before |
A data frame that represents the "before" case. See
polarPlot() for details of different input requirements.
|
after |
A data frame that represents the "after" case. See polarPlot()
for details of different input requirements.
|
pollutant |
Mandatory. A pollutant name corresponding to a variable in a
data frame should be supplied e.g. pollutant = "nox" . There can also
be more than one pollutant specified e.g. pollutant = c("nox",
"no2") . The main use of using two or more pollutants is for model
evaluation where two species would be expected to have similar
concentrations. This saves the user stacking the data and it is possible to
work with columns of data directly. A typical use would be pollutant
= c("obs", "mod") to compare two columns “obs” (the observations)
and “mod” (modelled values). When pair-wise statistics such as
Pearson correlation and regression techniques are to be plotted,
pollutant takes two elements too. For example, pollutant =
c("bc", "pm25") where "bc" is a function of "pm25" .
|
x |
Name of variable to plot against wind direction in polar
coordinates, the default is wind speed, “ws”.
|
limits |
By default, each individual polar marker has its own colour
scale. The limits argument will force all markers to use the same colour
scale. The limits are set in the form c(lower, upper) , so limits = c(-5, 5) would force the plot limits to span -5 to 5. It is recommended to use a
symmetrical limit scale (along with a "diverging" colour palette) for
effective visualisation.
|
latitude , longitude |
The decimal latitude/longitude. If not provided,
will be automatically inferred from data by looking for a column named
"lat"/"latitude" or "lon"/"lng"/"long"/"longitude" (case-insensitively).
|
control |
Used for splitting the input data into different groups which
can be selected between using a "layer control" interface, passed to the
type argument of openair::cutData() . control cannot be used if
multiple pollutant columns have been provided.
|
|
Columns to be used as the HTML content for marker popups. Popups
may be useful to show information about the individual sites (e.g., site
names, codes, types, etc.). If a vector of column names are provided they
are passed to buildPopup() using its default values.
|
label |
Column to be used as the HTML content for hover-over labels.
Labels are useful for the same reasons as popups, though are typically
shorter.
|
provider |
The base map(s) to be used. See
http://leaflet-extras.github.io/leaflet-providers/preview/ for a list of
all base maps that can be used. If multiple base maps are provided, they
can be toggled between using a "layer control" interface. By default, the
interface will use the provider names as labels, but users can define their
own using a named vector (e.g., c("Default" = "OpenStreetMap", "Satellite" = "Esri.WorldImagery") )
|
cols |
The colours used for plotting. It is recommended to use a
"diverging" colour palette (along with a symmetrical limit scale) for
effective visualisation.
|
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).
|
key |
Should a key for each marker be drawn? Default is FALSE .
|
draw.legend |
When limits are specified, should a shared legend be
created at the side of the map? Default is TRUE .
|
collapse.control |
Should the "layer control" interface be collapsed?
Defaults to FALSE .
|
d.icon |
The diameter of the plot on the map in pixels. This will affect
the size of the individual polar markers. Alternatively, a vector in the
form c(width, height) can be provided if a non-circular marker is
desired.
|
d.fig |
The diameter of the plots to be produced using openair in
inches. This will affect the resolution of the markers on the map.
Alternatively, a vector in the form c(width, height) can be provided if a
non-circular marker is desired.
|
type |
. Different sites are now
automatically detected based on latitude and longitude. Please use label
and/or popup to label different sites.
|
... |
Arguments passed on to openair::polarPlot
wd Name of wind direction field.
statistic The statistic that should be applied to each wind
speed/direction bin. Because of the smoothing involved, the colour scale
for some of these statistics is only to provide an indication of overall
pattern and should not be interpreted in concentration units e.g. for
statistic = "weighted.mean" where the bin mean is multiplied by the
bin frequency and divided by the total frequency. In many cases using
polarFreq will be better. Setting statistic = "weighted.mean"
can be useful because it provides an indication of the concentration *
frequency of occurrence and will highlight the wind speed/direction
conditions that dominate the overall mean.Can be:
-
“mean” (default), “median”, “max”
(maximum), “frequency”. “stdev” (standard deviation),
“weighted.mean”.
-
statistic = "nwr" Implements the Non-parametric Wind
Regression approach of Henry et al. (2009) that uses kernel smoothers. The
openair implementation is not identical because Gaussian kernels are
used for both wind direction and speed. The smoothing is controlled by
ws_spread and wd_spread .
-
statistic = "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 y
bin (by default a wind direction, wind speed interval) 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). Note that percentile intervals can also be considered; see
percentile for details.
When statistic = "r" or statistic = "Pearson" , the
Pearson correlation coefficient is calculated for two pollutants.
The calculation involves a weighted Pearson correlation coefficient, which
is weighted by Gaussian kernels for wind direction an the radial variable
(by default wind speed). More weight is assigned to values close to a wind
speed-direction interval. Kernel weighting is used to ensure that all data
are used rather than relying on the potentially small number of values in a
wind speed-direction interval.
When statistic = "Spearman" , the Spearman correlation
coefficient is calculated for two pollutants. The calculation
involves a weighted Spearman correlation coefficient, which is weighted by
Gaussian kernels for wind direction an the radial variable (by default wind
speed). More weight is assigned to values close to a wind speed-direction
interval. Kernel weighting is used to ensure that all data are used rather
than relying on the potentially small number of values in a wind
speed-direction interval.
-
"robust_slope" is another option for pair-wise statistics and
"quantile.slope" , which uses quantile regression to estimate the
slope for a particular quantile level (see also tau for setting the
quantile level).
-
"york_slope" is another option for pair-wise statistics which
uses the York regression method to estimate the slope. In this
method the uncertainties in x and y are used in the
determination of the slope. The uncertainties are provided by
x_error and y_error — see below.
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 too far from the original data
produces a plot where it is clear where the original data lie. If set to
FALSE missing data will be interpolated.
uncertainty Should the uncertainty in the calculated surface be shown?
If TRUE three plots are produced on the same scale showing the
predicted surface together with the estimated lower and upper uncertainties
at the 95% confidence interval. Calculating the uncertainties is useful to
understand whether features are real or not. For example, at high wind
speeds where there are few data there is greater uncertainty over the
predicted values. The uncertainties are calculated using the GAM and
weighting is done by the frequency of measurements in each wind
speed-direction bin. Note that if uncertainties are calculated then the
type is set to "default".
percentile If statistic = "percentile" then percentile
is used, expressed from 0 to 100. Note that the percentile value is
calculated in the wind speed, wind direction ‘bins’. For this reason
it can also be useful to set min.bin to ensure there are a
sufficient number of points available to estimate a percentile. See
quantile for more details of how percentiles are calculated.
percentile is also used for the Conditional Probability Function
(CPF) plots. percentile can be of length two, in which case the
percentile interval is considered for use with CPF. For example,
percentile = c(90, 100) will plot the CPF for concentrations between
the 90 and 100th percentiles. Percentile intervals can be useful for
identifying specific sources. In addition, percentile can also be of
length 3. The third value is the ‘trim’ value to be applied. When
calculating percentile intervals many can cover very low values where there
is no useful information. The trim value ensures that values greater than
or equal to the trim * mean value are considered before the
percentile intervals are calculated. The effect is to extract more detail
from many source signatures. See the manual for examples. Finally, if the
trim value is less than zero the percentile range is interpreted as
absolute concentration values and subsetting is carried out directly.
weights At the edges of the plot there may only be a few data points
in each wind speed-direction interval, which could in some situations
distort the plot if the concentrations are high. weights applies a
weighting to reduce their influence. For example and by default if only a
single data point exists then the weighting factor is 0.25 and for two
points 0.5. To not apply any weighting and use the data as is, use
weights = c(1, 1, 1) .
An alternative to down-weighting these points they can be removed
altogether using min.bin .
min.bin The minimum number of points allowed in a wind speed/wind
direction bin. The default is 1. A value of two requires at least 2 valid
records in each bin an so on; bins with less than 2 valid records are set
to NA. Care should be taken when using a value > 1 because of the risk of
removing real data points. It is recommended to consider your data with
care. Also, the polarFreq function can be of use in such
circumstances.
mis.col When min.bin is > 1 it can be useful to show where data
are removed on the plots. This is done by shading the missing data in
mis.col . To not highlight missing data when min.bin > 1
choose mis.col = "transparent" .
upper This sets the upper limit wind speed to be used. Often there are
only a relatively few data points at very high wind speeds and plotting all
of them can reduce the useful information in the plot.
force.positive The default is TRUE . Sometimes if smoothing data
with steep gradients it is possible for predicted values to be negative.
force.positive = TRUE ensures that predictions remain positive. This
is useful for several reasons. First, with lots of missing data more
interpolation is needed and this can result in artefacts because the
predictions are too far from the original data. Second, if it is known
beforehand that the data are all positive, then this option carries that
assumption through to the prediction. The only likely time where setting
force.positive = FALSE would be if background concentrations were
first subtracted resulting in data that is legitimately negative. For the
vast majority of situations it is expected that the user will not need to
alter the default option.
k This is the smoothing parameter used by the gam function in
package mgcv . Typically, value of around 100 (the default) seems to
be suitable and will resolve important features in the plot. The most
appropriate choice of k is problem-dependent; but extensive testing
of polar plots for many different problems suggests a value of k of
about 100 is suitable. Setting k to higher values will not tend to
affect the surface predictions by much but will add to the computation
time. Lower values of k will increase smoothing. Sometimes with few
data to plot polarPlot will fail. Under these circumstances it can
be worth lowering the value of k .
normalise If TRUE concentrations are normalised by dividing by
their mean value. This is done after fitting the smooth surface.
This option is particularly useful if one is interested in the patterns of
concentrations for several pollutants on different scales e.g. NOx and CO.
Often useful if more than one pollutant is chosen.
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.
ws_spread The value of sigma used for Gaussian kernel weighting of
wind speed when statistic = "nwr" or when correlation and regression
statistics are used such as r. Default is 0.5 .
wd_spread The value of sigma used for Gaussian kernel weighting of
wind direction when statistic = "nwr" or when correlation and
regression statistics are used such as r. Default is 4 .
x_error The x error / uncertainty used when statistic =
"york_slope" .
y_error The y error / uncertainty used when statistic =
"york_slope" .
kernel Type of kernel used for the weighting procedure for when
correlation or regression techniques are used. Only "gaussian" is
supported but this may be enhanced in the future.
formula.label When pair-wise statistics such as regression slopes are
calculated and plotted, should a formula label be displayed?
tau The quantile to be estimated when statistic is set to
"quantile.slope" . Default is 0.5 which is equal to the median
and will be ignored if "quantile.slope" is not used.
plot Should a plot be produced? FALSE can be useful when
analysing data to extract plot components and plotting them in other ways.
|
A leaflet object.