Interpolate time-indexed data frame — approx

Given a numeric data frame Y with rows indexed by a time vector tt, interpolates at time values specified by the vector tt_est. If tt_est is not in tt, will create new rows in the data frame corresponding to these interpolated points.

Usage

approx_df(Y, tt, tt_est = NULL, ...)

Arguments

Y: A data frame with all numeric columns
tt: A time vector with length equal to nrow(Y), indexing the rows in Y.
tt_est: A time vector of points to interpolate in Y. If NULL, will attempt to interpolate all points in Y (you may need to adjust the rule argument of stats::approx() here). Note that points not specified in tt_est will not be interpolated. tt_est does not need to be a subset of tt.
...: Further arguments to pass to stats::approx() other than x, y and xout.

Value

A list with:

.$tt the vector of time points, including time values of interpolated points
.$Y the corresponding interpolated data frame

Both outputs are sorted by tt.

Details

This is a wrapper for stats::approx(), with some differences. In the first place, stats::approx() is applied to each column of Y, using tt each time as the corresponding time vector indexing Y. Interpolated values are generated at points specified in tt_est but these are appended to the existing data (whereas stats::approx() will only return the interpolated points and nothing else). Further arguments to stats::approx() can be passed using the ... argument.

Examples

# a time vector
tt <- 2011:2020

# two random vectors with some missing values
y1 <- runif(10)
y2 <- runif(10)
y1[2] <- y1[5] <- NA
y2[3] <- y2[5] <- NA
# make into df
Y <- data.frame(y1, y2)

# interpolate for time = 2012
Y_int <- approx_df(Y, tt, 2012)
Y_int$Y
#>             y1        y2
#> 1  0.894915818 0.1376303
#> 2  0.830897179 0.0622309
#> 3  0.766878539        NA
#> 4  0.414541042 0.6655709
#> 5           NA        NA
#> 6  0.511333802 0.9136983
#> 7  0.743476679 0.8937201
#> 8  0.888419045 0.0897850
#> 9  0.612988593 0.5389992
#> 10 0.003176101 0.4564761

# notice Y_int$y2 is unchanged since at 2012 it did not have NA value
stopifnot(identical(Y_int$Y$y2, y2))

# interpolate at value not in tt
approx_df(Y, tt, 2015.5)
#> $tt
#>  [1] 2011.0 2012.0 2013.0 2014.0 2015.0 2015.5 2016.0 2017.0 2018.0 2019.0
#> [11] 2020.0
#> 
#> $Y
#>             y1        y2
#> 1  0.894915818 0.1376303
#> 2           NA 0.0622309
#> 3  0.766878539        NA
#> 4  0.414541042 0.6655709
#> 5           NA        NA
#> 6  0.487135612 0.8516664
#> 7  0.511333802 0.9136983
#> 8  0.743476679 0.8937201
#> 9  0.888419045 0.0897850
#> 10 0.612988593 0.5389992
#> 11 0.003176101 0.4564761
#>