geom_hollowpolygon

Description

new geom to manage hollows in contour plot

Usage

GeomHollowPolygon

Format

An object of class GeomHollowPolygon (inherits from Geom, ggproto, gg) of length 5.

References

See http://rpubs.com/kohske/352 See http://docs.ggplot2.org/dev/vignettes/extending-ggplot2.html

Contour plot of pollutant concentration

Description

contourPlot plots a contour map of pollutants. This function has been deprecated since version 2.0.0 and will be removed very soon. Use contourPlot2.

Usage

contourPlot(
  data,
  domain = NULL,
  background = NULL,
  underlayer = NULL,
  overlayer = NULL,
  legend = NULL,
  levels = NULL,
  size = 0,
  cover = TRUE,
  transparency = 0.66,
  smoothness = 1,
  colors = NULL,
  bare = FALSE
)

Arguments

Details

This is a convenience function to plot contour levels of a pollutant matrix with ggplot2.

Value

A ggplot2 plot.

Examples

## Not run: 
# Load example data in long format
data(volcano)
volcano3d <- reshape2::melt(volcano)
names(volcano3d) <- c("x", "y", "z")
# Contour plot with default options
contourPlot(volcano3d)

# Import variable CONCAN from inpufile, convert km to m (k = 1000):
data <- importRaster(paste0(dir, inputfile), 
                     k = 1000, 
                     variable = "CONCAN")

# Simple contour plot
contourPlot(data)

# Specifiy (sub)domain to be plotted; background image; legend title and 
# pollutant levels.
contourPlot(data, 
            domain(500000, 510000, 6000000, 6010000, 7, 7), 
            background = "img/background.png", 
            legend = "no2 [ug/m3]", 
            levels = c(10, 20, 30, 40))

# Add underlayer (same for overlayer)
library(ggplot2)
library(maptools)
perimetro <- readShapeLines("path_to/perimetro.shp")
perimetro <- fortify(perimetro)
strada <- readShapeLines("path_to/strada.shp")
strada <- fortify(strada)
myUnderlayer <- vector(mode = "list", length = 2)
myUnderlayer[[1]] <- geom_polygon(data = perimetro, 
                                  aes(long, lat, group = group), 
                                  colour = "black", 
                                  fill = NA, 
                                  size = 0.1, 
                                  alpha = 0.5)
myUnderlayer[[2]] <- geom_path(data = strada, 
                               aes(long, lat, group = group), 
                               colour = "grey", 
                               size = 0.1, 
                               alpha = 0.5)
contourPlot(data = test, 
            background = "path_to/basemap.png", 
            underlayer = myUnderlayer)

# Change default colour palette
contourPlot(data = test, 
            colors = RColorBrewer::brewer.pal(3, name = "PiYG"))

## End(Not run)

New contour plot of pollutant concentration field

Description

contourPlot2 plots a contour map of a given quantity, such as the ground concentration of an airborne pollutant or odour, defined on a regular grid.

Usage

contourPlot2(
  data,
  x = "x",
  y = "y",
  z = "z",
  domain = NULL,
  background = NULL,
  underlayer = NULL,
  overlayer = NULL,
  legend = NULL,
  levels = NULL,
  size = 0,
  fill = TRUE,
  tile = FALSE,
  transparency = 0.75,
  colors = NULL,
  bare = FALSE
)

Arguments

Details

This is a convenience function to plot contour levels of a scalar quantity such as pollutants computed by a dispersion model, with ggplot2 version >= 3.3.0.

Data are required to be on a regular grid, typically (but not necessarily) in UTM coordinates. The input dataframe has to be in long format, i.e. one line per value to be plotted. The names of the columns corresponding to x, y and z can be specified in the input parameters.

If tile = TRUE data are shown as they are, without any graphical interpolation required for contour plots. This is helpful when you want to visualise the raw data. Since version 2.4.0, when tile = TRUE the intervals include the lowest bound and exclude the highest bound: ⁠[min, max)⁠. Note: In previous version it was the opposite.

underlayer and overlayer layers are ggplot2 objects to be shown at different levels of the vertical stack of the plot. These are useful to show topographical information related to the plot, such as sources or receptors locations.

Value

A ggplot2 object.

Examples

# Load example data in long format
data(volcano)
volcano3d <- reshape2::melt(volcano)
names(volcano3d) <- c("x", "y", "z")
# Contour plot with default options
v <- contourPlot2(volcano3d)
v

# Set levels, and properly format the legend title:
contourPlot2(volcano3d, 
             levels = c(-Inf, seq(100, 200, 20), Inf),
             legend = expression(PM[10]~"["~mu*g~m^-3~"]"))

# Sometimes, instead of a contour plot it is better to plot the original
# raster data, without any interpolation:
contourPlot2(volcano3d, 
             levels = c(-Inf, seq(100, 200, 20), Inf), 
             tile = TRUE)

# Since contourPlot2 returns a `ggplot2` object, you can add instructions as:
library(ggplot2)
v + ggtitle("Example volcano data") +
    labs(x = NULL, y = NULL)

Create base map (OBSOLETE)

Description

Create base map. This is meant to be the deepest layer of contour plot map. Axes coordinates are supposed to be in meters.

Usage

createBaseMap(
  imageFile,
  domain = c(0, 0, 1000, 1000, 5, 5),
  font_size = 10,
  font_family = "sans"
)

Arguments

Value

A ggplot2 plot.

Examples

## Not run: 
# Import image 'img'. Divide the axis with 9 ticks.
v <- createBaseMap(img, c(minx, miny, extent, extent, 9, 9), font_size=10)

## End(Not run)

Download basemap from Italian National Geoportal

Description

This function tries to download the aerial orthophoto of the requested domain from the Italian National Geoportal. The output is given in png format at the path given in the file parameter.

Usage

downloadBasemap(
  file = file,
  xSW = 410000,
  ySW = 5000500,
  xExt = 5000,
  yExt = 5000,
  crs = 32,
  width = 1024,
  height = 1024,
  units = "px",
  res = 72
)

Arguments

Value

No value is returned.

Examples

## Not run: 
# Download a basemap of a domain with SW coordinates (410000, 5000500) 
# in the UTM32 CRS and extension 5000m in both directions.

downloadBasemap(file = "./basemap.png", 
                xSW = 410000, ySW = 5000500, xExt = 5000, yExt = 5000)

# Download a basemap of a domain with SW coordinates (410000, 5000500) 
# in the UTM32 CRS and extension 5000m in both directions.
# The file has to be 2048 x 2048 pixels.

downloadBasemap(file = "./basemap.png", 
                xSW = 410000, ySW = 5000500, xExt = 5000, yExt = 5000, 
                width = 2048, height = 2048)
                
# Download a basemap of a domain with SW coordinates (410000, 5000500)
# in the UTM32 CRS and extension 5000m in both directions.
# The file has to be 10cm x 10cm with a resolution of 150 dpi.

downloadBasemap(file = "./basemap.png", 
                xSW = 410000, ySW = 5000500, xExt = 5000, yExt = 5000, 
                width = 10, height = 10, units = "cm", res = 150)

## End(Not run)

ADSO/BIN data import function

Description

Import data from ADSO/BIN binary file. It requires an active Python installation with the arinfopy library.

Usage

importADSOBIN(
  file = file.choose(),
  variable = NULL,
  slice = 1,
  deadline = 1,
  k = 1,
  kz = 1,
  dx = 0,
  dy = 0,
  destaggering = FALSE,
  raster.object = FALSE,
  verbose = FALSE
)

Arguments

Details

The importADSIOBIN() function was developed to import data from an ADSO/BIN binary file. It relies on the ‘arinfopy’ (version >= 2.2.0) python library. For more information on the library see the GitHub repository.

For more information on the active python installation, check the documentation of reticulate.

Value

In standard use, importADSOBIN() return a data frame with ⁠(X, Y, Z)⁠ columns. Column Z contains the values of the requested variable. If the raster.object option is set, it returns a RasterLayer object.

See Also

importRaster(), importSurferGrd()

Examples

## Not run: 
# Read ground level (slice = 1) value of variable M001S001.
pm10 <- importADSOBIN(file = "average_2018.bin",
                      variable = "M001S001",
                      slice = 1)

# Read deadline 12 of the second vertical level of temperature:
temperature <- importADSOBIN(file = "swift_surfpro_01-10_01_2018",
                             variable = "TEMPK",
                             slice = 2,
                             deadline = 12)

# Read varibale M001S001 at ground level, at given date and time,
# and print basic information:
nox <- importADSOBIN(file = "conc_01-10_07_2018",
                     variable = "M001S001",
                     slice = 1,
                     deadline = "2018/07/02 12:00",
                     verbose = TRUE)

## End(Not run)

Import generic raster file

Description

The function import the first layer of a generic raster file. Data are imported as an array of x, y, z columns.

Usage

importRaster(
  file = file.choose(),
  k = 1,
  kz = 1,
  dx = 0,
  dy = 0,
  destaggering = FALSE,
  variable = NULL,
  verbose = FALSE
)

Arguments

Details

Supported files include those managed by the raster package (as netcdf),

Destaggering is useful for importing data from the SPRAY model and it is not applied by default.

An optional summary output can be printed by setting the verbose parameter.

This function is based on the terra package and it can import any format managed by it.

Value

It returns a dataframe with x, y and z columns.

See Also

importADSOBIN(), importSurferGrd()

Examples

## Not run: 
# Import binary (netcdf) file and convert coordinates from km to m,
# without destaggering:
mydata <- importRaster(file = "/path_to_file/filename.nc",
                       k = 1000,
                       destaggering = FALSE)

# Import binary (netcdf) file and convert coordinates from km to m,
# with shift of 100 m in both directions:
mydata <- importRaster(file = "/path_to_file/filename.nc",
                       k = 1000,
                       dx = 100,
                       dy = 100)

## End(Not run)

Import Grid file

Description

A function to import data from Surfer text grid file.

Usage

importSurferGrd(fname, k = 1000, destaggering = FALSE)

Arguments

Details

Surfer grd file is imported and an array of x, y, z columns is returned X and y coordinates can be converted from km to m (default k=1000) and vice versa. Destaggering is applied by default.

Value

A dataset with x, y and z columns is returned.

See Also

importRaster(), importADSOBIN()

Examples

## Not run: 
# Import Surfer Grd file and convert coordinates from km to m, 
# with destaggering
mydata <- importSurferGrd("/path_to_file/filename.grd", k = 1000)

# Import Surfer Grd file and do not convert coordinates, without
#  destaggering
mydata <- importSurferGrd("path_to_file/filename.grd", k = 1,
                          destaggering = FALSE)

## End(Not run)

Plot hourly average radiation

Description

Plot a histogram with hourly average of solar radiation, together with hourly maxima for June and December.

Usage

plotAvgRad(mydata, date = "date", rad = "radg")

Arguments

Value

A ggplot2 plot.

See Also

plotStabilityClass(), plotAvgTemp()

Examples

data(stMeteo)
plotAvgRad(stMeteo, date = "date", rad = "radg")

Plot average temperature

Description

plotAvgTemp builds a bar plot of time average temperature and two line plots with maximum and minimum temperature.

Usage

plotAvgTemp(
  mydata,
  temp = "temp",
  avg.time = "1 month",
  ylabel = "Temperatura [C]",
  title = ""
)

Arguments

Value

A plot with average, min and max temperature in a given range of time.

Note

plotAvgTemp uses openair::timeAvearge to compute average.

See Also

plotStabilityClass(), plotAvgRad()

Examples

# Plot histogram with monthly averages together with maxima and minima 
# curves
data(stMeteo)
plotAvgTemp(stMeteo)
plotAvgTemp(stMeteo, temp = "temperature", 
            avg.time = "1 month", ylabel = "Temperatura [C]")

Plot stability class

Description

Plot histogram of stability class on season or hour base.

Usage

plotStabilityClass(mydata, sc = "sc", type = "season")

Arguments

Details

Numerical values of stability classes are mapped as: 1 = A, 2 = B, ..., 6 = F.

Value

A ggplot2 plot.

See Also

stabilityClass(), plotAvgRad(), plotAvgTemp()

Examples

data(stMeteo)

# Season plot of stability class pgt
plotStabilityClass(stMeteo, sc = "pgt", type = "season")

# Hourly plot of stability class pgt
plotStabilityClass(stMeteo, sc = "pgt", type = "hour")

Remove data outliers

Description

Remove data outliers based on the interquartile range.

Usage

removeOutliers(x, k = 1.5)

Arguments

Details

The interquartile range IQR is computed from input dataset as IQR = Q3 - Q1, where Q1 is 25th percentile and Q3 is the 75th percentile. Values larger than Q3 + k * IQR and smaller than Q1 - k * IQR are deemed as outliers and substituted with NA's.

The default value of k is 1.5.

Value

A numeric vector with the same length as input vector.

Examples

mydata <- c(-10 * runif(10), runif(10))
removeOutliers(mydata)

Compute rolling max

Description

The rolling maximum value along a series of data is computed.

Usage

rollingMax(mydata, length = 24)

Arguments

Details

It computes the maximum value centred along a subset of data.

Value

A numeric vector of the same length as mydata.

Examples

# Compute rolling max along 24 hours on hourly time series
data(airquality)
solar.R.24 <- rollingMax(mydata = airquality$Solar.R, length = 24)

Meteorological dataset with hourly values

Description

A dataset containing 8760 hourly values of some meteorological variables corresponding to a full solar year.

Usage

stMeteo

Format

A data frame with 8760 rows and 7 variables:

date

date time in yyyy-mm-hh HH:MM:SS

ws

wind speed in m/s

wd

wind direction in deg.

temp

air temperature in C

radg

Global solar radiation in W/m^2

tcc

Total cloud cover in integers ranging from 0 to 8

pgt

Pasquill-Gifford-Turner stability class

Source

Self derived dataset.

Stability class.

Description

Computes stability class given net radiation, total cloud cover and wind speed.

Usage

stabilityClass(rad, tcc, ws, option = "impact")

Arguments

Details

stabilityClass() computes stability class according to IAEA method based on net radiation, total cloud cover tcc and wind speed. Net radiation and wind are used by day; tcc and wind are used by night.

Three different alogorithms are implemented; see source code for details.

Value

stabilityClass returns a numeric vector with Pasquill stability classes coded as: A = 1, B = 2, ... , F = 6.

See Also

plotStabilityClass()

Examples

# Compute stability class with custom algorithm
stMeteo$cst <- stabilityClass(rad = stMeteo$rad,
                              tcc = stMeteo$tcc,
                              ws = stMeteo$ws,
                              option = "custom")
                              

Contour plot with hollow management

Description

Contour plot with hollow management

Usage

stat_hollow_contour(
  mapping = NULL,
  data = NULL,
  geom = "hollow_polygon",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Vector field plot

Description

Simple function to plot a velocities vector field.

Usage

vectorField(data, scale = 1, everyx = 1, everyy = 1, size = 0.25)

Arguments

Details

This function plots a vector field given a data.frame with coordinates (x, y) and corresponding velocity components (u, v). Vectors are coloured by magnitude (speed). The coordinates are assumed to be on a regular rectangular domain in UTM reference system.

This function is heavily inspired by snippets of code in R Graphics Cookbook by Winston Chang (https://r-graphics.org/index.html).

Value

A ggplot2 plot.

Examples

## Not run: 
metU <- importADSOBIN('/path/to/meteofile',
                      variable = 'U',
                      slice=2,
                      k = 1000,
                      verbose = TRUE)
metU <- as.data.frame(metU)
metU <- metU %>% 
        mutate(u = z, z = NULL)

metV <- importADSOBIN('/path/to/meteofile',
                      variable = 'V', 
                      slice=2, 
                      k = 1000, 
                      verbose = TRUE)
metV <- as.data.frame(metV)
metV <- metV %>%  
        mutate(v = z, z = NULL)

met <- merge(metU, metV, by = c("x", "y"))

vectorField(met, everyx = 2, everyy = 2, scalex = 10, scaley = 10) +
    coord_fixed(ratio = 1, xlim = c(0, 1000), ylim = c(0, 1000)) +
    scale_color_viridis_c()


## End(Not run)