R: Traditional wind rose plot and pollution rose variation

windRose {openair} | R Documentation

## Traditional wind rose plot and pollution rose variation

### Description

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.

### Usage

    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 Note, this argument is superseded in `pollutionRose`.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.

### 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.

### 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.

### Note

`windRose` and `pollutionRose` both use drawOpenKey to produce scale keys.

### Author(s)

David Carslaw (with some additional contributions by Karl Ropkins)

### 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)

    ## End(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")

    ## End(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")

[Package openair version 2.8-5 Index]