percentileRose plots percentiles by wind direction with flexible
conditioning. The plot can display multiple percentile lines or filled areas.
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,
...
)an openair object
A data frame minimally containing wd and a numeric field
to plot --- 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.
Name of wind direction field.
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.
The percentile value(s) to plot. Must be between 0--100. If
percentile = NA then only a mean line will be shown.
Should the wind direction data be smoothed using a cyclic spline?
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).
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.
Default angle of “spokes” is when smooth = FALSE.
Show the mean by wind direction as a line?
Line type for mean line.
Line width for mean line.
Line colour for mean line.
Should the percentile intervals be filled (default) or should
lines be drawn (fill = FALSE).
User-supplied intervals for the scale e.g. intervals =
c(0, 10, 30, 50)
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.
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.
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.
Location where the scale key is to plotted. Allowed
arguments currently include "top", "right", "bottom"
and "left".
Fine control of the scale key via drawOpenKey. See
drawOpenKey for further details.
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.
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.
David Carslaw
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.
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.
Other polar directional analysis functions:
polarAnnulus(),
polarCluster(),
polarDiff(),
polarFreq(),
polarPlot(),
pollutionRose(),
windRose()
# 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) {
# 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")
}
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