Default Wind Rose Plot — windrose.default • foehnix

Windrose plot, can handle two type of plots (see type) using the same information.

# S3 method for default
windrose(
  x,
  ff,
  interval = 10,
  type = "density",
  windsector = NULL,
  windsector.col = "gray90",
  main = NULL,
  hue = c(10, 100),
  power = 0.5,
  ...,
  dd = NULL,
  filter = NULL,
  ffvar = "ff",
  ddvar = "dd",
  breaks = NULL,
  legend.pos = c("right", "left"),
  legend.title = NULL,
  labels.angle = 225
)

Arguments

x: either an univariate object (numeric vector or zoo) containing meteorological wind directions or a multivariate zoo or data.frame object containing both, wind speed and wind meteorological wind direction. More details provided in the 'Details' section.
ff: zoo object or numeric vector with sind speed data (>= 0).
interval: numeric, a fraction of 360. If 360 cannot be devided by interval without a rest the script will stop. Interval for the segments along the wind direction dd. Default 10.
type: NULL or character, one of density or histogram. If NULL both types will be plotted.
windsector: list, matrix, or data frame for highlighting one or multiple wind sectors. See 'Examples' ('Highlighting wind sectors').
windsector.col: color of the wind sector (if provided).
main: character or expression, figure title. If not specified, a default one will be used.
hue: numeric vector of length 1 or two (for type = "density" two are used, for type = "histogram" only the first one will be used at the moment). Hue(s) for colorspace::heat_hcl(...).
power: numeric >0, power parameter for the alpha channel if type = "histogram". If 1 the alpha channel is linear for the whole range of ff, values != 1 change the alpha behavior.
...: additional optional arguments forwarded, see 'Details'.
dd: can be used if univariate objects or vectors are provided for wind speed and meteorological wind direction (see 'Details' section).
filter: a custom set of filter rules (see foehnix_filter).
ffvar: string, custom name of the wind speed variable in x if x is a multivariate object (see 'Details' section).
ddvar: string, custom name of the wind direction variable in x if x is a multivariate object (see 'Details' section).
breaks: NULL or a sequence of numeric values to set the breaks along ff.
legend.pos: character, either right (default) or left. Where to put the legend.
legend.title: character or expression to set legend title.
labels.angle: numeric, default is 225. Where to draw the labels in the density plot. labels.angle is in meteorological degrees.

Details

The windrose function can be used in different ways. The main purpose is to plot (conditional) wind roses for foehnix objects (uses windrose.foehnix), but there is this generic windrose.default method which can be used to create wind roses on non-foehnix objects.

windrose.default can either with called with two univariate inputs for wind direction and wind speed, or with a multivariate zoo object or data.frame which provides both, wind speed and wind direction. Moreover, additional variables which can be used in combination with the filter option. Examples are provided in the example section.

Univariate inputs/vectors: the windrose.default function requires both, wind direction and wind speed. If univariate objects/vectors are used at least the two inputs dd (identical to x) and ff have to be specified. dd (x) is the meteorological wind direction in degrees (]0, 360[, 0/360 is 'wind from north', 90 'wind from east', 180 'wind from south', and 270 'wind from west'). ff has to be of the same length as dd containing the corresponding wind speed.

Multivariate input: rather than providing dd (x) and ff a multivariate zoo object or data.frame can be provided when calling the function containing (at least) wind direction and wind speed. By default, the method expects the wind direction variable to be named "dd", the wind speed named "ff". Custom names can be specified using the two input arguments ffvar and ddvar.

Custom filter: the optional filter input can be used if input x is a multivariate object and provides a convenient way to subset/filter the data. A detailed description how to define the filter rules can be found on the documentation page of the foehnix_filter method.

Additional optional arguments: The function allows to provide additional arguments for better customization. These arguments are forwarded to selected calls. Currently implemented:

For type = "density": border, lty, and lty forwarded to polygon(...).

Examples

# Loading observation data for station Ellboegen.
# The object returned is a zoo time series object
# containing (see ?ellboegen).
data <- demodata("ellboegen")
class(data)
#> [1] "zoo"
head(data)
#>                      dd  ff  rh    t
#> 2006-01-01 01:00:00 171 0.6  90 -0.4
#> 2006-01-01 02:00:00 268 0.3 100 -1.8
#> 2006-01-01 03:00:00 115 5.2  79  0.9
#> 2006-01-01 04:00:00 152 2.1  88 -0.6
#> 2006-01-01 05:00:00 319 0.7 100 -2.6
#> 2006-01-01 06:00:00  36 0.1  99 -1.7

# Extract dd/ff and create a windrose,
# using two vectors as input:
dd <- as.vector(data$dd)
ff <- as.vector(data$ff)
windrose(dd, ff,
         main = "Demo Windrose\nUse vector inputs")
windrose(dd = dd, ff = ff,
         main = "Demo Windrose\nUse vector inputs")


# Using univariate zoo objects as input:
windrose(dd = data$dd, ff = data$ff,
         main = "Demo Windrose\nUnivariate zoo objects as inputs")


# Or specify a multivariate zoo object or data.frame
# object on input x:
windrose(data,
         main = "Demo Windrose\nUse multivariate zoo object")

windrose(as.data.frame(data),
         main = "Demo Windrose\nUse multivariate data.frame object")


# Custom names for ff/dd
copy <- data
names(copy)[1:2] <- c("wind_dir", "wind_spd")
windrose(copy, main = "Demo Windrose\nMultivariate zoo, custom names",
         ddvar = "wind_dir", ffvar = "wind_spd")


# Highlighting wind sectors
windrose(data, windsector = list(c(43, 223)),
         main = "Windrose\nUnnamed Wind Sector")

windrose(data, windsector = matrix(c(43, 223), nrow = 1),
         main = "Windrose\nUnnamed Wind Sector")
windrose(data, windsector = data.frame(from = 43, to = 223),
         main = "Windrose\nUnnamed Wind Sector")
windrose(data, windsector = list(A = c(100, 160), B = c(310, 10)),
         main = "Windrose\nNamed Wind Sector")

sectors <- matrix(c(100, 160, 310, 10), nrow = 2, byrow = TRUE,
                  dimnames = list(c("Down", "Up"), NULL))
windrose(data, windsector = sectors, 
         main = "Windrose\nUnnamed Wind Sector")

sectors <- matrix(seq(0, 350, by = 10), ncol = 2, byrow = TRUE)
windrose(data, windsector = sectors, main = "Yey")


# Custom filter: for details, see ?foehnix_filter
# Example 1:
# - Plot windrose for all observations where the wind
#   direction was within 43-233 degrees (south southeast)
filter1 <- list(dd = c(43, 223))
windrose(data, main = "Custom filter on wind direction,\n43 - 223 degrees",
         type = "hist", filter = filter1)


# Example 2:
# - Plot windrose for all observations where the wind
#   speed was > 5 meters per second.
filter2 <- list(ff = function(x) x > 10)
windrose(data, main = "Custom filter on wind speed\nonly observations where ff > 5",
         type = "hist", filter = filter2)


# Example 3:
# - Plot windrose for specific months (create new variable
#   'month' in advance):
data$month <- as.POSIXlt(index(data))$mon + 1
par(mfrow = c(1,2))
windrose(data, main = "Wind rose for January",
         filter = list(month = function(x) x == 1))
windrose(data, main = "Wind rose for July",
         filter = list(month = function(x) x == 7))


# Example 4:
# Similar to example 3, but for 
data$hour <- as.POSIXlt(index(data))$hour
par(mfrow = c(1,2))
windrose(data, main = "Wind rose for midnight (00 UTC)",
         filter = list(hour = function(x) x == 0))
windrose(data, main = "Wind rose for noon (12 UTC)",
         filter = list(hour = function(x) x == 12))


# Example 5:
# A more complex filter: midnight, winter (Dez/Jan/Feb)
# for wind speeds exceeding 5 meters per second.
filter5 <- function(x) x$month %in% c(12, 1, 2) & x$hour == 0 & x$ff > 5
par(mfrow = c(1,2))
windrose(data, main = "DJF 12 UTC (ff > 5)\nComplex Filter", filter = filter5)
windrose(data, main = "DJF 12 UTC (ff > 5)\nComplex Filter", filter = filter5, type = "hist")