windroseMapStatic {openairmaps} | R Documentation |
Wind rose plots on a static ggmap
Description
windroseMapStatic()
creates a ggplot2
map using wind roses as markers. As
this function returns a ggplot2
object, further customisation can be
achieved using functions like ggplot2::theme()
and ggplot2::guides()
. See
openair::polarPlot()
for more information.
Usage
windroseMapStatic(
data,
ggmap = NULL,
ws.int = 2,
breaks = 4,
facet = NULL,
latitude = NULL,
longitude = NULL,
cols = "turbo",
alpha = 1,
key = FALSE,
facet.nrow = NULL,
d.icon = 150,
d.fig = 3,
...
)
Arguments
data |
A data frame. The data frame must contain the data to plot the
directional analysis marker, which includes wind speed (ws ), wind
direction (wd ), and the column representing the concentration of a
pollutant. In addition, data must include a decimal latitude and
longitude.
|
ggmap |
A ggmap object obtained using ggmap::get_map() or a similar
function to use as the basemap.
|
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.
|
breaks |
Most commonly, the number of break points for wind speed in
windRose. For windRose and the ws.int default of 2 m/s, the default, 4,
generates the break points 2, 4, 6, 8 m/s. 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.
|
facet |
Used for splitting the input data into different panels, passed
to the type argument of openair::cutData() . facet cannot be used if
multiple pollutant columns have been provided.
|
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).
|
cols |
The colours used for plotting. See openair::openColours() for
more information.
|
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 .
|
facet.nrow |
Passed to the nrow argument of ggplot2::facet_wrap() .
|
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.
|
... |
Arguments passed on to openair::polarAnnulus
resolution Two plot resolutions can be set: “normal” and
“fine” (the default).
local.tz Should the results be calculated in local time that includes
a treatment of daylight savings time (DST)? The default is not to consider
DST issues, provided the data were imported without a DST offset. 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 into the atmosphere typically 1 hour earlier during the summertime
i.e. when DST applies. When plotting diurnal profiles, this has the effect
of “smearing-out” the concentrations. Sometimes, a useful approach
is to express time as local time. This correction tends to produce
better-defined diurnal profiles of concentration (or other variables) and
allows a better comparison to be made with emissions/activity data. If set
to FALSE then GMT is used. Examples of usage include local.tz
= "Europe/London" , local.tz = "America/New_York" . See
cutData and import for more details.
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", "site") will
produce a 2x2 plot split by season and site. The use of two types is mostly
meant for situations where there are several sites. Note, when two types
are provided the first forms the columns and the second the rows.
Also note that for the polarAnnulus function some type/period
combinations are forbidden or make little sense. For example, type =
"season" and period = "trend" (which would result in a plot with
too many gaps in it for sensible smoothing), or type = "weekday" and
period = "weekday" .
statistic The statistic that should be applied to each wind
speed/direction bin. Can be “mean” (default), “median”,
“max” (maximum), “frequency”. “stdev” (standard
deviation), “weighted.mean” or “cpf” (Conditional Probability
Function). 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.
percentile If statistic = "percentile" or statistic =
"cpf" 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.
width The width of the annulus; can be “normal” (the default),
“thin” or “fat”.
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.
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.
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 end of year.
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 The smoothing value supplied to gam for the temporal and wind
direction components, respectively. In some cases e.g. a trend plot with
less than 1-year of data the smoothing with the default values may become
too noisy and affected more by outliers. Choosing a lower value of k
(say 10) may help produce a better plot.
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.
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.
key.footer see key.footer .
key.position Location where the scale key is to plotted. Allowed
arguments currently include "top" , "right" , "bottom"
and "left" .
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.
|
Value
a ggplot2
plot with a ggmap
basemap
Further customisation using ggplot2
As the outputs of the static directional analysis functions are ggplot2
figures, further customisation is possible using functions such as
ggplot2::theme()
, ggplot2::guides()
and ggplot2::labs()
.
If multiple pollutants are specified, subscripting (e.g., the "x" in "NOx")
is achieved using the ggtext package. Therefore if you
choose to override the plot theme, it is recommended to use
[ggplot2::theme()]
and [ggtext::element_markdown()]
to define the
strip.text
parameter.
When arguments like limits
, percentile
or breaks
are defined, a
legend is automatically added to the figure. Legends can be removed using
ggplot2::theme(legend.position = "none")
, or further customised using
ggplot2::guides()
and either color = ggplot2::guide_colourbar()
for
continuous legends or fill = ggplot2::guide_legend()
for discrete
legends.
See Also
the original openair::windRose()
windroseMap()
for the interactive leaflet
equivalent of
windroseMapStatic()
Other static directional analysis maps:
annulusMapStatic()
,
diffMapStatic()
,
freqMapStatic()
,
percentileMapStatic()
,
polarMapStatic()
,
pollroseMapStatic()
[Package
openairmaps version 0.8.1
Index]