ggradar: ggplot2-wrapper to summarise and plot data by wind direction...

In Ostluft/rOstluft.plot: Ploting air quality data

ggplot2-wrapper to summarise and plot data by wind direction bins as radar plot

Description

ggplot2-wrapper to summarise and plot data by wind direction bins as radar plot

Usage

ggradar(
  data,
  wd,
  y,
  group = stat,
  mapping = NULL,
  facet_groups = grp(),
  wd_binwidth = 45,
  fun = "mean",
  fun.args = list(),
  nmin = 3,
  fun_reorder = identity,
  bg = NULL,
  ...
)

Arguments

data	tibble containing wind speed, wind direction and air pollutant concentration data
wd	symbolic giving the wind direction column name
y	symbolic giving the wind direction column name
group	symbolic or expression defining the grouping for the ggplot2::geom_polygon(). Defaulting to stat column returned from summary_wind()
mapping	add or overwrite mappings. default is aes(x = lon, y = lat, group = date, color = height) (x has to be wind direction and y the parameter of interest); for more details, check out the examples
facet_groups	symbolic or expression specifying the variable(s) for facetting; passed to summary_wind() by using the grp() function (facet_groups and groups are passed to summary_wind() groupings argument); default = groups()
wd_binwidth	numeric, binwidth for wind direction in °, wd_binwidth should fullfill: (360 / wd_binwidth) %in% c(4, 8, 16, 32)
fun	character string or vector of character strings, stat function(s) to be applied at wind direction bins
fun.args	list, arguments to fun
nmin	numeric, minimum number of data points to be averaged in one wind direction bin
fun_reorder	a function (default is identity()) used to reorder the factor levels of the mapping's group variable (can be useful to change the fill order, see examples)
bg	raster map, e.g. ggmap object as plot background
...	other parameters passed on to ggplot2::geom_polygon()

Value

ggplot2::ggplot() object

Examples

library(ggplot2)
library(dplyr)
library(tidyr)

fn <- rOstluft.data::f("Zch_Stampfenbachstrasse_2010-2014.csv")
data <-
  rOstluft::read_airmo_csv(fn) %>%
  rOstluft::rolf_to_openair() %>%
  dplyr::mutate(wday = lubridate::wday(date, label = TRUE, week_start = 1))

# wind direction radar chart with mean values of y as summary statistics
ggradar(data, wd, NOx)

# don't like the legend?
ggradar(data, wd, NOx, show.legend = FALSE)

# wind direction radar chart with pre-calculated summary stats
# (same as above)
df <- data %>%
   dplyr::filter(!is.na(.data$wd)) %>%
   dplyr::mutate(wd = cut_wd(.data$wd, binwidth = 45)) %>%
   dplyr::group_by(.data$wd) %>%
   dplyr::summarise(NOx = mean(.data$NOx, na.rm = TRUE)) %>%
   dplyr::ungroup()

# we can provide a string as group, in the case no variable
# for group exists in the data
ggradar(df, wd, NOx, "mean", fun = "identity", show.legend = FALSE)

# same as above but with defined fill and alpha, no color
ggradar(data, wd, NOx, fill = "gray30", alpha = 0.5, color = NA)

# same as above but with no fill, defined color etc
ggradar(data, wd, NOx, fill = NA, color = "steelblue", lwd = 1)

# higher wind direction resolution (actually: highest with predefined labels)
ggradar(data, wd, NOx, wd_binwidth = 11.25,
        fill = "gray30", alpha = 0.5)

# apply different statistic function
q95 <- function(x, ...) quantile(x, 0.95, ...)
ggradar(data, wd, NOx, fun = list(q95 = q95), alpha = 0.5)

# group by multiple statistic functions and omit polygon filling
ggradar(data, wd, NOx,
        fun = list("mean", "median", "perc95" = q95), fill = NA)

# ... adjust x and color and fill scales and reorder stat levels for appropriate fill order
q05 <- function(x, ...) quantile(x, 0.05, ...)
q95 <- function(x, ...) quantile(x, 0.95, ...)
stat_reorder <- function(stat) {
  factor(stat, levels = rev(c("perc05", "median", "mean", "perc95")))
}
ggradar(data, wd, NOx,
     fun = list("perc05" = q05, "median", "mean", "perc95" = q95),
     fun_reorder = stat_reorder, color = NA, alpha = 0.9) +
   scale_y_continuous(limits = c(0,120)) +
   scale_fill_viridis_d(begin = 0.2)

# ... same as above but with one-colored fill and stats as facets
ggradar(data, wd, NOx,
        fun = list("mean", "median", "perc95" = q95),
        fill = "steelblue",
        show.legend = FALSE) +
  facet_wrap(vars(stat), ncol = 2)

# multiple y-parameters and facetting (facetting variable has to be separately
# specified in facet_groups!)
df2 <- dplyr::select(data, wd, NO, NOx, wday, date) %>%
  tidyr::gather(par, val, -wd, -wday, -date)

ggradar(df2, wd, val, wday,
        facet_groups = grp(par), fill = NA) +
  facet_wrap(vars(par))

# same as above, but calculate grouping on the fly with an expression
ggradar(df2, wd, val, group = lubridate::wday(date, label = TRUE, week_start = 1),
        facet_groups = grp(par), fill = NA) +
  labs(color = "Week day") +
  facet_wrap(vars(par))

# with background map
bb <- bbox_lv95(2683141, 1249040, 500)
bg <- get_stadia_map(bb)
ggradar(data, wd, NOx, bg = bg, color = "blue", fill = "blue", alpha = 0.5) +
  theme(panel.grid.major = ggplot2::element_line(linetype = 1, color = "white"))

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