Bigdata linear interpolation in R

Question

I've a big data set with some NA values. The sample data is below.

Data <-   data.frame(col_1 = c('A','A','A','A', 'A', 'A', 'A', 'B', 'B', 'B'), col_2 = c('C','C', 'C', 'D', 'D','D', 'D', 'E', 'E', 'E'), col_3 = c(10,15,20, 10,20,25,30,5,10,15), value = c(0.9, NA, 0.6, 0.9, NA, NA,0.4, 0.8,NA,0.4))

I want to fill those NA s with linear interpolation. For example to fill the NA for col_1 = 'A' and col_2 = 'C'

value = 0.9 + (0.6-0.9)*(15-10)/(20-10) =  0.75

And for the second NA for col_1 = 'A' and col_2 = 'D'

value = 0.9 + (0.4-0.9)*(25-10)/(30-10) =  0.53

Is there an efficient way of doing it since my data is big? Thank you. The expected outcome is.

Data_Updated <- data.frame(col_1 = c('A','A','A','A', 'A', 'A', 'A', 'B', 'B', 'B'), col_2 = c('C','C', 'C', 'D', 'D','D', 'D', 'E', 'E', 'E'), col_3 = c(10,15,20, 10,20,25,30,5,10,15), value = c(0.9, 0.75, 0.6, 0.9, 0.65, 0.53,0.4, 0.8,0.6,0.4))

Answer 1

Try if this is fast enough:

library(data.table)
library(zoo)

setDT(Data)
Data[, value1 := na.approx(value, x = col_3), by = .(col_1, col_2)]
#    col_1 col_2 col_3 value value1
# 1:     A     C    10   0.9  0.900
# 2:     A     C    15    NA  0.750
# 3:     A     C    20   0.6  0.600
# 4:     A     D    10   0.9  0.900
# 5:     A     D    20    NA  0.650
# 6:     A     D    25    NA  0.525
# 7:     A     D    30   0.4  0.400
# 8:     B     E     5   0.8  0.800
# 9:     B     E    10    NA  0.600
#10:     B     E    15   0.4  0.400

Answer 2

An option with dplyr

library(dplyr)
library(zoo)
Data %>%  
    group_by(col_1, col_2) %>%
     mutate(value1 = na.approx(value, x = col_3))