Package 'metR'

Description

Saves keystrokes for computing anomalies.

Usage

Anomaly(x, baseline = seq_along(x), ...)

Arguments

Value

A numeric vector of the same length as x with each value's distance to the mean.

See Also

Other utilities: JumpBy(), Mag(), Percentile(), logic

Examples

# Zonal temperature anomaly
library(data.table)
temperature[, .(lon = lon, air.z = Anomaly(air)), by = .(lat, lev)]

Description

This scale allows ggplot to understand data that has been discretised with some procedure akin to cut and access the underlying continuous values. For a scale that does the opposite (take continuous data and treat them as discrete) see ggplot2::binned_scale().

Usage

as.discretised_scale(scale_function)

scale_fill_discretised(
  ...,
  low = "#132B43",
  high = "#56B1F7",
  space = "Lab",
  na.value = "grey50",
  guide = ggplot2::guide_colorsteps(even.steps = FALSE, show.limits = TRUE),
  aesthetics = "fill"
)

scale_fill_divergent_discretised(
  ...,
  low = scales::muted("blue"),
  mid = "white",
  high = scales::muted("red"),
  midpoint = 0,
  space = "Lab",
  na.value = "grey50",
  guide = ggplot2::guide_colorsteps(even.steps = FALSE, show.limits = TRUE)
)

discretised_scale(
  aesthetics,
  scale_name,
  palette,
  name = ggplot2::waiver(),
  breaks = ggplot2::waiver(),
  labels = ggplot2::waiver(),
  limits = NULL,
  trans = scales::identity_trans(),
  na.value = NA,
  drop = FALSE,
  guide = ggplot2::guide_colorsteps(even.steps = FALSE),
  position = "left",
  rescaler = scales::rescale,
  oob = scales::censor,
  super = ScaleDiscretised
)

Arguments

Details

This scale makes it very easy to synchronise the breaks of filled contours and the breaks shown no the colour guide. Bear in mind that when using geom_contour_fill(), the default fill aesthetic (level_mid) is not discretised. To use this scale with that geom, you need to set aes(fill = after_stat(level)).

Value

A function with the same arguments as scale_function that works with discretised values.

See Also

scale_fill_discretised

Examples

library(ggplot2)
scale_fill_brewer_discretised <- as.discretised_scale(scale_fill_distiller)



library(ggplot2)

# Using the `level` compute aesthetic from `geom_contour_fill()`
# (or ggplot2::geom_contour_filled()), the default scale is discrete.
# This means that you cannot map colours to the underlying numbers.
v <- ggplot(faithfuld, aes(waiting, eruptions, z = density))
v + geom_contour_fill(aes(fill = after_stat(level)))

v + geom_contour_fill(aes(fill = after_stat(level))) +
  scale_fill_discretised()

# The scale can be customised the same as any continuous colour scale
v + geom_contour_fill(aes(fill = after_stat(level))) +
  scale_fill_discretised(low = "#a62100", high = "#fff394")

# Setting limits explicitly will truncate the scale
# (if any limit is inside the range of the breaks but doesn't
# coincide with any range, it will be rounded with a warning)
v + geom_contour_fill(aes(fill = after_stat(level))) +
  scale_fill_discretised(low = "#a62100", high = "#fff394",
                         limits = c(0.01, 0.028))

# Or extend it.
v + geom_contour_fill(aes(fill = after_stat(level))) +
  scale_fill_discretised(low = "#a62100", high = "#fff394",
                         limits = c(0, 0.07))

v + geom_contour_fill(aes(fill = after_stat(level))) +
  scale_fill_divergent_discretised(midpoint = 0.02)

# Existing continous scales can be "retrofitted" by changing the `super`
# and `guide` arguments.
v + geom_contour_fill(aes(fill = after_stat(level))) +
    scale_fill_distiller(super = ScaleDiscretised)

# Unequal breaks will, by default, map to unequal spacing in the guide
v + geom_contour_fill(aes(fill = after_stat(level)), breaks = c(0, 0.005, 0.01, 0.02, 0.04)) +
  scale_fill_discretised()

# You can change that by the `even.steps` argument on ggplot2::guide_colorsteps()
v + geom_contour_fill(aes(fill = after_stat(level)), breaks = c(0, 0.005, 0.01, 0.02, 0.04)) +
  scale_fill_discretised(guide = guide_colorsteps(even.steps = TRUE, show.limits = TRUE))

Description

This is a helper function to quickly make an interpolated list of locations between a number of locations

Usage

as.path(x, y, n = 10, path = TRUE)

Arguments

Details

This function is mostly useful when combined with Interpolate

Value

A list of components x and y with the list of locations and the path arguments

See Also

Interpolate

Description

Converts longitude from [0, 360) to [-180, 180) and vice versa.

Usage

ConvertLongitude(lon, group = NULL, from = NULL)

Arguments

Value

If group is missing, a numeric vector the same length of lon. Else, a list with vectors lon and group.

Examples

library(ggplot2)
library(data.table)

data(geopotential)

ggplot(geopotential[date == date[1]], aes(lon, lat, z = gh)) +
    geom_contour(color = "black") +
    geom_contour(aes(x = ConvertLongitude(lon)))

map <- setDT(map_data("world"))
map[, c("lon", "group2") := ConvertLongitude(long, group, from = 180)]

ggplot(map, aes(lon, lat, group = group2)) +
    geom_path()

Description

Coriolis and beta parameters by latitude.

Usage

coriolis(lat)

f(lat)

coriolis.dy(lat, a = 6371000)

f.dy(lat, a = 6371000)

Arguments

Details

All functions use the correct sidereal day (24hs 56mins 4.091s) instead of the incorrect solar day (24hs) for 0.3\ pedantry.

Description

Returns an eof object with just the n principal components.

Usage

## S3 method for class 'eof'
cut(x, n, ...)

Arguments

Description

The matrices returned by EOF() are normalized. This function multiplies the left or right matrix by the diagonal matrix to return it to proper units.

Usage

denormalise(eof, which = c("left", "right"))

denormalize(eof, which = c("left", "right"))

Arguments

Description

Derivate a discrete variable using finite differences

Usage

Derivate(
  formula,
  order = 1,
  cyclical = FALSE,
  fill = FALSE,
  data = NULL,
  sphere = FALSE,
  a = 6371000,
  equispaced = TRUE
)

Laplacian(
  formula,
  cyclical = FALSE,
  fill = FALSE,
  data = NULL,
  sphere = FALSE,
  a = 6371000,
  equispaced = TRUE
)

Divergence(
  formula,
  cyclical = FALSE,
  fill = FALSE,
  data = NULL,
  sphere = FALSE,
  a = 6371000,
  equispaced = TRUE
)

Vorticity(
  formula,
  cyclical = FALSE,
  fill = FALSE,
  data = NULL,
  sphere = FALSE,
  a = 6371000,
  equispaced = TRUE
)

Arguments

Details

Each element of the return vector is an estimation of $\frac{\partial^n x}{\partial y^{n}}$ by centred finite differences.

If sphere = TRUE, then the first two independent variables are assumed to be longitude and latitude (in that order) in degrees. Then, a correction is applied to the derivative so that they are in the same units as a.

Using fill = TRUE will degrade the solution near the edges of a non-cyclical boundary. Use with caution.

Laplacian(), Divergence() and Vorticity() are convenient wrappers that call Derivate() and make the appropriate sums. For Divergence() and Vorticity(), formula must be of the form vx + vy ~ x + y (in that order).

Value

If there is one independent variable and one dependent variable, a numeric vector of the same length as the dependent variable. If there are two or more independent variables or two or more dependent variables, a list containing the directional derivatives of each dependent variables.

See Also

Other meteorology functions: EOF(), GeostrophicWind(), WaveFlux(), thermodynamics, waves

Examples

data.table::setDTthreads(2)
theta <- seq(0, 360, length.out = 20)*pi/180
theta <- theta[-1]
x <- cos(theta)
dx_analytical <- -sin(theta)
dx_finitediff <- Derivate(x ~ theta, cyclical = TRUE)[[1]]

plot(theta, dx_analytical, type = "l")
points(theta, dx_finitediff, col = "red")

# Curvature (Laplacian)
# Note the different boundary conditions for each dimension
variable <- expand.grid(lon = seq(0, 360, by = 3)[-1],
                        lat = seq(-90, 90, by = 3))
variable$z <- with(variable, cos(lat*pi/180*3) + sin(lon*pi/180*2))
variable <- cbind(
     variable,
     as.data.frame(Derivate(z ~ lon + lat, data = variable,
                          cyclical = c(TRUE, FALSE), order = 2)))
library(ggplot2)
ggplot(variable, aes(lon, lat)) +
    geom_contour(aes(z = z)) +
    geom_contour(aes(z = z.ddlon + z.ddlat), color = "red")

# The same as
ggplot(variable, aes(lon, lat)) +
    geom_contour(aes(z = z)) +
    geom_contour(aes(z = Laplacian(z ~ lon + lat, cyclical = c(TRUE, FALSE))),
                 color = "red")

Description

Computes Singular Value Decomposition (also known as Principal Components Analysis or Empirical Orthogonal Functions).

Usage

EOF(
  formula,
  n = 1,
  data = NULL,
  B = 0,
  probs = c(lower = 0.025, mid = 0.5, upper = 0.975),
  rotate = NULL,
  suffix = "PC",
  fill = NULL,
  engine = NULL
)

Arguments

Details

Singular values can be computed over matrices so formula denotes how to build a matrix from the data. It is a formula of the form VAR ~ LEFT | RIGHT (see Formula::Formula) in which VAR is the variable whose values will populate the matrix, and LEFT represent the variables used to make the rows and RIGHT, the columns of the matrix. Think it like "VAR as a function of LEFT and RIGHT". The variable combination used in this formula must identify an unique value in a cell.

So, for example, v ~ x + y | t would mean that there is one value of v for each combination of x, y and t, and that there will be one row for each combination of x and y and one row for each t.

In the result, the left and right vectors have dimensions of the LEFT and RIGHT part of the formula, respectively.

It is much faster to compute only some singular vectors, so is advisable not to set n to NULL. If the irlba package is installed, EOF uses irlba::irlba instead of base::svd since it's much faster.

The bootstrapping procedure follows Fisher et.al. (2016) and returns the standard deviation of each singular value.

Value

An eof object which is just a named list of data.tables

left

data.table with left singular vectors

right

data.table with right singular vectors

sdev

data.table with singular values, their explained variance, and, optionally, quantiles estimated via bootstrap

There are some methods implemented

• summary

• screeplot and the equivalent ggplot2::autoplot

• cut.eof

• predict

References

Fisher, A., Caffo, B., Schwartz, B., & Zipunnikov, V. (2016). Fast, Exact Bootstrap Principal Component Analysis for p > 1 million. Journal of the American Statistical Association, 111(514), 846–860. doi:10.1080/01621459.2015.1062383

See Also

Other meteorology functions: Derivate(), GeostrophicWind(), WaveFlux(), thermodynamics, waves

Examples

# The Antarctic Oscillation is computed from the
# monthly geopotential height anomalies weighted by latitude.
library(data.table)
data(geopotential)
geopotential <- copy(geopotential)
geopotential[, gh.t.w := Anomaly(gh)*sqrt(cos(lat*pi/180)),
      by = .(lon, lat, month(date))]

eof <- EOF(gh.t.w ~ lat + lon | date, 1:5, data = geopotential,
           B = 100, probs = c(low = 0.1, hig = 0.9))

# Inspect the explained variance of each component
summary(eof)
screeplot(eof)

# Keep only the 1st.
aao <- cut(eof, 1)

# AAO field
library(ggplot2)
ggplot(aao$left, aes(lon, lat, z = gh.t.w)) +
    geom_contour(aes(color = after_stat(level))) +
    coord_polar()

# AAO signal
ggplot(aao$right, aes(date, gh.t.w)) +
    geom_line()

# standard deviation, % of explained variance and
# confidence intervals.
aao$sdev

# Reconstructed fields based only on the two first
# principal components
field <- predict(eof, 1:2)

# Compare it to the real field.
ggplot(field[date == date[1]], aes(lon, lat)) +
    geom_contour_fill(aes(z = gh.t.w), data = geopotential[date == date[1]]) +
    geom_contour2(aes(z = gh.t.w, linetype = factor(-sign(stat(level))))) +
    scale_fill_divergent()

Description

Computes Eliassen-Palm fluxes.

Usage

EPflux(lon, lat, lev, t, u, v)

Arguments

Value

A data.table with columns Flon, Flat and Flev giving the zonal, meridional and vertical components of the EP Fluxes at each longitude, latitude and level.

References

Plumb, R. A. (1985). On the Three-Dimensional Propagation of Stationary Waves. Journal of the Atmospheric Sciences, 42(3), 217–229. doi:10.1175/1520-0469(1985)042<0217:OTTDPO>2.0.CO;2 Cohen, J., Barlow, M., Kushner, P. J., & Saito, K. (2007). Stratosphere–Troposphere Coupling and Links with Eurasian Land Surface Variability. Journal of Climate, 20(21), 5335–5343. doi:10.1175/2007JCLI1725.1

Description

Computes a linear regression with stats::.lm.fit and returns the estimate and, optionally, standard error for each regressor.

Usage

FitLm(y, ..., intercept = TRUE, weights = NULL, se = FALSE, r2 = se)

ResidLm(y, ..., intercept = TRUE, weights = NULL)

Detrend(y, time = seq_along(y))

Arguments

Value

FitLm returns a list with elements

term

the name of the regressor

estimate

estimate of the regression

std.error

standard error

df

degrees of freedom

r.squared

Percent of variance explained by the model (repeated in each term)

adj.r.squared

r.squared' adjusted based on the degrees of freedom)

ResidLm and Detrend returns a vector of the same length

If there's no complete cases in the regression, NAs are returned with no warning.

Examples

# Linear trend with "signficant" areas shaded with points
library(data.table)
library(ggplot2)
system.time({
  regr <- geopotential[, FitLm(gh, date, se = TRUE), by = .(lon, lat)]
})

ggplot(regr[term != "(Intercept)"], aes(lon, lat)) +
    geom_contour(aes(z = estimate, color = after_stat(level))) +
    stat_subset(aes(subset = abs(estimate) > 2*std.error), size = 0.05)

# Using stats::lm() is much slower and with no names.
## Not run: 
system.time({
  regr <- geopotential[, coef(lm(gh ~ date))[2], by = .(lon, lat)]
})

## End(Not run)

Description

Parametrization of ggplot2::geom_segment either by location and displacement or by magnitude and angle with default arrows. geom_arrow() is the same as geom_vector() but defaults to preserving the direction under coordinate transformation and different plot ratios.

Usage

geom_arrow(
  mapping = NULL,
  data = NULL,
  stat = "arrow",
  position = "identity",
  ...,
  start = 0,
  direction = c("ccw", "cw"),
  pivot = 0.5,
  preserve.dir = TRUE,
  min.mag = 0,
  skip = 0,
  skip.x = skip,
  skip.y = skip,
  arrow.angle = 15,
  arrow.length = 0.5,
  arrow.ends = "last",
  arrow.type = "closed",
  arrow = grid::arrow(arrow.angle, grid::unit(arrow.length, "lines"), ends = arrow.ends,
    type = arrow.type),
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_vector(
  mapping = NULL,
  data = NULL,
  stat = "arrow",
  position = "identity",
  ...,
  start = 0,
  direction = c("ccw", "cw"),
  pivot = 0.5,
  preserve.dir = FALSE,
  min.mag = 0,
  skip = 0,
  skip.x = skip,
  skip.y = skip,
  arrow.angle = 15,
  arrow.length = 0.5,
  arrow.ends = "last",
  arrow.type = "closed",
  arrow = grid::arrow(arrow.angle, grid::unit(arrow.length, "lines"), ends = arrow.ends,
    type = arrow.type),
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Details

Direction and start allows to work with different standards. For the meteorological standard, for example, use star = -90 and direction = "cw".

Aesthetics

geom_vector understands the following aesthetics (required aesthetics are in bold)

• x

• y

• either mag and angle, or dx and dy

• alpha

• colour

• linetype

• size

• lineend

See Also

Other ggplot2 helpers: MakeBreaks(), WrapCircular(), geom_contour2(), geom_contour_fill(), geom_label_contour(), geom_relief(), geom_streamline(), guide_colourstrip(), map_labels, reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Examples

library(data.table)
library(ggplot2)

data(seals)
# If the velocity components are in the same units as the axis,
# geom_vector() (or geom_arrow(preserve.dir = TRUE)) might be a better option
ggplot(seals, aes(long, lat)) +
    geom_arrow(aes(dx = delta_long, dy = delta_lat), skip = 1, color = "red") +
    geom_vector(aes(dx = delta_long, dy = delta_lat), skip = 1) +
    scale_mag()

data(geopotential)
geopotential <- copy(geopotential)[date == date[1]]
geopotential[, gh.z := Anomaly(gh), by = .(lat)]
geopotential[, c("u", "v") := GeostrophicWind(gh.z, lon, lat)]

(g <- ggplot(geopotential, aes(lon, lat)) +
    geom_arrow(aes(dx = dlon(u, lat), dy = dlat(v)), skip.x = 3, skip.y = 2,
               color = "red") +
    geom_vector(aes(dx = dlon(u, lat), dy = dlat(v)), skip.x = 3, skip.y = 2) +
    scale_mag( guide = "none"))

# A dramatic illustration of the difference between arrow and vector
g + coord_polar()

# When plotting winds in a lat-lon grid, a good way to have both
# the correct direction and an interpretable magnitude is to define
# the angle by the longitud and latitude displacement and the magnitude
# by the wind velocity. That way arrows are always parallel to streamlines
# and their magnitude are in the correct units.
ggplot(geopotential, aes(lon, lat)) +
    geom_contour(aes(z = gh.z)) +
    geom_vector(aes(angle = atan2(dlat(v), dlon(u, lat))*180/pi,
                   mag = Mag(v, u)), skip = 1, pivot = 0.5) +
    scale_mag()

# Sverdrup transport
library(data.table)
b <- 10
d <- 10
grid <- as.data.table(expand.grid(x = seq(1, d, by = 0.5),
                                  y = seq(1, b, by = 0.5)))
grid[, My := -sin(pi*y/b)*pi/b]
grid[, Mx := -pi^2/b^2*cos(pi*y/b)*(d - x)]

ggplot(grid, aes(x, y)) +
    geom_arrow(aes(dx = Mx, dy = My))

# Due to limitations in ggplot2 (see: https://github.com/tidyverse/ggplot2/issues/4291),
# if you define the vector with the dx and dy aesthetics, you need
# to explicitly add scale_mag() in order to show the arrow legend.

ggplot(grid, aes(x, y)) +
    geom_arrow(aes(dx = Mx, dy = My)) +
    scale_mag()

# Alternative, use Mag and Angle.
ggplot(grid, aes(x, y)) +
    geom_arrow(aes(mag = Mag(Mx, My), angle = Angle(Mx, My)))

Description

While ggplot2's geom_contour can plot nice contours, it doesn't work with the polygon geom. This stat makes some small manipulation of the data to ensure that all contours are closed and also computes a new aesthetic int.level, which differs from level (computed by ggplot2::geom_contour) in that represents the value of the z aesthetic inside the contour instead of at the edge. It also computes breaks globally instead of per panel, so that faceted plots have all the same binwidth.

Usage

geom_contour_fill(
  mapping = NULL,
  data = NULL,
  stat = "ContourFill",
  position = "identity",
  ...,
  breaks = MakeBreaks(),
  bins = NULL,
  binwidth = NULL,
  proj = NULL,
  proj.latlon = TRUE,
  clip = NULL,
  kriging = FALSE,
  global.breaks = TRUE,
  na.fill = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_contour_fill(
  mapping = NULL,
  data = NULL,
  geom = "polygon",
  position = "identity",
  ...,
  breaks = MakeBreaks(),
  bins = NULL,
  binwidth = NULL,
  global.breaks = TRUE,
  proj = NULL,
  proj.latlon = TRUE,
  clip = NULL,
  kriging = FALSE,
  na.fill = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_contour_fill understands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• group

• linetype

• size

• weight

Computed variables

level

An ordered factor that represents bin ranges.

level_d

Same as level, but automatically uses scale_fill_discretised()

level_low,level_high,level_mid

Lower and upper bin boundaries for each band, as well the mid point between the boundaries.

See Also

Other ggplot2 helpers: MakeBreaks(), WrapCircular(), geom_arrow(), geom_contour2(), geom_label_contour(), geom_relief(), geom_streamline(), guide_colourstrip(), map_labels, reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Examples

library(ggplot2)
surface <- reshape2::melt(volcano)
ggplot(surface, aes(Var1, Var2, z = value)) +
  geom_contour_fill() +
  geom_contour(color = "black", size = 0.1)

ggplot(surface, aes(Var1, Var2, z = value)) +
  geom_contour_fill(aes(fill = after_stat(level)))

ggplot(surface, aes(Var1, Var2, z = value)) +
  geom_contour_fill(aes(fill = after_stat(level_d)))

Description

Illuminated contours (aka Tanaka contours) use varying brightness and width to create an illusion of relief. This can help distinguishing between concave and convex areas (local minimums and maximums), specially in black and white plots or to make photocopy safe plots with divergent colour palettes, or to render a more aesthetically pleasing representation of topography.

Usage

geom_contour_tanaka(
  mapping = NULL,
  data = NULL,
  stat = "Contour2",
  position = "identity",
  ...,
  breaks = NULL,
  bins = NULL,
  binwidth = NULL,
  sun.angle = 60,
  light = "white",
  dark = "gray20",
  range = c(0.01, 0.5),
  smooth = 0,
  proj = NULL,
  proj.latlon = TRUE,
  clip = NULL,
  kriging = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_contour_tanaka understands the following aesthetics (required aesthetics are in bold)

• x

• y

• z

• linetype

Examples

library(ggplot2)
library(data.table)
# A fresh look at the boring old volcano dataset
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour_fill(aes(z = value)) +
    geom_contour_tanaka(aes(z = value)) +
    theme_void()

# If the transition between segments feels too abrupt,
# smooth it a bit with smooth
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour_fill(aes(z = value)) +
    geom_contour_tanaka(aes(z = value), smooth = 1) +
    theme_void()

data(geopotential)
geo <- geopotential[date == unique(date)[4]]
geo[, gh.z := Anomaly(gh), by = lat]

# In a monochrome contour map, it's impossible to know which areas are
# local maximums or minimums.
ggplot(geo, aes(lon, lat)) +
    geom_contour2(aes(z = gh.z), color = "black", xwrap = c(0, 360))

# With tanaka contours, they are obvious.
ggplot(geo, aes(lon, lat)) +
    geom_contour_tanaka(aes(z = gh.z), dark = "black",
                        xwrap = c(0, 360)) +
    scale_fill_divergent()

# A good divergent color palette has the same luminosity for positive
# and negative values.But that means that printed in grayscale (Desaturated),
# they are indistinguishable.
(g <- ggplot(geo, aes(lon, lat)) +
    geom_contour_fill(aes(z = gh.z), xwrap = c(0, 360)) +
    scale_fill_gradientn(colours = c("#767676", "white", "#484848"),
                         values = c(0, 0.415, 1)))

# Tanaka contours can solve this issue.
g + geom_contour_tanaka(aes(z = gh.z))

Description

Similar to ggplot2::geom_contour but it can label contour lines, accepts accepts a function as the breaks argument and and computes breaks globally instead of per panel.

Usage

geom_contour2(
  mapping = NULL,
  data = NULL,
  stat = "contour2",
  position = "identity",
  ...,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  breaks = MakeBreaks(),
  bins = NULL,
  binwidth = NULL,
  global.breaks = TRUE,
  na.rm = FALSE,
  na.fill = FALSE,
  skip = 1,
  margin = grid::unit(c(1, 1, 1, 1), "pt"),
  label.placer = label_placer_flattest(),
  show.legend = NA,
  inherit.aes = TRUE
)

stat_contour2(
  mapping = NULL,
  data = NULL,
  geom = "contour2",
  position = "identity",
  ...,
  breaks = MakeBreaks(),
  bins = NULL,
  binwidth = NULL,
  proj = NULL,
  proj.latlon = TRUE,
  clip = NULL,
  kriging = FALSE,
  global.breaks = TRUE,
  na.rm = FALSE,
  na.fill = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_contour2 understands the following aesthetics (required aesthetics are in bold):

Aesthetics related to contour lines:

• x

• y

• z

• alpha

• colour

• group

• linetype

• size

• weight

Aesthetics related to labels:

• label

• label_colour

• label_alpha

• label_size

• family

• fontface

Computed variables

level

height of contour

See Also

Other ggplot2 helpers: MakeBreaks(), WrapCircular(), geom_arrow(), geom_contour_fill(), geom_label_contour(), geom_relief(), geom_streamline(), guide_colourstrip(), map_labels, reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Other ggplot2 helpers: MakeBreaks(), WrapCircular(), geom_arrow(), geom_contour_fill(), geom_label_contour(), geom_relief(), geom_streamline(), guide_colourstrip(), map_labels, reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Examples

library(ggplot2)

# Breaks can be a function.
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour2(aes(z = value, color = after_stat(level)),
                  breaks = AnchorBreaks(130, binwidth = 10))

# Add labels by supplying the label aes.
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour2(aes(z = value, label = after_stat(level)))

ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour2(aes(z = value, label = after_stat(level)),
                  skip = 0)

# Use label.placer to control where contours are labelled.
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour2(aes(z = value, label = after_stat(level)),
                      label.placer = label_placer_n(n = 2))

# Use the rot_adjuster argument of the placer function to
# control the angle. For example, to fix it to some angle:
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
    geom_contour2(aes(z = value, label = after_stat(level)),
                  skip = 0,
                  label.placer = label_placer_flattest(rot_adjuster = 0))

Description

Draws labels on contours built with ggplot2::stat_contour.

Usage

geom_label_contour(
  mapping = NULL,
  data = NULL,
  stat = "text_contour",
  position = "identity",
  ...,
  min.size = 5,
  skip = 1,
  label.placer = label_placer_flattest(),
  parse = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  label.padding = grid::unit(0.25, "lines"),
  label.r = grid::unit(0.15, "lines"),
  label.size = 0.25,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_text_contour(
  mapping = NULL,
  data = NULL,
  stat = "text_contour",
  position = "identity",
  ...,
  min.size = 5,
  skip = 1,
  rotate = TRUE,
  label.placer = label_placer_flattest(),
  parse = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  stroke = 0,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Details

Is best used with a previous call to ggplot2::stat_contour with the same parameters (e.g. the same binwidth, breaks, or bins). Note that while geom_text_contour() can angle itself to follow the contour, this is not the case with geom_label_contour().

Aesthetics

geom_text_contour understands the following aesthetics (required aesthetics are in bold):

• x

• y

• label

• alpha

• angle

• colour

• stroke.color

• family

• fontface

• group

• hjust

• lineheight

• size

• vjust

See Also

Other ggplot2 helpers: MakeBreaks(), WrapCircular(), geom_arrow(), geom_contour2(), geom_contour_fill(), geom_relief(), geom_streamline(), guide_colourstrip(), map_labels, reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Examples

library(ggplot2)
v <- reshape2::melt(volcano)
g <- ggplot(v, aes(Var1, Var2)) +
       geom_contour(aes(z = value))
g + geom_text_contour(aes(z = value))

g + geom_text_contour(aes(z = value), stroke = 0.2)

g + geom_text_contour(aes(z = value), stroke = 0.2, stroke.colour = "red")

g + geom_text_contour(aes(z = value, stroke.colour = after_stat(level)), stroke = 0.2) +
    scale_colour_gradient(aesthetics = "stroke.colour", guide = "none")

g + geom_text_contour(aes(z = value), rotate = FALSE)

g + geom_text_contour(aes(z = value),
                      label.placer = label_placer_random())

g + geom_text_contour(aes(z = value),
                      label.placer = label_placer_n(3))

g + geom_text_contour(aes(z = value),
                      label.placer = label_placer_flattest())

g + geom_text_contour(aes(z = value),
                      label.placer = label_placer_flattest(ref_angle = 90))

Description

geom_relief() simulates shading caused by relief. Can be useful when plotting topographic data because relief shading might give a more intuitive impression of the shape of the terrain than contour lines or mapping height to colour. geom_shadow() projects shadows.

Usage

geom_relief(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  sun.angle = 60,
  raster = TRUE,
  interpolate = TRUE,
  shadow = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_shadow(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  sun.angle = 60,
  range = c(0, 1),
  skip = 0,
  raster = TRUE,
  interpolate = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Details

light and dark must be valid colours determining the light and dark shading (defaults to "white" and "gray20", respectively).

Aesthetics

geom_relief() and geom_shadow() understands the following aesthetics (required aesthetics are in bold)

• x

• y

• z

• light

• dark

• sun.angle

See Also

Other ggplot2 helpers: MakeBreaks(), WrapCircular(), geom_arrow(), geom_contour2(), geom_contour_fill(), geom_label_contour(), geom_streamline(), guide_colourstrip(), map_labels, reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Examples

## Not run: 
library(ggplot2)
ggplot(reshape2::melt(volcano), aes(Var1, Var2)) +
      geom_relief(aes(z = value))

## End(Not run)

Description

Streamlines are paths that are always tangential to a vector field. In the case of a steady field, it's identical to the path of a massless particle that moves with the "flow".

Usage

geom_streamline(
  mapping = NULL,
  data = NULL,
  stat = "streamline",
  position = "identity",
  ...,
  L = 5,
  min.L = 0,
  res = 1,
  S = NULL,
  dt = NULL,
  xwrap = NULL,
  ywrap = NULL,
  skip = 1,
  skip.x = skip,
  skip.y = skip,
  n = NULL,
  nx = n,
  ny = n,
  jitter = 1,
  jitter.x = jitter,
  jitter.y = jitter,
  arrow.angle = 6,
  arrow.length = 0.5,
  arrow.ends = "last",
  arrow.type = "closed",
  arrow = grid::arrow(arrow.angle, grid::unit(arrow.length, "lines"), ends = arrow.ends,
    type = arrow.type),
  lineend = "butt",
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_streamline(
  mapping = NULL,
  data = NULL,
  geom = "streamline",
  position = "identity",
  ...,
  L = 5,
  min.L = 0,
  res = 1,
  S = NULL,
  dt = NULL,
  xwrap = NULL,
  ywrap = NULL,
  skip = 1,
  skip.x = skip,
  skip.y = skip,
  n = NULL,
  nx = n,
  ny = n,
  jitter = 1,
  jitter.x = jitter,
  jitter.y = jitter,
  arrow.angle = 6,
  arrow.length = 0.5,
  arrow.ends = "last",
  arrow.type = "closed",
  arrow = grid::arrow(arrow.angle, grid::unit(arrow.length, "lines"), ends = arrow.ends,
    type = arrow.type),
  lineend = "butt",
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Details

Streamlines are computed by simple integration with a forward Euler method. By default, stat_streamline() computes dt and S from L, res, the resolution of the grid and the mean magnitude of the field. S is then defined as the number of steps necessary to make a streamline of length L under an uniform mean field and dt is chosen so that each step is no larger than the resolution of the data (divided by the res parameter). Be aware that this rule of thumb might fail in field with very skewed distribution of magnitudes.

Alternatively, L and/or res are ignored if S and/or dt are specified explicitly. This not only makes it possible to fine-tune the result but also divorces the integration parameters from the properties of the data and makes it possible to compare streamlines between different fields.

The starting grid is a semi regular grid defined, either by the resolution of the field and the skip.x and skip.y parameters o the nx and ny parameters, jittered by an amount proportional to the resolution of the data and the jitter.x and jitter.y parameters.

It might be important that the units of the vector field are compatible to the units of the x and y dimensions. For example, passing dx and dy in m/s on a longitude-latitude grid will might misleading results (see spherical).

Missing values are not permitted and the field must be defined on a regular grid, for now.

Aesthetics

stat_streamline understands the following aesthetics (required aesthetics are in bold)

• x

• y

• dx

• dy

• alpha

• colour

• linetype

• size

Computed variables

step

step in the simulation

dx

dx at each location of the streamline

dy

dy at each location of the streamline

See Also

Other ggplot2 helpers: MakeBreaks(), WrapCircular(), geom_arrow(), geom_contour2(), geom_contour_fill(), geom_label_contour(), geom_relief(), guide_colourstrip(), map_labels, reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Examples

## Not run: 
library(data.table)
library(ggplot2)
data(geopotential)

geopotential <- copy(geopotential)[date == date[1]]
geopotential[, gh.z := Anomaly(gh), by = .(lat)]
geopotential[, c("u", "v") := GeostrophicWind(gh.z, lon, lat)]

(g <- ggplot(geopotential, aes(lon, lat)) +
    geom_contour2(aes(z = gh.z), xwrap = c(0, 360)) +
    geom_streamline(aes(dx = dlon(u, lat), dy = dlat(v)), L = 60,
                    xwrap = c(0, 360)))

# The circular parameter is particularly important for polar coordinates
g + coord_polar()

# If u and v are not converted into degrees/second, the resulting
# streamlines have problems, specially near the pole.
ggplot(geopotential, aes(lon, lat)) +
    geom_contour(aes(z = gh.z)) +
    geom_streamline(aes(dx = u, dy = v), L = 50)

# The step variable can be mapped to size or alpha to
# get cute "drops". It's important to note that after_stat(dx) (the calculated variable)
# is NOT the same as dx (from the data).
ggplot(geopotential, aes(lon, lat)) +
    geom_streamline(aes(dx = dlon(u, lat), dy = dlat(v), alpha = after_stat(step),
                        color = sqrt(after_stat(dx^2) + after_stat(dy^2)),
                        size = after_stat(step)),
                        L = 40, xwrap = c(0, 360), res = 2, arrow = NULL,
                        lineend = "round") +
    scale_size(range = c(0, 0.6))

# Using topographic information to simulate "rivers" from slope
topo <- GetTopography(295, -55+360, -30, -42, res = 1/20)  # needs internet!
topo[, c("dx", "dy") := Derivate(h ~ lon + lat)]
topo[h <= 0, c("dx", "dy") := 0]

# See how in this example the integration step is too coarse in the
# western montanous region where the slope is much higher than in the
# flatlands of La Pampa at in the east.
ggplot(topo, aes(lon, lat)) +
    geom_relief(aes(z = h), interpolate = TRUE, data = topo[h >= 0]) +
    geom_contour(aes(z = h), breaks = 0, color = "black") +
    geom_streamline(aes(dx = -dx, dy = -dy), L = 10, skip = 3, arrow = NULL,
                    color = "#4658BD") +
    coord_quickmap()
 
## End(Not run)

Description

Monthly geopotential field at 700hPa south of 20°S from January 1990 to December 2000.

Usage

geopotential

Format

A data.table with 53224 rows and 5 variables.

lon

longitude in degrees

lat

latitude in degrees

lev

level in hPa

gh

geopotential height in meters

date

date

Source

https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.derived.pressure.html

Description

Geostrophic wind from a geopotential height field.

Usage

GeostrophicWind(gh, lon, lat, cyclical = "guess", g = 9.81, a = 6371000)

Arguments

Details

If cyclical = "guess" (the default) the function will try to guess if lon covers the whole globe and set cyclical conditions accordingly. For more predictable results, set the boundary condition explicitly.

Value

A named list with vectors for the zonal and meridional component of geostrophic wind.

See Also

Other meteorology functions: Derivate(), EOF(), WaveFlux(), thermodynamics, waves

Examples

data(geopotential)
geopotential <- data.table::copy(geopotential)
geopotential[date == date[1], c("u", "v") := GeostrophicWind(gh, lon, lat)]
library(ggplot2)
ggplot(geopotential[date == date[1]], aes(lon, lat)) +
    geom_contour(aes(z = gh)) +
    geom_vector(aes(dx = u, dy = v), skip = 2) +
    scale_mag()

Description

Get Meteorological data This function is defunct.

Usage

GetSMNData(
  date,
  type = c("hourly", "daily", "radiation"),
  bar = FALSE,
  cache = TRUE,
  file.dir = tempdir()
)

Arguments

Value

Nothing

Description

Retrieves topographic data from ETOPO1 Global Relief Model (see references).

Usage

GetTopography(
  lon.west,
  lon.east,
  lat.north,
  lat.south,
  resolution = 3.5,
  cache = TRUE,
  file.dir = tempdir(),
  verbose = interactive()
)

Arguments

Details

Very large requests can take long and can be denied by the NOAA server. If the function fails, try with a smaller bounding box or coarser resolution.

Longitude coordinates must be between 0 and 360.

Value

A data table with height (in meters) for each longitude and latitude.

References

Source: Amante, C. and B.W. Eakins, 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA. doi:10.7289/V5C8276M

Examples

## Not run: 
topo <- GetTopography(280, 330, 0, -60, resolution = 0.5)
library(ggplot2)
ggplot(topo, aes(lon, lat)) +
    geom_raster(aes(fill = h)) +
    geom_contour(aes(z = h), breaks = 0, color = "black", size = 0.3) +
    scale_fill_gradient2(low = "steelblue", high = "goldenrod2", mid = "olivedrab") +
    coord_quickmap()

## End(Not run)

Description

Provides methods for (soft) imputation of missing values.

Usage

Impute2D(formula, data = NULL, method = "interpolate")

Arguments

Details

This is "soft" imputation because the imputed values are not supposed to be representative of the missing data but just filling for algorithms that need complete data (in particular, contouring). The method used if method = "interpolate" is to do simple linear interpolation in both the x and y direction and then average the result.

This is the imputation method used by geom_contour_fill().

Description

Imputes missing values via Data Interpolating Empirical Orthogonal Functions (DINEOF).

Usage

ImputeEOF(
  formula,
  max.eof = NULL,
  data = NULL,
  min.eof = 1,
  tol = 0.01,
  max.iter = 10000,
  validation = NULL,
  verbose = interactive()
)

Arguments

Details

Singular values can be computed over matrices so formula denotes how to build a matrix from the data. It is a formula of the form VAR ~ LEFT | RIGHT (see Formula::Formula) in which VAR is the variable whose values will populate the matrix, and LEFT represent the variables used to make the rows and RIGHT, the columns of the matrix. Think it like "VAR as a function of LEFT and RIGHT".

Alternatively, if value.var is not NULL, it's possible to use the (probably) more familiar data.table::dcast formula interface. In that case, data must be provided.

If data is a matrix, the formula argument is ignored and the function returns a matrix.

Value

A vector of imputed values with attributes eof, which is the number of singular values used in the final imputation; and rmse, which is the Root Mean Square Error estimated from cross-validation.

References

Beckers, J.-M., Barth, A., and Alvera-Azcárate, A.: DINEOF reconstruction of clouded images including error maps – application to the Sea-Surface Temperature around Corsican Island, Ocean Sci., 2, 183-199, doi:10.5194/os-2-183-2006, 2006.

Examples

library(data.table)
data(geopotential)
geopotential <- copy(geopotential)
geopotential[, gh.t := Anomaly(gh), by = .(lat, lon, month(date))]

# Add gaps to field
geopotential[, gh.gap := gh.t]
set.seed(42)
geopotential[sample(1:.N, .N*0.3), gh.gap := NA]

max.eof <- 5    # change to a higher value
geopotential[, gh.impute := ImputeEOF(gh.gap ~ lat + lon | date, max.eof,
                                      verbose = TRUE, max.iter = 2000)]

library(ggplot2)
ggplot(geopotential[date == date[1]], aes(lon, lat)) +
    geom_contour(aes(z = gh.t), color = "black") +
    geom_contour(aes(z = gh.impute))

# Scatterplot with a sample.
na.sample <- geopotential[is.na(gh.gap)][sample(1:.N, .N*0.1)]
ggplot(na.sample, aes(gh.t, gh.impute)) +
    geom_point()

# Estimated RMSE
attr(geopotential$gh.impute, "rmse")
# Real RMSE
geopotential[is.na(gh.gap), sqrt(mean((gh.t - gh.impute)^2))]

Description

Interpolates values using bilinear interpolation.

Usage

Interpolate(formula, x.out, y.out, data = NULL, grid = TRUE, path = FALSE)

Arguments

Details

formula must be of the form VAR1 | VAR2 ~ X + Y where VAR1, VAR2, etc... are the names of the variables to interpolate and X and Y the names of the x and y values, respectively. It is also possible to pass only values of x, in which case, regular linear interpolation is performed and y.out, if exists, is ignored with a warning.

If grid = TRUE, x.out and y.out must define the values of a regular grid. If grid = FALSE, they define the locations where to interpolate. Both grid and path cannot be set to TRUE and the value of path takes precedence.

x.out can be a list, in which case, the first two elements will be interpreted as the x and y values where to interpolate and it can also have a path element that will be used in place of the path argument. This helps when creating a path with as.path (see Examples)

Value

A data.frame with interpolated values and locations

Examples

library(data.table)
data(geopotential)
geopotential <- geopotential[date == date[1]]
# new grid
x.out <- seq(0, 360, by = 10)
y.out <- seq(-90, 0, by = 10)

# Interpolate values to a new grid
interpolated <- geopotential[, Interpolate(gh ~ lon + lat, x.out, y.out)]

# Add values to an existing grid
geopotential[, gh.new := Interpolate(gh ~ lon + lat, lon, lat,
                                     data = interpolated, grid = FALSE)$gh]

# Interpolate multiple values
geopotential[, c("u", "v") := GeostrophicWind(gh, lon, lat)]
interpolated <- geopotential[, Interpolate(u | v ~ lon + lat, x.out, y.out)]

# Interpolate values following a path
lats <- c(-34, -54, -30)   # start and end latitudes
lons <- c(302, 290, 180)   # start and end longituded
path <- geopotential[, Interpolate(gh ~ lon + lat, as.path(lons, lats))]

Description

Reduces the density of a regular grid using a cross pattern.

Usage

is.cross(x, y, skip = 0)

cross(x, y)

Arguments

Value

is.cross returns a logical vector indicating whether each point belongs to the reduced grid or not. cross returns a list of x and y components of the reduced density grid.

Examples

# Basic usage
grid <- expand.grid(x = 1:10, y = 1:10)
cross <- is.cross(grid$x, grid$y, skip = 2)

with(grid, plot(x, y))
with(grid, points(x[cross], y[cross], col = "red"))

# Its intended use is to highlight areas with geom_subset()
# with reduced densnity. This "hatches" areas with temperature
# over 270K
library(ggplot2)
ggplot(temperature[lev == 500], aes(lon, lat)) +
  geom_raster(aes(fill = air)) +
  stat_subset(aes(subset = air > 270 & is.cross(lon, lat)),
              geom = "point", size = 0.1)

Description

Skip observations

Usage

JumpBy(x, by, start = 1, fill = NULL)

Arguments

Details

Mostly useful for labelling only every byth element.

Value

A vector of the same class as x and, if fill is not null, the same length.

See Also

Other utilities: Anomaly(), Mag(), Percentile(), logic

Examples

x <- 1:50
JumpBy(x, 2)   # only odd numbers
JumpBy(x, 2, start = 2)   # only even numbers
JumpBy(x, 2, fill = NA)   # even numbers replaced by NA
JumpBy(x, 2, fill = 6)   # even numbers replaced by 6

Description

Extended binary operators for easy subsetting.

Usage

x %~% target

Similar(x, target, tol = Inf)

Arguments

Details

%~% can be thought as a "similar" operator. It's a fuzzy version of %in% in that returns TRUE for the element of x which is the (first) closest to any element of target.

Similar is a functional version of %~% that also has a tol parameter that indicates the maximum allowed tolerance.

Value

A logical vector of the same length of x.

See Also

Other utilities: Anomaly(), JumpBy(), Mag(), Percentile()

Examples

set.seed(198)
x <- rnorm(100)
x[x %~% c(0.3, 0.5, 1)]

# Practical use case: vertical cross-section at
# approximately 36W between 50S and 50N.
cross.lon <- -34 + 360
library(ggplot2)
library(data.table)
ggplot(temperature[lon %~% cross.lon & lat %between% c(-50, 50)],
       aes(lat, lev)) +
    geom_contour(aes(z = air))

Description

Computes the magnitude of a vector of any dimension. Or angle (in degrees) in 2 dimensions.

Usage

Mag(...)

Angle(x, y)

Arguments

Details

Helpful to save keystrokes and gain readability when computing wind (or any other vector quantity) magnitude.

Value

Mag: A numeric vector the same length as each element of ... that is $\sqrt(x^2 + y^2 + ...)$. Angle: A numeric vector of the same length as x and y that is atan2(y, x)*180/pi.

See Also

Other utilities: Anomaly(), JumpBy(), Percentile(), logic

Other utilities: Anomaly(), JumpBy(), Percentile(), logic

Examples

Mag(10, 10)
Angle(10, 10)
Mag(10, 10, 10, 10)
Mag(list(10, 10, 10, 10))

# There's no vector recicling!
## Not run: 
Mag(1, 1:2)

## End(Not run)

Description

Functions that return functions suitable to use as the breaks argument in ggplot2's continuous scales and in geom_contour_fill.

Usage

MakeBreaks(binwidth = NULL, bins = 10, exclude = NULL)

AnchorBreaks(anchor = 0, binwidth = NULL, exclude = NULL, bins = 10)

Arguments

Details

MakeBreaks is essentially an export of the default way ggplot2::stat_contour makes breaks.

AnchorBreaks makes breaks starting from an anchor value and covering the range of the data according to binwidth.

Value

A function that takes a range as argument and a binwidth as an optional argument and returns a sequence of equally spaced intervals covering the range.

See Also

Other ggplot2 helpers: WrapCircular(), geom_arrow(), geom_contour2(), geom_contour_fill(), geom_label_contour(), geom_relief(), geom_streamline(), guide_colourstrip(), map_labels, reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Examples

my_breaks <- MakeBreaks(10)
my_breaks(c(1, 100))
my_breaks(c(1, 100), 20)    # optional new binwidth argument ignored

MakeBreaks()(c(1, 100), 20)  # but is not ignored if initial binwidth is NULL

# One to one mapping between contours and breaks
library(ggplot2)
binwidth <- 20
ggplot(reshape2::melt(volcano), aes(Var1, Var2, z = value)) +
    geom_contour(aes(color = after_stat(level)), binwidth = binwidth) +
    scale_color_continuous(breaks = MakeBreaks(binwidth))

#Two ways of getting the same contours. Better use the second one.
ggplot(reshape2::melt(volcano), aes(Var1, Var2, z = value)) +
    geom_contour2(aes(color = after_stat(level)), breaks = AnchorBreaks(132),
                  binwidth = binwidth) +
    geom_contour2(aes(color = after_stat(level)), breaks = AnchorBreaks(132, binwidth)) +
    scale_color_continuous(breaks = AnchorBreaks(132, binwidth))

Description

Provide easy functions for adding suffixes to longitude and latitude for labelling maps.

Usage

LonLabel(lon, east = "°E", west = "°W", zero = "°")

LatLabel(lat, north = "°N", south = "°S", zero = "°")

Arguments

Details

The default values are for Spanish.

See Also

Other ggplot2 helpers: MakeBreaks(), WrapCircular(), geom_arrow(), geom_contour2(), geom_contour_fill(), geom_label_contour(), geom_relief(), geom_streamline(), guide_colourstrip(), reverselog_trans(), scale_divergent, scale_longitude, stat_na(), stat_subset()

Examples

LonLabel(0:360)

Description

Creates a mask

Usage

MaskLand(lon, lat, mask = "world", wrap = c(0, 360))

Arguments

Value

A logical vector of the same length as lat and lon where TRUE means that the point is inside one of the polygons making up the map. For a global map (the default), this means that the point is over land.

Examples

# Make a sea-land mask
mask <- temperature[lev == 1000, .(lon = lon, lat = lat, land = MaskLand(lon, lat))]
temperature <- temperature[mask, on = c("lon", "lat")]
library(ggplot2)

ggplot(mask, aes(lon, lat)) +
   geom_raster(aes(fill = land))

# Take the temperature difference between land and ocean
diftemp <- temperature[,
          .(tempdif = mean(air[land == TRUE]) - mean(air[land == FALSE])),
           by = .(lat, lev)]

ggplot(diftemp, aes(lat, lev)) +
    geom_contour(aes(z = tempdif, color = after_stat(level))) +
    scale_y_level() +
    scale_x_latitude() +
    scale_color_divergent()

Description

Many useful functions and extensions for dealing with meteorological data in the tidy data framework. Extends 'ggplot2' for better plotting of scalar and vector fields and provides commonly used analysis methods in the atmospheric sciences.

Overview

Conceptually it's divided into visualization tools and data tools. The former are geoms, stats and scales that help with plotting using 'ggplot2', such as stat_contour_fill or scale_y_level, while the later are functions for common data processing tools in the atmospheric sciences, such as Derivate or EOF; these are implemented to work in the 'data.table' paradigm, but also work with regular data frames.

To get started, check the vignettes:

• Visualization Tools: vignette("Visualization-tools", package = "metR")

• Working with Data: vignette("Working-with-data", package = "metR")

Author(s)

Maintainer: Elio Campitelli [email protected] (ORCID)

See Also

Useful links:

• https://eliocamp.github.io/metR/

• https://github.com/eliocamp/metR

• Report bugs at https://github.com/eliocamp/metR/issues

Description

Computes percentiles.

Usage

Percentile(x)

Arguments

Value

A numeric vector of the same length as x with the percentile of each value of x.

See Also

Other utilities: Anomaly(), JumpBy(), Mag(), logic

Examples

x <- rnorm(100)
p <- Percentile(x)

Description

Using the ncdf4-package package, it reads a NetCDF file. The advantage over using ncvar_get is that the output is a tidy data.table with proper dimensions.

Usage

ReadNetCDF(
  file,
  vars = NULL,
  out = c("data.frame", "vector", "array"),
  subset = NULL,
  key = FALSE
)

GlanceNetCDF(file, ...)

Arguments