Interpolates values using bilinear interpolation.
Interpolate(formula, x.out, y.out, data = NULL, grid = TRUE, path = FALSE)a formula indicating dependent and independent variables (see Details)
x and y values where to interpolate (see Details)
optional data.frame with the data
logical indicating if x.out and y.out define a regular grid.
a logical or character indicating if the x.out and y.out define a path. If character, it will be the name of the column returning the order of said path.
A data.frame with interpolated values and locations
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)
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]
#> lon lat lev gh date gh.new
#> 1: 0.0 -22.5 700 3163.839 1990-01-01 NA
#> 2: 2.5 -22.5 700 3162.516 1990-01-01 NA
#> 3: 5.0 -22.5 700 3162.226 1990-01-01 NA
#> 4: 7.5 -22.5 700 3162.323 1990-01-01 NA
#> 5: 10.0 -22.5 700 3163.097 1990-01-01 NA
#> ---
#> 4028: 347.5 -90.0 700 2715.936 1990-01-01 2715.936
#> 4029: 350.0 -90.0 700 2715.936 1990-01-01 NA
#> 4030: 352.5 -90.0 700 2715.936 1990-01-01 NA
#> 4031: 355.0 -90.0 700 2715.936 1990-01-01 NA
#> 4032: 357.5 -90.0 700 2715.936 1990-01-01 NA
# Interpolate multiple values
geopotential[, c("u", "v") := GeostrophicWind(gh, lon, lat)]
#> lon lat lev gh date gh.new u v
#> 1: 0.0 -22.5 700 3163.839 1990-01-01 NA NA 1.08181190
#> 2: 2.5 -22.5 700 3162.516 1990-01-01 NA NA 0.55189199
#> 3: 5.0 -22.5 700 3162.226 1990-01-01 NA NA 0.06625043
#> 4: 7.5 -22.5 700 3162.323 1990-01-01 NA NA -0.29800162
#> 5: 10.0 -22.5 700 3163.097 1990-01-01 NA NA -0.75064329
#> ---
#> 4028: 347.5 -90.0 700 2715.936 1990-01-01 2715.936 NA 0.00000000
#> 4029: 350.0 -90.0 700 2715.936 1990-01-01 NA NA 0.00000000
#> 4030: 352.5 -90.0 700 2715.936 1990-01-01 NA NA 0.00000000
#> 4031: 355.0 -90.0 700 2715.936 1990-01-01 NA NA 0.00000000
#> 4032: 357.5 -90.0 700 2715.936 1990-01-01 NA NA 0.00000000
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))]