How to replace NAs with the linear interpolation between known observations? [duplicate]

Question

I have the following data frame,

df <- data.frame(id = c("a", "a", "a", "a", "b", "b", "b", "b"),
        time = 1:4, value = c(100, NA, NA, 550, 300, NA, NA, 900))

Can someone suggest an approach for replacing the NA values in df by dividing the difference of the value column evenly over time? At time 1, A is 100 and at time 4 A is 550. How would one change the NAs in times 2 and 3 to 250 and 400? And then 500 and 700 for B at times 2 and 3?

I can write a complex for loop to brute force it, but is there a more efficient solution?

Answer 1

You could use na.approx from zoo

library(zoo)
df$value <- na.approx(df$value)
df
#  id time value
#1  a    1   100
#2  a    2   250
#3  a    3   400
#4  a    4   550
#5  b    1   300
#6  b    2   500
#7  b    3   700
#8  b    4   900