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)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.


# ... 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)
#> ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors.
ggradar(data, wd, NOx, bg = bg, color = "blue", fill = "blue", alpha = 0.5) +
  theme(panel.grid.major = ggplot2::element_line(linetype = 1, color = "white"))