Interpolate in 2D with data from dataframe using dplyr in R
I have two data frames, Reference and Interpolated. These are glimpse () links:
$ Value (dbl) 62049.67, 62040.96, 62053.02, 62039.31, 62020.82, 62001.73,...
$ X (dbl) -10.14236, -10.14236, -10.14236, -10.14236, -10.14236, -10....
$ Y (dbl) -12.68236, -12.64708, -12.61181, -12.57653, -12.54125, -12....
And this is Interpolated:
$ X (dbl) -10.1346, -10.0838, -10.0330, -9.9822, -9.9314, -9.8806, -9...
$ Y (dbl) -12.6746, -12.6746, -12.6746, -12.6746, -12.6746, -12.6746,...
I want to get the variable Value to Interpolated using 2D interpolation from Reference.
I was thinking about using the bicubic () function from the akima package, something like bicubic(Reference$X, Reference$Y, Reference$Value, Interpolated$X, Interpolated$Y)
. However, bicubic () expects a matrix in Reference $ Value.
Is there an easy way to interpolate in 2D with data from a dataframe, preferably using dplyr?
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I don't know if you ever got an answer. I was looking for the same thing and had to create my own functions for this. See below:
interpolate <- function(x, x1, x2, y1, y2) {
# Interpolates between two points.
#
# Args:
# x: Corresponding x value of y value to return.
# x1: Low x-value.
# x2: High x-value.
# y1: Low y-value.
# y2: High y-value.
#
# Returns:
# Interpolated value corresponding to x between the two points.
y <- y1 + (y2-y1)*(x-x1)/(x2-x1)
return(y)
}
doubleinterpolate <- function(x, y, z, xout, yout) {
# Returns a double interpolated value among three vectors with two
# values in two of the vectors.
#
# Args:
# x: Vector containing a known value.
# y: Vector containing a known value.
# z: Vector containing an unknown value.
# xout: Known value in x-vector.
# yout: Known value in y-vector.
#
# Returns:
# Double interpolated value in z of the points xout and yout.
# Determine adjacent values in the table
x_low <- max(x[x < xout])
x_high <- min(x[x > xout])
y_low <- max(y[y < yout])
y_high <- min(y[y > yout])
# Create df and subset
df <- data_frame(x = x, y = y, z = z)
df_low <- df[x == x_low, ]
df_high <- df[x == x_high, ]
# Interpolate low x-values
yint1 <- as.numeric(spline(df_low$y, df_low$z, xout = yout)[2])
yint2 <- as.numeric(spline(df_high$y, df_high$z, xout = yout)[2])
#Interpolate to get last value
zout <- interpolate(xout, x_low, x_high, yint1, yint2)
return(zout)
}
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