ggplot2 wrapper to create a wind-based polar plot with cartesian coords

Inspired and derived from openair::polarPlot(), i.e. applying a stat function on data to yield a wind direction + wind velocity heatmap, (in cartesian u, v wind component space); includes options for smoothing, flexible grid setting, flexible stat functions, data extrapolation and plotting over a raster map.

Usage

ggpolarplot(
  data,
  ws,
  wd,
  z,
  groupings = grp(),
  fun = "mean",
  fun.args = list(na.rm = TRUE),
  nmin = 3,
  ws_max = NA,
  smooth = TRUE,
  k = 200,
  extrapolate = TRUE,
  dist = 0.1,
  pixels = 80^2,
  fill_scale = scale_fill_gradientn(colours = matlab::jet.colors(20), na.value = NA),
  ylabels = scales::unit_format(unit = "m/s"),
  breaks = waiver(),
  bg = NULL,
  ...
)

Arguments

data: tibble containing wind speed, wind direction and air pollutant data
ws: symbol giving the wind velocity parameter name (wind velocity preferably in m/s)
wd: symbol giving the wind direction parameter name in degrees
z: symbol giving the parameter name to be summarised
groupings: additional groupings. Use helper grp() to create
fun: function or list of functions for summary.
fun.args: a list of extra arguments to pass to fun.
nmin: numeric, minimum number of values for fun, if n < nmin: NA is returned
ws_max: numeric or Inf, maximum wind velocity for binning: above ws_max, z is set NA
smooth: TRUE/FALSE, applies if groups = c("u", "v"); should smoothing of summary results should be performed using fit_gam_surface()?
k: numeric, applies if smooth = TRUE; degree of smoothing in smooth term in fit_gam_surface()
extrapolate: TRUE/FALSE, applies if smooth = TRUE; fit_gam_surface() returns extrapolated (predicted) values for u, v coordinates that otherwise would have have NA for summarised z if extrapolate = TRUE, those values are returned (to a certain degree depending on the value of dist)
dist: numeric, fraction of 1, applies if smooth = TRUE and extrapolate = TRUE; maximum distance to next coordinate-pair at which the result of fit_gam_surface(z) should be returned
pixels: number of bins at the 2-dimensional u, v wind component coordinate system
fill_scale: ggplot2 continuous fill scale, e.g. scale_fill_gradientn(). If prefered the scale can added later to the returned ggplot object, but a message that the scale is replaced will be printed.
ylabels: function to format ylabels. Default adds unit " m/s"
breaks: waiver() or numeric vector, provides y-axis breaks
bg: raster map, e.g. ggmap object as plot background
...: Other arguments passed on to ggplot2::geom_raster(). Used to set an aesthetic to a fixed value

Value

ggplot2::ggplot() object

Examples

library(ggplot2)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

fn <- rOstluft.data::f("Zch_Stampfenbachstrasse_2010-2014.csv")

df <- rOstluft::read_airmo_csv(fn) %>%
  rOstluft::rolf_to_openair() %>%
  dplyr::mutate(wday = lubridate::wday(date, label = TRUE, week_start = 1))

# simple
ggpolarplot(df, wd = wd, ws = ws, z = NOx)
#> Warning: Removed 3772 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# change smoothing degree, add custom breaks and change grid style, different fill scale
fs <- scale_fill_viridis_c(na.value = NA)
ggpolarplot(df, wd = wd, ws = ws, z = NOx, k = 25, fill_scale = fs, breaks = seq(0,8,2))  +
  theme(panel.grid.major = element_line(linetype = 2, size = 0.25, color = "gray80"))
#> Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
#> ℹ Please use the `linewidth` argument instead.
#> Warning: Removed 3772 rows containing missing values or values outside the scale range
#> (`geom_raster()`).



# no data extrapolation
ggpolarplot(df, wd = wd, ws = ws, z = NOx, extrapolate = FALSE, breaks = seq(0,8,2))
#> Warning: Removed 5402 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# no smoothing
ggpolarplot(df, wd = wd, ws = ws, z = NOx, smooth = FALSE, breaks = seq(0,8,2))
#> Warning: Removed 5402 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# squish maximum NOx concentration and cut off wind velocity at 4 m/s
fs = scale_fill_viridis_squished(limits = c(0,50), breaks = seq(0,50,10), na.value = NA)
ggpolarplot(df, wd = wd, ws = ws, z = NOx, ws_max = 4,  fill_scale = fs,
            smooth = FALSE, breaks = c(0, 2, 4))
#> Warning: Removed 3450 rows containing missing values or values outside the scale range
#> (`geom_raster()`).



# like jet colors better, want it smoothed?
ggpolarplot(df, wd = wd, ws = ws, z = NOx, ws_max = 4, smooth = TRUE, breaks = 0:4) +
  scale_fill_gradientn_squished(limits = c(0,50), breaks = seq(0,50,10),
                                na.value = NA, colors = matlab::jet.colors(100))
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> Warning: Removed 1740 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# change binning parameters
ggpolarplot(df, wd = wd, ws = ws, z = NOx, smooth = FALSE, pixels = 50^2,
            nmin = 10, breaks = seq(0,6,2))
#> Warning: Removed 2140 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# facetting variable must be included in grp
ggpolarplot(df, wd = wd, ws = ws, z = NOx, groupings = grp(wday),
            ws_max = 4, pixels = 50^2, k = 25, breaks = c(0, 2, 4)) +
  facet_wrap(vars(wday))
#> Warning: Removed 6006 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# different stat fun
ggpolarplot(df, wd = wd, ws = ws, z = NOx, fun = "quantile",
            fun.args = list(probs = 0.95, na.rm = TRUE),
            breaks = seq(0,10,2))
#> Warning: Removed 3772 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# facetting by stat
ggpolarplot(df, wd = wd, ws = ws, z = NOx, fun = list("mean", "median"), ws_max = 4,
             pixels = 50^2, k = 25, breaks = c(0, 2, 4)) +
  facet_wrap(vars(stat))
#> Warning: Removed 1230 rows containing missing values or values outside the scale range
#> (`geom_raster()`).


# background map, just remember the location on the map has no correlation with
# the overlaying data. Only the direction.
bb <- bbox_lv95(2683141, 1249040, 500)
bg <- get_stadia_map(bb)
#> ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors.
fs <- scale_fill_gradientn_squished(
  limits = c(0,50), breaks = seq(0,50,10),
  na.value = NA, colors = matlab::jet.colors(20)
)

ggpolarplot(df, wd = wd, ws = ws, z = NOx, ws_max = 4,
            bg = bg, alpha = 0.6,
            fill_scale = fs, smooth = TRUE, breaks = c(0,2,4)) +
  theme(
    panel.grid.major = element_line(linetype = 2, color = "black", size = 0.5)
  )
#> Warning: Removed 1740 rows containing missing values or values outside the scale range
#> (`geom_raster()`).