metR v0.6.0

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Tools for Easier Analysis of Meteorological Fields

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.

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metR

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status CRAN
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metR packages several functions and utilities that make R better for handling meteorological data in the tidy data paradigm. It started mostly sa a packaging of assorted wrappers and tricks that I wrote for my day to day work as a researcher in atmospheric sciences. Since then, it has grown organically and for my own needs and feedback from users.

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.

Currently metR is in developement but maturing. Most functions check arguments and there are some tests. However, some functions might change it’s interface, and functionality can be moved to other packages, so please bear that in mind.

Installation

You can install metR from CRAN with:

install.packages("metR")

Or the developement version with:

# install.packages("devtools")
devtools::install_github("eliocamp/metR")

If you need to read netcdf files, you might need to install the netcdf and udunits2 libraries. On Ubuntu and it’s derivatives this can be done by typing

sudo apt install libnetcdf-dev netcdf-bin libudunits2-dev

Examples

In this example we easily perform Principal Components Decomposition (EOF) on monthly geopotential height, then compute the geostrophic wind associated with this field and plot the field with filled contours and the wind with streamlines.

library(metR)
library(data.table)
library(ggplot2)
data(geopotential)
# Use Empirical Orthogonal Functions to compute the Antarctic Oscillation
geopotential <- copy(geopotential)
geopotential[, gh.t.w := Anomaly(gh)*sqrt(cos(lat*pi/180)),
      by = .(lon, lat, month(date))]
aao <- EOF(gh.t.w ~ lat + lon | date, data = geopotential, n = 1)
aao$left[, c("u", "v") := GeostrophicWind(gh.t.w, lon, lat)]

# AAO field
binwidth <- 0.01
ggplot(aao$left, aes(lon, lat, z = gh.t.w)) +
    geom_contour_fill(binwidth = binwidth, xwrap = c(0, 360)) +    # filled contours!
    geom_streamline(aes(dx = dlon(u, lat), dy = dlat(v)), 
                    size = 0.4, L = 80, skip = 3, xwrap = c(0, 360)) +
    scale_x_longitude() +
    scale_y_latitude(limits = c(-90, -20)) +
    scale_fill_divergent(name = "AAO pattern", 
                         breaks = MakeBreaks(binwidth),
                         guide = guide_colorstrip()) +
    coord_polar()
#> Warning in .check_wrap_param(list(...)): 'xwrap' and 'ywrap' will be deprecated.
#> Use ggperiodic::periodic insead.

# AAO signal
ggplot(aao$right, aes(date, gh.t.w)) +
    geom_line() +
    geom_smooth(span = 0.4)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

You can read more in the vignettes: Visualization tools and Working with data.

Functions in metR

Name Description
Derivate Derivate a discrete variable using finite differences
ConvertLongitude Converts between longitude conventions
Impute2D Impute missing values by linear or constant interpolation
WaveFlux Calculate wave-activity flux
WrapCircular Wrap periodic data to any range
Trajectory Compute trajectories
ImputeEOF Impute missing values
spherical Transform between spherical coordinates and physical coordinates
guide_vector Reference arrow for magnitude scales
season Assign seasons to months
cut.eof Remove some principal components.
denormalise Denormalise eof matrices
guide_colourstrip Discretized continuous colour guide
is.cross Cross pattern
FitLm Fast estimates of linear regression
stat_na Filter only NA values.
logic Extended logical operators
MakeBreaks Functions for making breaks
Mag Magnitude of a vector
stat_subset Subset values
GeostrophicWind Calculate geostrophic winds
geom_contour_tanaka Illuminated contours
geom_contour_fill Filled 2d contours of a 3d surface
geom_relief Relief Shading
geom_streamline Streamlines
GetSMNData Get Meteorological data
MaskLand Mask
Percentile Percentiles
thermodynamics Thermodynamics
GetTopography Get topographic data
temperature Air temperature
EOF Empirical Orthogonal Function
EPflux Computes Eliassen-Palm fluxes.
JumpBy Skip observations
Interpolate Bilinear interpolation
coriolis Effects of the Earth's rotation
as.path Interpolates between locations
geom_label_contour Label contours
scale_longitude Helpful scales for maps
scale_mag Scale for vector magnitudes
waves Fourier transform
geopotential Geopotential height
geom_contour2 2d contours of a 3d surface
map_labels Label longitude and latitude
geom_arrow Arrows
metR metR: Tools for Easier Analysis of Meteorological Fields
scale_divergent Divergent colour scales
reverselog_trans Reverse log transform
DivideTimeseries Divides long timeseries for better reading
Anomaly Anomalies
ReadNetCDF Read NetCDF files.
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Vignettes of metR

Name
Visualization-tools.Rmd
Working-with-data.Rmd
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Details

Type Package
Language en-GB
License GPL-3
URL https://github.com/eliocamp/metR
BugReports https://github.com/eliocamp/metR/issues
ByteCompile yes
Encoding UTF-8
LazyData true
RoxygenNote 7.0.2
VignetteBuilder knitr
NeedsCompilation no
Packaged 2020-02-10 15:32:11 UTC; elio
Repository CRAN
Date/Publication 2020-02-10 16:30:02 UTC

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