summary_wind: Summarise z values over binned wind data.

In Ostluft/rOstluft.plot: Ploting air quality data

Summarise z values over binned wind data.

Description

Input data should be original unbinned data. 1-dimensional binning and calculating summary statistics over wind direction and/or wind velocity bins, respectively. NA values in z and in ws, wd (after cutting) will be silently removed before applying functions

Usage

summary_wind(
  data,
  ws,
  wd,
  z,
  groupings = grp(),
  fun = "mean",
  fun.args = list(),
  nmin = 3,
  wd_cutfun = cut_wd.fun(binwidth = 45),
  ws_cutfun = cut_ws.fun(binwidth = 1)
)

Arguments

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

Value

a tibble with summarised data

Computed variables

The names of the columns stay the same as in data (for the arguments "ws", "wd" and "z").

• binned column ws if is.null(ws) == FALSE

• binned column wd

• factor column stat containing the name of the summarize function as level

• column z with the result of the summarize function

• n count of occurrences for the corresponding bin

• freq frequency of the corresponding bin

Examples

library(ggplot2)
fn <- rOstluft.data::f("Zch_Stampfenbachstrasse_2010-2014.csv")
data <- rOstluft::read_airmo_csv(fn)
data <- rOstluft::rolf_to_openair(data)
data <- dplyr::mutate(data, year = lubridate::year(date))

summary_wind(data, "ws", "wd", "NO2")

# multiple stats: Pass function, by name, reference, as function or one sided formula
q95 <- function(x) stats::quantile(x, probs = 0.95)

funs <- list(
  "mean",
  "median",
  "q95_1" = q95,
  "q95_2" = function(x) stats::quantile(x, probs = 0.95),
  "q95_3" = ~ stats::quantile(., probs = 0.95)
)

res <- summary_wind(data, "ws", "wd", "NO2", fun = funs)
res

# and q95, q95_1, q95_2, q95_3 alle have the same value
tidyr::spread(res, "stat", "NO2") %>%
  dplyr::select(q95_1, q95_2, q95_3)

# is for some reason fun.args used with multiple functions, use ... to catch
# superfluous arguments:
funs <- list(
  "q95" = function(x, ...) stats::quantile(x, probs = 0.95),
  "mean"
)
summary_wind(data, "ws", "wd", "NO2", fun = funs, fun.args = list(na.rm = TRUE))


# more wd classes, less ws classes and squish ws
summary_wind(data, "ws", "wd", "NO",
             wd_cutfun = cut_wd.fun(binwidth = 22.5),
             ws_cutfun = cut_ws.fun(binwidth = 2, ws_max = 6))

# no ws class
summary_wind(data, NULL, "wd", "NO")

# additional grouping with strings, symbols or named expressions
summary_wind(data, ws, wd, NO2, group = grp("site", year, wday = lubridate::wday(date)))

# how often comes which concentration from one direction
summary_wind(data, NULL, wd, NO2,
             group = grp(NO2_class = ggplot2::cut_number(NO2, 5)))

# the same but we use ws as pollutant
summary_wind(data, NO2, wd, NO2, ws_cutfun = cut_number.fun(5))

# some plots using the summarized data

# a radar plot
funs <- list(
  "mean",
  "median",
  "q95" = ~ stats::quantile(., probs = 0.95)
)

data_summarized <- summary_wind(data, ws, wd, NOx, fun = funs,
  ws_cutfun = cut_number.fun(1)
)

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, 0, 0)) +
  facet_wrap(vars(stat))


# a wind rose
data_summarized <- summary_wind(data, ws, wd, ws,
  ws_cutfun = cut_ws.fun(ws_max = 4, reverse = TRUE)
)

ggplot(data_summarized, aes(x = wd, y = freq, fill = ws)) +
  geom_bar(stat = "identity") +
  coord_polar2(start = - 22.5 / 180 * pi ) +
  scale_y_continuous(
    limits = c(0, NA),
    expand = c(0,0, 0, 0),
    labels = scales::percent
  ) +
  scale_fill_viridis_d()


# a pollution rose, use the pollutant as ws
data_summarized <- summary_wind(data, NOx, wd, NOx,
  ws_cutfun = cut_number.fun(5)
)

# we can plot with the group as fill
ggplot(data_summarized, aes(x = wd, y = freq, fill = forcats::fct_rev(NOx))) +
  geom_bar(stat = "identity") +
  coord_polar2(start = - 22.5 / 180 * pi ) +
  scale_y_continuous(limits = c(0, NA), expand = c(0,0, 0, 0)) +
  scale_fill_viridis_d(direction = -1, name = "NOx")

# or the mean of the group as fill
ggplot(data_summarized, aes(x = wd, y = freq, fill = NOx.stat)) +
  geom_bar(stat = "identity") +
  coord_polar2(start = - 22.5 / 180 * pi ) +
  scale_y_continuous(limits = c(0, NA), expand = c(0,0, 0, 0)) +
  scale_fill_viridis_c(name = "NOx")

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