missing data to the beginning of the first year and the end of the last year. The purpose is to ensure that the trend plot begins and ends at the beginning or end of year.


The default is TRUE. Sometimes if smoothing data with steep gradients it is possible for predicted values to be negative. force.positive = TRUE ensures that predictions remain positive. This is useful for several reasons. First, with lots of missing data more interpolation is needed and this can result in artefacts because the predictions are too far from the original data. Second, if it is known beforehand that the data are all positive, then this option carries that assumption through to the prediction. The only likely time where setting force.positive = FALSE would be if background concentrations were first subtracted resulting in data that is legitimately negative. For the vast majority of situations it is expected that the user will not need to alter the default option.


The smoothing value supplied to gam for the temporal and wind direction components, respectively. In some cases e.g. a trend plot with less than 1-year of data the smoothing with the default values may become too noisy and affected more by outliers. Choosing a lower value of k (say 10) may help produce a better plot.


If TRUE concentrations are normalised by dividing by their mean value. This is done after fitting the smooth surface. This option is particularly useful if one is interested in the patterns of concentrations for several pollutants on different scales e.g. NOx and CO. Often useful if more than one pollutant is chosen.


Adds additional text/labels to the scale key. For example, passing the options key.header = "header", key.footer = "footer1" 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.


see key.footer.


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


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.


a ggplot2 plot with a ggspatial basemap

Further customisation using ggplot2

As the outputs of the static directional analysis functions are ggplot2 figures, further customisation is possible using functions such as ggplot2::theme(), ggplot2::guides() and ggplot2::labs().

If multiple pollutants are specified, subscripting (e.g., the "x" in "NOx") is achieved using the ggtext package. Therefore if you choose to override the plot theme, it is recommended to use [ggplot2::theme()] and [ggtext::element_markdown()] to define the strip.text parameter.

When arguments like limits, percentile or breaks are defined, a legend is automatically added to the figure. Legends can be removed using ggplot2::theme(legend.position = "none"), or further customised using ggplot2::guides() and either color = ggplot2::guide_colourbar() for continuous legends or fill = ggplot2::guide_legend() for discrete legends.

See also

the original openair::windRose()

windroseMap() for the interactive leaflet equivalent of windroseMapStatic()

Other static directional analysis maps: annulusMapStatic(), diffMapStatic(), freqMapStatic(), percentileMapStatic(), polarMapStatic(), pollroseMapStatic()