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Summarise z values over binned wind data.

Source: R/geom_bar_wind.R, R/stat_summary_wind.R
geom_bar_wind.Rd

All calculation are done by summary_wind(), see the documentation for the details. stat_summary_wind() uses the aesthetics ws, wd and z to map the correspondent arguments for summary_wind(). The computed variables are named ws, wd and z. geom_bar_wind() is an extension to ggplot2::geom_bar() with stat_summary_wind() as default stat and correct handling of the factorized x aesthetic. To map the computed Variables use ggplot2::stat().

Usage

geom_bar_wind(
  mapping = NULL,
  data = NULL,
  stat = "summary_wind",
  position = "stack",
  ...,
  fun = "mean",
  fun.args = list(),
  nmin = 3,
  wd_cutfun = cut_wd.fun(binwidth = 45),
  ws_cutfun = cut_ws.fun(binwidth = 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_summary_wind(
  data = NULL,
  mapping = NULL,
  geom = "bar_wind",
  position = "stack",
  ...,
  groupings = grp(),
  fun = "mean",
  fun.args = list(),
  nmin = 3,
  wd_cutfun = cut_wd.fun(binwidth = 45),
  ws_cutfun = cut_ws.fun(binwidth = 1),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

a data.frame or tibble containing the data (wide format)

stat

The statistical transformation to use on the data for this layer. When using a geom_*() function to construct a layer, the stat argument can be used the override the default coupling between geoms and stats. The stat argument accepts the following:

  • A Stat ggproto subclass, for example StatCount.

  • A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".

  • For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

fun

function or list of functions for summary.

fun.args

a list of extra arguments passed on to fun.

nmin

numeric, minimum number of values for fun, if n < nmin: NA is returned

wd_cutfun

function, cut function for wind direction (to create bins). See cut_wd.fun() for options.

ws_cutfun

function, cut function for wind speed. See cut_ws.fun() for examples

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

geom

The geometric object to use to display the data for this layer. When using a stat_*() function to construct a layer, the geom argument can be used to override the default coupling between stats and geoms. The geom argument accepts the following:

  • A Geom ggproto subclass, for example GeomPoint.

  • A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

  • For more information and other ways to specify the geom, see the layer geom documentation.

groupings

additional groupings. Use helper grp() to create

Value

ggplot2::layer()

Recommendation

The facetting functions takes the inputs and evaluate them in the context of the dataset. This means it is impossible to use a computed variable from a stat or from an asthetic. This limitation means summary_wind() isn't a good fit with ggplot as a stat.

In most cases it is simpler to summarize the data beforehand and then create a plot from the summarized data.

ggwindrose() and ggradar() can be used to create some standardized plots.

Examples

library(ggplot2)

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

# a simple wind rose
ggplot(data, aes(x = after_stat(wd), y = after_stat(freq), fill = after_stat(stat))) +
  geom_bar_wind(
    mapping = aes(wd = wd, ws = ws, z = ws),
    ws_cutfun = cut_ws.fun(ws_max = 4),
    width = 1,
    color = "white"
  ) +
  coord_polar2(start = - 22.5 / 180 * pi ) +
  scale_fill_viridis_d(direction = -1)
#> Warning: Removed 27 rows containing non-finite outside the scale range
#> (`stat_summary_wind()`).


# use stat_summary_wind to generate a radar plot using
# the polygon geom, position "identity" and coord_radar()
f <- list(
  "median",
  "mean",
  "perc95" = ~ stats::quantile(., probs = 0.95)
)

ggplot(data, aes(x = after_stat(wd), y = after_stat(z), color = after_stat(stat), group = after_stat(stat))) +
  stat_summary_wind(
    mapping = aes(wd = wd, ws = ws, z = NOx),
    geom = "polygon", position = "identity",
    fun = f,
    ws_cutfun = function(x) factor("ws"),
    fill = NA,
    size = 1
  ) +
  coord_radar(start = - 22.5 / 180 * pi) +
  scale_y_continuous(limits = c(0, NA), expand = c(0,0)) +
  scale_color_viridis_d(end = 0.8)
#> Warning: Removed 767 rows containing non-finite outside the scale range
#> (`stat_summary_wind()`).


# ggplot2 doesn't support faceting over a Variable computed in a stat.
# But we can summarise the data first and then create the plot.
# Less ggplot2 magic, but more transparent for the user
data_summarized <- summary_wind(data, ws, wd, NOx, fun = f,
  ws_cutfun = function(x) factor("ws")
)

ggplot(data_summarized, aes(x = wd, y = NOx, color = stat, group = stat)) +
  geom_polygon(size = 1, fill = NA) +
  coord_radar(start = - 22.5 / 180 * pi ) +
  scale_color_viridis_d(end = 0.8) +
  scale_y_continuous(limits = c(0, NA), expand = c(0,0)) +
  facet_wrap(vars(stat))


# like faceting the mapping mechanism makes it hard to impossible
# to use the grouping argument of summary wind. Do the summarise
# external.
# For example: how often comes which concentration from a sector
data_summarized <- summary_wind(data, ws, wd, NOx,
  groupings = grp(
    fNOx = ggplot2::cut_number(NO2, 5),
    year = lubridate::year(date)
  ),
  ws_cutfun = cut_number.fun(1)
)

ggplot(data_summarized, aes(x = wd, y = freq, fill = forcats::fct_rev(fNOx))) +
  geom_bar(stat = "identity") +
  coord_polar2(start = - 22.5 / 180 * pi ) +
  scale_fill_viridis_d(direction = -1, name = "NOx")