openair (version 0.3-8)

polarAnnulus: Bivariate polarAnnulus plot

Description

Typically plots the concentration of a pollutant by wind direction and as a function of time as an annulus. The function is good for visualising how concentrations of pollutants vary by wind direction and a time period e.g. by month, day of week.

Usage

polarAnnulus(polar,
    pollutant = "nox", resolution = "fine",
    local.time = FALSE, period = "hour", type = "default",
    limits = c(0, 100), cols = "default", width = "normal",
    exclude.missing = TRUE, date.pad = FALSE, 
    force.positive = TRUE, k = 15, main = "", 
    key.header = "", key.footer = pollutant, 
    key.position = "right", key = NULL, 
    auto.text = TRUE, ...)

Arguments

polar

A data frame minimally containing ws, wd and a pollutant. Can also contain date if plots by time period are required.

pollutant

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

resolution

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

local.time

Should the results be calculated in local time? The default is TRUE. 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 in

period

This determines the temporal period to consider. Options are "trend" (the default), "season" to plot variation throughout the year, "weekday" to plot day of the week variation and "hour" to plot the diurnal variation.

type

The type can either be "default" or "site". The former will produce a single plot and the latter produces the same number of plots as there are sites. It is also possible to choose type as anoth

limits

The function does its best to choose sensible limits automatically. However, there are circumstances when the user will wish to set different ones. An example would be a series of plots showing each year of data separately. The limits are set in

cols

Colours to be used for plotting. Options include "default", "increment", "heat", "jet" 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 wo

width

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

exclude.missing

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

date.pad

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

force.positive

The default is TRUE. Sometimes if smoothing data with streep gradients it is possible for predicted values to be negative. force.positive = TRUE ensures that predictions remain postive. This is useful for several reasons

k

This is the smoothing parameter that is set if auto.smooth is set to FALSE. Typically, value of around 15 (the default) seems to be suitable and will resolve more features in the plot. For type = "trend" k = 20

main

Title of plot.

key.header, key.footer

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

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.

Warning

The function is written mostly for more than 1-year of hourly data. If less than 1-year of data are used and type = "trend" then type = "season" will be used, which should have the desired effect. Setting k too high may result in an error if there are insufficient data to justify such detailed smoothing. Calculations will take longer as k increases.

Details

The polarAnnulus function shares many of the properties of the polarPlot. However, polarAnnulus is focussed on displaying information on how concentrations of a pollutant (values of another variable) vary with wind direction and time. Plotting as an annulus helps to reduce compression of information towards the centre of the plot. The circular plot is easy to interpret because wind direction is most easily understood in polar rather than Cartesian coordinates. The inner part of the annulus represents the earliest time and the outer part of the annulus the latest time. The time dimension can be shown in many ways including "trend" (default), "hour" (hour or day), "season" (month of the year) and "weekday" (day of the week). Taking hour as an example, the plot will show how concentrations vary by hour of the day and wind direction. Such plots can be very useful for understanding how different source influences affect a location. For type = "trend" the amount of smoothing does not vary linearly with the length of the time series i.e. a certain amount of smoothing per unit interval in time. This is a deliberate choice because should one be interested in a subset (in time) of data, more detail will be provided for the subset compared with the full data set. This allows users to investigate specific periods in morte detail. Full flexibility is given through the smoothing parameter k.

See Also

polarPlot, polarFreq

Examples

# load example data from package
data(mydata)

# trend plot for PM10 at Marylebone Rd
polarAnnulus(mydata, poll="pm10", main = "trend in pm10 at Marylebone Road")

# seasonal plot for PM10 at Marylebone Rd
polarAnnulus(mydata, poll="pm10", period = "season")

# trend in coarse particles (PMc = PM10 - PM2.5), calculate PMc first

mydata$pmc <- mydata$pm10 - mydata$pm25
polarAnnulus(mydata, poll="pmc", period = "trend", main = "trend in pmc at Marylebone Road")