ggplot wrapper to create a windrose (polar wind-bin frequency plot)

Usage

ggwindrose(
  data,
  ws,
  wd,
  wd_binwidth = 45,
  ws_binwidth = 1,
  ws_max = NA,
  groupings = grp(),
  nmin = 1,
  fill_scale = scale_fill_viridis_d(),
  reverse = TRUE,
  bg = NULL,
  ...
)

Arguments

data: tibble containing wind speed, wind direction and/or air pollutant concentration data
ws: symbol giving the wind velocity column name (wind velocity preferably in m/s)
wd: symbol giving the wind direction column name in degrees
wd_binwidth: numeric, binwidth for wind direction in °, wd_binwidth should fullfill: (360 / wd_binwidth) %in% c(4, 8, 16, 32)
ws_binwidth: numeric, binwidth for wind speed
ws_max: numeric, can be NA, wind speed is squished at this value
groupings: additional groupings. Use helper grp() to create. Necessary for some facets!
nmin: numeric, minimum number of values for fun, if n < nmin: NA is returned
fill_scale: ggplot2 discrete fill scale, e.g. ggplot2::scale_fill_gradientn()
reverse: TRUE/FALSE, should wind speed bin factors be sorted descending (TRUE) or ascending (FALSE). Usually for wind roses a descending order (higher wind speed on the outside) is used.
bg: raster map, e.g. ggmap object as plot background
...: Other arguments passed on to ggplot2::geom_bar(). Used to set an aesthetic to a fixed value. Defaults are color = "white", width = 1, size = 0.25

Value

ggplot2::ggplot() object

Examples

library(ggplot2)

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

data <-
  rOstluft::read_airmo_csv(fn) %>%
  rOstluft::rolf_to_openair() %>%
  openair::cutData(date, type = "daylight")

ggwindrose(data, ws, wd)


# squish ws
ggwindrose(data, ws, wd, ws_max = 5)


# change binning
ggwindrose(data, ws, wd, wd_binwidth = 22.5, ws_binwidth = 1.5, ws_max = 4.5)


# don't like bar outlines?
ggwindrose(data, "ws", "wd", color = "black", ws_max = 4)


# bigger outlines
ggwindrose(data, ws, wd, ws_max = 5, size = 1)


# a map as background
bb <- bbox_lv95(2683141, 1249040, 500)
bg <- get_stadia_map(bb)
#> ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors.
ggwindrose(data, ws, wd, ws_max = 5, alpha = 0.8, bg = bg) +
  theme(
    panel.grid.major = element_line(linetype = 2, color = "black", size = 0.5)
   )


# another fill scale
ggwindrose(data, ws, wd, ws_max = 5,
           fill_scale = scale_fill_manual(values = matlab::jet.colors(6)))


# reverse the coloring of the fill
ggwindrose(data, ws, wd, ws_max = 4,
           fill_scale = scale_fill_viridis_d(direction = -1))


# reverse the order of ws, but keep the coloring and legend order
ggwindrose(data, ws, wd, ws_max = 4, reverse = FALSE,
           fill_scale = scale_fill_viridis_d(direction = -1))


# faceting: important the faceting variable, must also be in grouping!
ggwindrose(data, ws, wd, ws_max = 5, groupings = grp(daylight)) +
  facet_wrap(vars(daylight))


# you can use groupings to directly mutate the data for faceting.
# in this example we define the groupings external for better
# readability
groupings = grp(
  season = cut_season(date, labels = c(DJF = "winter", MAM = "spring",
                      JJA = "summer", SON = "autumn")),
  year = cut_seasonyear(date, label = "year")
)

# only three years for smaller plot size and cut the last december
# theming remove the NOSW labels and reduce the y spacing between plots
data <- dplyr::filter(data, date < lubridate::ymd(20121201))
ggwindrose(data, ws, wd, ws_max = 3, groupings = groupings) +
  facet_grid(rows = vars(year), cols = vars(season)) +
  theme(
    axis.text.x = element_blank(),
    panel.spacing.y = unit(0, "pt")
  )