一个丑陋且可能效率低下的 Base R 解决方案：
# Data provided:
A <- cbind(time=c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100),
Avalue=c(1, 2, 3, 2, 1, 2, 3, 2, 1, 2))
B <- cbind(time=c(15, 30, 45, 60), Bvalue=c(100, 200, 300, 400))
C <- merge(A,B, all=TRUE)
# Scalar valued at the minimum time difference: -> min_time_diff
min_time_diff <- min(diff(C$time))
# Adjust frequency of the series to hold all steps in range: -> df
df <- merge(C,
data.frame(time = seq(min_time_diff,
max(C$time),
by = min_time_diff)),
by = "time",
all = TRUE)
# Linear interpolation function handling ties,
# returns interpolated vector the same length
# a the input vector: -> vector
l_interp_vec <- function(na_vec){
approx(x = na_vec,
method = "linear",
ties = "constant",
n = length(na_vec))$y
}
# Applied to a dataframe, replacing NA values
# in each of the numeric vectors,
# with interpolated values.
# input is dataframe: -> dataframe()
interped_df <- data.frame(lapply(df, function(x){
if(is.numeric(x)){
# Store a scalar of min row where x isn't NA: -> min_non_na
min_non_na <- min(which(!(is.na(x))))
# Store a scalar of max row where x isn't NA: -> max_non_na
max_non_na <- max(which(!(is.na(x))))
# Store scalar of the number of rows needed to impute prior
# to first NA value: -> ru_lower
ru_lower <- ifelse(min_non_na > 1, min_non_na - 1, min_non_na)
# Store scalar of the number of rows needed to impute after
# the last non-NA value: -> ru_lower
ru_upper <- ifelse(max_non_na == length(x),
length(x) - 1,
(length(x) - (max_non_na + 1)))
# Store a vector of the ramp to function: -> l_ramp_up:
ramp_up <- as.numeric(
cumsum(rep(x[min_non_na]/(min_non_na), ru_lower))
)
# Apply the interpolation function on vector "x": -> y
y <- as.numeric(l_interp_vec(as.numeric(x[min_non_na:max_non_na])))
# Create a vector that combines the ramp_up vector
# and y if the first NA is at row 1: -> z
if(length(ramp_up) > 1 & max_non_na != length(x)){
# Create a vector interpolations if there are
# multiple NA values after the last value: -> lower_l_int
lower_l_int <- as.numeric(cumsum(rep(mean(diff(c(ramp_up, y))),
ru_upper+1)) +
as.numeric(x[max_non_na]))
# Store the linear interpolations in a vector: -> z
z <- as.numeric(c(ramp_up, y, lower_l_int))
}else if(length(ramp_up) > 1 & max_non_na == length(x)){
# Store the linear interpolations in a vector: -> z
z <- as.numeric(c(ramp_up, y))
}else if(min_non_na == 1 & max_non_na != length(x)){
# Create a vector interpolations if there are
# multiple NA values after the last value: -> lower_l_int
lower_l_int <- as.numeric(cumsum(rep(mean(diff(c(ramp_up, y))),
ru_upper+1)) +
as.numeric(x[max_non_na]))
# Store the linear interpolations in a vector: -> z
z <- as.numeric(c(y, lower_l_int))
}else{
# Store the linear interpolations in a vector: -> z
z <- as.numeric(y)
}
# Interpolate between points in x, return new x:
return(as.numeric(ifelse(is.na(x), z, x)))
}else{
x
}
}
)
)
# Subset interped df to only contain
# the time values in C, store a data frame: -> int_df_subset
int_df_subset <- interped_df[interped_df$time %in% C$time,]