ggwindrose: ggplot wrapper to create a windrose (polar wind-bin frequency...

In Ostluft/rOstluft.plot: Ploting air quality data

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

Description

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)
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")
  )

Ostluft/rOstluft.plot documentation built on Jan. 26, 2025, 1:05 a.m.