Summarise y values over binned wind data, split into u and v components.
Source:R/summary_wind_2d.R
summary_wind_2d.RdInput data should be original unbinned data including wind direction and wind velocity; binning is done 2-dimensional over cartesian u and v wind vectors
Arguments
- data
a data.frame or tibble containing the data (wide format). requires input data including at least three columns carrying information regarding:
wind direction (in °)
wind velocity
z-values (e.g. air pollutant concentration)
- ws
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 to pass to fun.
- nmin
numeric, minimum number of values for fun, if n < nmin: NA is returned
- ws_max
numeric or Inf, maximum wind velocity for binning: above ws_max, z is set NA
- bins
numeric, number of bins over the range of values if
!groups %in% c("u", "v")- smooth
TRUE/FALSE, applies if groups = c("u", "v"); should smoothing of summary results should be performed using
fit_gam_surface()?- k
numeric, applies if smooth = TRUE; degree of smoothing in smooth term in
fit_gam_surface()- extrapolate
TRUE/FALSE, applies if smooth = TRUE;
fit_gam_surface()returns extrapolated (predicted) values for u, v coordinates that otherwise would have have NA for summarised z if extrapolate = TRUE, those values are returned (to a certain degree depending on the value of dist)- dist
numeric, fraction of 1, applies if smooth = TRUE and extrapolate = TRUE; maximum distance to next coordinate-pair at which the result of fit_gam_surface(z) should be returned
Computed variables
a tibble is returned, binned over u and v, with variables:
wd: wind direction corresponding to midpoint value of u and v
ws: wind velocity corresponding to midpoint value of u and v
u: midpoints of bins over u (from input wd and ws)
v: midpoints of bins over v (from input wd and ws)
z: result from fun(z, ...)
Examples
library(ggplot2)
fn <- rOstluft.data::f("Zch_Stampfenbachstrasse_2010-2014.csv")
data <- rOstluft::read_airmo_csv(fn)
data <- rOstluft::rolf_to_openair(data)
# summary NO2
summary_wind_2d(data, ws, wd, NOx, smooth = FALSE)
#> # A tibble: 100 × 8
#> u v stat n freq NOx wd ws
#> <dbl> <dbl> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 -8.12 -8.12 mean 0 0 NA 45 11.5
#> 2 -8.12 -6.32 mean 0 0 NA 52.1 10.3
#> 3 -8.12 -4.51 mean 12 0.000138 6.08 60.9 9.29
#> 4 -8.12 -2.71 mean 18 0.000207 6.27 71.6 8.56
#> 5 -8.12 -0.903 mean 3 0.0000345 6.82 83.7 8.17
#> 6 -8.12 0.903 mean 0 0 NA 96.3 8.17
#> 7 -8.12 2.71 mean 0 0 NA 108. 8.56
#> 8 -8.12 4.51 mean 0 0 NA 119. 9.29
#> 9 -8.12 6.32 mean 0 0 NA 128. 10.3
#> 10 -8.12 8.12 mean 0 0 NA 135 11.5
#> # ℹ 90 more rows
# multiple stats: Pass function, by name, reference, as function or one sided formula
funs <- list(
"mean",
"median" = function(x) median(x, na.rm = TRUE),
"q95" = ~ stats::quantile(., probs = 0.95)
)
summary_wind_2d(data, ws, wd, NOx, fun = funs, smooth = FALSE)
#> Warning: attributes are not identical across measure variables; they will be dropped
#> # A tibble: 300 × 8
#> u v stat n freq NOx wd ws
#> <dbl> <dbl> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 -8.12 -8.12 mean 0 0 NA 45 11.5
#> 2 -8.12 -8.12 median 0 0 NA 45 11.5
#> 3 -8.12 -8.12 q95 0 0 NA 45 11.5
#> 4 -8.12 -6.32 mean 0 0 NA 52.1 10.3
#> 5 -8.12 -6.32 median 0 0 NA 52.1 10.3
#> 6 -8.12 -6.32 q95 0 0 NA 52.1 10.3
#> 7 -8.12 -4.51 mean 12 0.000138 6.08 60.9 9.29
#> 8 -8.12 -4.51 median 12 0.000138 5.63 60.9 9.29
#> 9 -8.12 -4.51 q95 12 0.000138 8.23 60.9 9.29
#> 10 -8.12 -2.71 mean 18 0.000207 6.27 71.6 8.56
#> # ℹ 290 more rows
# 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_2d(data, ws, wd, NOx, fun = funs, fun.args = list(na.rm = TRUE),
smooth = FALSE)
#> Warning: attributes are not identical across measure variables; they will be dropped
#> # A tibble: 200 × 8
#> u v stat n freq NOx wd ws
#> <dbl> <dbl> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 -8.12 -8.12 mean 0 0 NA 45 11.5
#> 2 -8.12 -8.12 q95 0 0 NA 45 11.5
#> 3 -8.12 -6.32 mean 0 0 NA 52.1 10.3
#> 4 -8.12 -6.32 q95 0 0 NA 52.1 10.3
#> 5 -8.12 -4.51 mean 12 0.000138 6.08 60.9 9.29
#> 6 -8.12 -4.51 q95 12 0.000138 8.23 60.9 9.29
#> 7 -8.12 -2.71 mean 18 0.000207 6.27 71.6 8.56
#> 8 -8.12 -2.71 q95 18 0.000207 10.4 71.6 8.56
#> 9 -8.12 -0.903 mean 3 0.0000345 6.82 83.7 8.17
#> 10 -8.12 -0.903 q95 3 0.0000345 8.83 83.7 8.17
#> # ℹ 190 more rows
# additional groupings
summary_wind_2d(data, ws, wd, NOx, groupings = grp(site), smooth = FALSE)
#> # A tibble: 100 × 9
#> u v stat site n freq NOx wd ws
#> <dbl> <dbl> <chr> <fct> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 -8.12 -8.12 mean Zch_Stampfenbachstrasse 0 0 NA 45 11.5
#> 2 -8.12 -6.32 mean Zch_Stampfenbachstrasse 0 0 NA 52.1 10.3
#> 3 -8.12 -4.51 mean Zch_Stampfenbachstrasse 12 0.000138 6.08 60.9 9.29
#> 4 -8.12 -2.71 mean Zch_Stampfenbachstrasse 18 0.000207 6.27 71.6 8.56
#> 5 -8.12 -0.903 mean Zch_Stampfenbachstrasse 3 0.0000345 6.82 83.7 8.17
#> 6 -8.12 0.903 mean Zch_Stampfenbachstrasse 0 0 NA 96.3 8.17
#> 7 -8.12 2.71 mean Zch_Stampfenbachstrasse 0 0 NA 108. 8.56
#> 8 -8.12 4.51 mean Zch_Stampfenbachstrasse 0 0 NA 119. 9.29
#> 9 -8.12 6.32 mean Zch_Stampfenbachstrasse 0 0 NA 128. 10.3
#> 10 -8.12 8.12 mean Zch_Stampfenbachstrasse 0 0 NA 135 11.5
#> # ℹ 90 more rows
# we can use expressions in grp For better readability groupings is
# defined outside of the function call
groupings = grp("site", year = lubridate::year(date))
summary_wind_2d(data, ws, wd, NOx, groupings = groupings, smooth = FALSE)
#> # A tibble: 500 × 10
#> u v stat site year n freq NOx wd ws
#> <dbl> <dbl> <chr> <fct> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 -8.12 -8.12 mean Zch_Stampfenbachstrasse 2010 0 0 NA 45 11.5
#> 2 -8.12 -8.12 mean Zch_Stampfenbachstrasse 2011 0 0 NA 45 11.5
#> 3 -8.12 -8.12 mean Zch_Stampfenbachstrasse 2012 0 0 NA 45 11.5
#> 4 -8.12 -8.12 mean Zch_Stampfenbachstrasse 2013 0 0 NA 45 11.5
#> 5 -8.12 -8.12 mean Zch_Stampfenbachstrasse 2014 0 0 NA 45 11.5
#> 6 -8.12 -6.32 mean Zch_Stampfenbachstrasse 2010 0 0 NA 52.1 10.3
#> 7 -8.12 -6.32 mean Zch_Stampfenbachstrasse 2011 0 0 NA 52.1 10.3
#> 8 -8.12 -6.32 mean Zch_Stampfenbachstrasse 2012 0 0 NA 52.1 10.3
#> 9 -8.12 -6.32 mean Zch_Stampfenbachstrasse 2013 0 0 NA 52.1 10.3
#> 10 -8.12 -6.32 mean Zch_Stampfenbachstrasse 2014 0 0 NA 52.1 10.3
#> # ℹ 490 more rows
# smoothing
df1 <- summary_wind_2d(data, ws, wd, NOx, bins = 100^2, smooth = FALSE)
df2 <- summary_wind_2d(data, ws, wd, NOx, bins = 100^2, extrapolate = FALSE)
df3 <- summary_wind_2d(data, ws, wd, NOx, bins = 100^2, smooth = TRUE)
df <- dplyr::bind_rows(
"smooth = F" = df1,
"smooth = T, extrapolate = F" = df2,
"smooth = T, extrapolate = T" = df3,
.id = "smoothing"
)
ggplot(df, aes(x = u, y = v, fill = NOx)) +
coord_fixed(expand = FALSE) +
lims(x = c(-7.5, 7.5), y = c(-7.5, 7.5)) +
geom_raster() +
scale_fill_viridis_c(na.value = NA) +
facet_wrap(vars(smoothing), ncol = 2)
#> Warning: Removed 23382 rows containing missing values or values outside the scale range
#> (`geom_raster()`).
# for a small number of bins reduce k
summary_wind_2d(data, ws, wd, NO2, bins = 5^2, smooth = TRUE, k = 5)
#> # A tibble: 25 × 8
#> stat u v n freq NO2 wd ws
#> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 mean -7.22 -7.22 0 0 NA 45 10.2
#> 2 mean -7.22 -3.61 211 0.00243 8.90 63.4 8.07
#> 3 mean -7.22 0 26 0.000299 10.0 90 7.22
#> 4 mean -7.22 3.61 4 0.0000461 9.19 117. 8.07
#> 5 mean -7.22 7.22 0 0 NA 135 10.2
#> 6 mean -3.61 -7.22 7 0.0000806 10.8 26.6 8.07
#> 7 mean -3.61 -3.61 1858 0.0214 17.2 45 5.11
#> 8 mean -3.61 0 8147 0.0938 20.0 90 3.61
#> 9 mean -3.61 3.61 186 0.00214 18.5 135 5.11
#> 10 mean -3.61 7.22 0 0 NA 153. 8.07
#> # ℹ 15 more rows