openair: polarFreq – R documentation

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polarFreq

Function to plot wind speed/direction frequencies and other statistics

Description

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.

Usage

polarFreq(
  mydata,
  pollutant = "",
  statistic = "frequency",
  ws.int = 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,
  ...
)

Arguments

`mydata`	A data frame minimally containing `ws`, `wd` and `date`.
`pollutant`	Mandatory. A pollutant name corresponding to a variable in a data frame should be supplied e.g. `pollutant = "nox"`
`statistic`	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.
`ws.int`	Wind speed interval assumed. In some cases e.g. a low met mast, an interval of 0.5 may be more appropriate.
`wd.nint`	Number of intervals of wind direction.
`grid.line`	Radial spacing of grid lines.
`breaks`	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.
`cols`	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")`
`trans`	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`	`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.
`min.bin`	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.
`ws.upper`	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 `ws.int = 10`.
`offset`	`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 `ws.int` is greater than the maximum wind speed. See example below.
`border.col`	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.
`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.
`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.
`...`	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.

Details

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.

Value

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.

Author(s)

David Carslaw

References

~put references to the literature/web site here ~

Examples

# basic wind frequency plot
polarFreq(mydata)

# wind frequencies by year
## Not run: polarFreq(mydata, type = "year")


# mean SO2 by year, showing only bins with at least 2 points
## Not run: polarFreq(mydata, pollutant = "so2", type = "year", statistic = "mean", min.bin = 2)

# weighted mean SO2 by year, showing only bins with at least 2 points
## Not run: polarFreq(mydata, pollutant = "so2", type = "year", statistic = "weighted.mean",
min.bin = 2)
## End(Not run)

#windRose for just 2000 and 2003 with different colours
## Not run: polarFreq(subset(mydata, format(date, "%Y") %in% c(2000, 2003)),
type = "year", cols = "jet")
## End(Not run)

# user defined breaks from 0-700 in intervals of 100 (note linear scale)
## Not run: polarFreq(mydata, breaks = seq(0, 700, 100))

# more complicated user-defined breaks - useful for highlighting bins
# with a certain number of data points
## Not run: polarFreq(mydata, breaks = c(0, 10, 50, 100, 250, 500, 700))

# source contribution plot and use of offset option
## Not run: polarFreq(mydata, pollutant = "pm25", statistic
="weighted.mean", offset = 50, ws.int = 25, trans = FALSE) 
## End(Not run)

openair

Tools for the Analysis of Air Pollution Data

v2.10-0

GPL (>= 2)

Authors

David Carslaw [aut, cre], Karl Ropkins [aut]

Initial release

2022-06-21