R: Pollution rose variation of the traditional wind rose plot

pollutionRose {openair}

R Documentation

Pollution rose variation of the traditional wind rose plot

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

pollutionRose(
  mydata,
  pollutant = "nox",
  key.footer = pollutant,
  key.position = "right",
  key = TRUE,
  breaks = 6,
  paddle = FALSE,
  seg = 0.9,
  normalise = FALSE,
  alpha = 1,
  plot = TRUE,
  ...
)

Arguments

`mydata`	A data frame containing fields `ws` and `wd`
`pollutant`	Mandatory. A pollutant name corresponding to a variable in a data frame should be supplied e.g. `pollutant = "nox"`.
`key.footer`	Adds additional text/labels below the scale key. See `key.header` for further information.
`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()`.
`breaks`	Most commonly, the number of break points for pollutant concentrations. 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.
`paddle`	Either `TRUE` or `FALSE`. If `TRUE` plots rose using 'paddle' style spokes. If `FALSE` plots rose using 'wedge' style spokes.
`seg`	When `paddle = TRUE`, `seg` determines with width of the segments. For example, `seg = 0.5` will produce segments 0.5 * `angle`.
`normalise`	If `TRUE` each wind direction segment is normalised to equal one. This is useful for showing how the concentrations (or other parameters) contribute to each wind sector when the proportion 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.
`alpha`	The alpha transparency to use for the plotting surface (a value between 0 and 1 with zero being fully transparent and 1 fully opaque). Setting a value below 1 can be useful when plotting surfaces on a map using the package `openairmaps`.
`plot`	Should a plot be produced? `FALSE` can be useful when analysing data to extract plot components and plotting them in other ways.
`...`	Arguments passed on to `windRose` `ws` Name of the column representing wind speed. `wd` Name of the column representing wind direction. `ws2,wd2` The user can supply a second set of wind speed and wind direction values with which the first can be compared. See `pollutionRose()` for more details. `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. `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 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. `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. `offset` The size of the 'hole' in the middle of the plot, expressed as a percentage of the polar axis scale, default 10. `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. `key.header` Adds additional text/labels above the scale key. 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. `dig.lab` The number of significant figures at which scientific number formatting is used in break point and key labelling. Default 5. `include.lowest` Logical. If `FALSE` (the default), the first interval will be left exclusive and right inclusive. If `TRUE`, the first interval will be left and right inclusive. Passed to the `include.lowest` argument of `cut()`. `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. `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 statistic is below the plot. Custom annotations may be added by setting value to `c("annotation 1", "annotation 2")`. `angle.scale` The 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.

Details

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

an openair object. Summarised proportions can 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.

Examples

# 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 2.18-2 Index]