diffMap: Bivariate polar 'difference' plots on dynamic and static maps

In davidcarslaw/openairmaps: Create Maps of Air Pollution Data

Bivariate polar 'difference' plots on dynamic and static maps

Description

The diffMap() function creates a map using bivariate polar plots as markers. Any number of pollutants can be specified using the pollutant argument, and multiple layers of markers can be created using type. By default, these maps are dynamic and can be panned, zoomed, and otherwise interacted with. Using the static argument allows for static images to be produced instead.

Usage

diffMap(
  before,
  after,
  pollutant = NULL,
  x = "ws",
  limits = "free",
  latitude = NULL,
  longitude = NULL,
  crs = 4326,
  type = NULL,
  popup = NULL,
  label = NULL,
  provider = "OpenStreetMap",
  cols = "RdBu",
  alpha = 1,
  key = FALSE,
  legend = TRUE,
  legend.position = NULL,
  legend.title = NULL,
  legend.title.autotext = TRUE,
  control.collapsed = FALSE,
  control.position = "topright",
  control.autotext = TRUE,
  d.icon = 200,
  d.fig = 3.5,
  static = FALSE,
  static.nrow = NULL,
  ...
)

Arguments

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	Limits for the plot colour scale. default: "free" | scope: dynamic & static One of: "free" (the default) which allows all of the markers to use different colour scales. A numeric vector in the form c(lower, upper) used to define the colour scale. For example, limits = c(-10, 10) would force the plot limits to span -10 to 10. It is recommended to use a symmetrical limit scale (along with a "diverging" colour palette) for effective visualisation. Note that the "fixed" option is not supported in diffMap().
latitude, longitude	The decimal latitude(Y)/longitude(X). default: NULL | scope: dynamic & static Column names representing the decimal latitude and longitude (or other Y/X coordinate if using a different crs). If not provided, will be automatically inferred from data by looking for a column named "lat"/"latitude" or "lon"/"lng"/"long"/"longitude" (case-insensitively).
crs	The coordinate reference system (CRS). default: 4326 | scope: dynamic & static The coordinate reference system (CRS) of the data, passed to sf::st_crs(). By default this is EPSG:4326, the CRS associated with the commonly used latitude and longitude coordinates. Different coordinate systems can be specified using crs (e.g., crs = 27700 for the British National Grid). Note that non-lat/lng coordinate systems will be re-projected to EPSG:4326 for plotting on the map.
type	A method to condition the data for separate plotting. default: NULL | scope: dynamic & static Used for splitting the input data into different groups, passed to the type argument of openair::cutData(). When type is specified: Dynamic: The different data splits can be toggled between using a "layer control" menu. Static:: The data splits will each appear in a different panel. type cannot be used if multiple pollutant columns have been provided.
popup	Content for marker popups on dynamic maps. default: NULL | scope: dynamic Columns to be used as the HTML content for marker popups on dynamic maps. 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	Content for marker hover-over on dynamic maps. default: NULL | scope: dynamic 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 basemap(s) to be used. default: "OpenStreetMap" | scope: dynamic & static The base map(s) to be used beneath the polar markers. If not provided, will default to "OpenStreetMap"/"osm" for both dynamic and static maps. Dynamic: Any number of leaflet::providers. 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")) Static: One of rosm::osm.types(). There is some overlap in static and dynamic providers. For example, {ggspatial} uses "osm" to specify "OpenStreetMap". When static providers are provided to dynamic maps or vice versa, {openairmaps} will attempt to substitute the correct provider string.
cols	Colours to use for plotting. default: "RdBu" | scope: dynamic & static The colours used for plotting, passed to openair::openColours(). It is recommended to use a "diverging" colour palette (along with a symmetrical limit scale) for effective visualisation.
alpha	Transparency value for polar markers. default: 1 | scope: dynamic & static A value between 0 (fully transparent) and 1 (fully opaque).
key	Transparency value for polar markers. default: FALSE | scope: dynamic & static Draw a key for each individual marker? Potentially useful when limits = "free", but of limited use otherwise.
legend	Draw a shared legend? default: TRUE | scope: dynamic & static When all markers share the same colour scale (e.g., when limits != "free" in polarMap()), should a shared legend be created at the side of the map?
legend.position	Position of the shared legend. default: NULL | scope: dynamic & static When legend = TRUE, where should the legend be placed? Dynamic: One of "topright", "topright", "bottomleft" or "bottomright". Passed to the position argument of leaflet::addLegend(). Static:: One of "top", "right", "bottom" or "left". Passed to the legend.position argument of ggplot2::theme().
legend.title	Title of the legend. default: NULL | scope: dynamic & static By default, when legend.title = NULL, the function will attempt to provide a sensible legend title. legend.title allows users to overwrite this - for example, to include units or other contextual information. For dynamic maps, users may wish to use HTML tags to format the title.
legend.title.autotext	Automatically format the title of the legend? default: TRUE | scope: dynamic & static When legend.title.autotext = TRUE, legend.title will be first run through quickTextHTML() (dynamic) or openair::quickText() (static).
control.collapsed	Show the layer control as a collapsed? default: FALSE | scope: dynamic For dynamic maps, should the "layer control" interface be collapsed? If TRUE, users will have to hover over an icon to view the options.
control.position	Position of the layer control menu default: "topright" | scope: dynamic When type != NULL, or multiple pollutants are specified, where should the "layer control" interface be placed? One of "topleft", "topright", "bottomleft" or "bottomright". Passed to the position argument of leaflet::addLayersControl().
control.autotext	Automatically format the content of the layer control menu? default: TRUE | scope: dynamic When control.autotext = TRUE, the content of the "layer control" interface will be first run through quickTextHTML().
d.icon	The diameter of the plot on the map in pixels. default: 200 | scope: dynamic & static 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. default: 3.5 | scope: dynamic & static 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.
static	Produce a static map? default: FALSE This controls whether a dynamic or static map is produced. The former is the default and is broadly more useful, but the latter may be preferable for DOCX or PDF outputs (e.g., academic papers).
static.nrow	Number of rows in a static map. default: NULL | scope: static Controls the number of rows of panels on a static map when multiple pollutants or type are specified; passed to the nrow argument of ggplot2::facet_wrap(). The default, NULL, results in a roughly square grid of panels.
...	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.

Value

Either:

• Dynamic: A leaflet object

• Static: A ggplot2 object using ggplot2::coord_sf() coordinates with a ggspatial basemap

Customisation of static maps 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

openair::polarDiff()

Other directional analysis maps: annulusMap(), freqMap(), percentileMap(), polarMap(), pollroseMap(), windroseMap()

Examples

## Not run: 
# NB: "after" is some dummy data to demonstrate functionality
diffMap(
  before = polar_data,
  after = dplyr::mutate(polar_data, nox = jitter(nox, factor = 5)),
  pollutant = "nox"
)

## End(Not run)

davidcarslaw/openairmaps documentation built on April 28, 2024, 3 p.m.