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Function to plot percentiles by wind direction

Source: R/percentileRose.R
percentileRose.Rd

percentileRose plots percentiles by wind direction with flexible conditioning. The plot can display multiple percentile lines or filled areas.

Usage

percentileRose(
  mydata,
  pollutant = "nox",
  wd = "wd",
  type = "default",
  percentile = c(25, 50, 75, 90, 95),
  smooth = FALSE,
  method = "default",
  cols = "default",
  angle = 10,
  mean = TRUE,
  mean.lty = 1,
  mean.lwd = 3,
  mean.col = "grey",
  fill = TRUE,
  intervals = NULL,
  angle.scale = 45,
  auto.text = TRUE,
  key.header = NULL,
  key.footer = "percentile",
  key.position = "bottom",
  key = TRUE,
  alpha = 1,
  plot = TRUE,
  ...
)

Arguments

mydata

A data frame minimally containing wd and a numeric field to plot — pollutant.

pollutant

Mandatory. A pollutant name corresponding to a variable in a data frame should be supplied e.g. pollutant = "nox". More than one pollutant can be supplied e.g. pollutant = c("no2", "o3") provided there is only one type.

wd

Name of wind direction field.

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.

percentile

The percentile value(s) to plot. Must be between 0–100. If percentile = NA then only a mean line will be shown.

smooth

Should the wind direction data be smoothed using a cyclic spline?

method

When method = "default" the supplied percentiles by wind direction are calculated. When method = "cpf" the conditional probability function (CPF) is plotted and a single (usually high) percentile level is supplied. The CPF is defined as CPF = my/ny, where my is the number of samples in the wind sector y with mixing ratios greater than the overall percentile concentration, and ny is the total number of samples in the same wind sector (see Ashbaugh et al., 1985).

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"). cols can also take the values "viridis", "magma", "inferno", or "plasma" which are the viridis colour maps ported from Python's Matplotlib library.

angle

Default angle of “spokes” is when smooth = FALSE.

mean

Show the mean by wind direction as a line?

mean.lty

Line type for mean line.

mean.lwd

Line width for mean line.

mean.col

Line colour for mean line.

fill

Should the percentile intervals be filled (default) or should lines be drawn (fill = FALSE).

intervals

User-supplied intervals for the scale e.g. intervals = c(0, 10, 30, 50)

angle.scale

Sometimes the placement of the scale may interfere with an interesting feature. The user can therefore set angle.scale to any value between 0 and 360 degrees to mitigate such problems. For example angle.scale = 45 will draw the scale heading in a NE direction.

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.

key.header

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.

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.

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.

...

Other graphical parameters are passed onto cutData and lattice:xyplot. For example, percentileRose passes the option hemisphere = "southern" on to cutData to provide southern (rather than default northern) hemisphere handling of type = "season". Similarly, common graphical arguments, such as xlim and ylim for plotting ranges and lwd for line thickness when using fill = FALSE, are passed on xyplot, although some local modifications may be applied by openair. For example, axis and title labelling options (such as xlab, ylab and main) are passed to xyplot via quickText to handle routine formatting.

Value

an openair object

Details

percentileRose calculates percentile levels of a pollutant and plots them by wind direction. One or more percentile levels can be calculated and these are displayed as either filled areas or as lines.

The wind directions are rounded to the nearest 10 degrees, consistent with surface data from the UK Met Office before a smooth is fitted. The levels by wind direction are optionally calculated using a cyclic smooth cubic spline using the option smooth. If smooth = FALSE then the data are shown in 10 degree sectors.

The percentileRose function compliments other similar functions including windRose, pollutionRose, polarFreq or polarPlot. It is most useful for showing the distribution of concentrations by wind direction and often can reveal different sources e.g. those that only affect high percentile concentrations such as a chimney stack.

Similar to other functions, flexible conditioning is available through the type option. It is easy for example to consider multiple percentile values for a pollutant by season, year and so on. See examples below.

percentileRose also offers great flexibility with the scale used and the user has fine control over both the range, interval and colour.

References

Ashbaugh, L.L., Malm, W.C., Sadeh, W.Z., 1985. A residence time probability analysis of sulfur concentrations at ground canyon national park. Atmospheric Environment 19 (8), 1263-1270.

See also

Other polar directional analysis functions: polarAnnulus(), polarCluster(), polarDiff(), polarFreq(), polarPlot(), pollutionRose(), windRose()

Author

David Carslaw

Examples

# basic percentile plot
percentileRose(mydata, pollutant = "o3")


# 50/95th percentiles of ozone, with different colours
percentileRose(mydata, pollutant = "o3", percentile = c(50, 95), col = "brewer1")


if (FALSE) { # \dontrun{
# percentiles of ozone by year, with different colours
percentileRose(mydata, type = "year", pollutant = "o3", col = "brewer1")

# percentile concentrations by season and day/nighttime..
percentileRose(mydata, type = c("season", "daylight"), pollutant = "o3", col = "brewer1")
} # }