Linear interpolation among columns in r - r

Question

I am working with some temperature data where I have temperatures at certain depths e.g. 0.9m, 2.5m and 5m. I would like to interpolate this values so I obtain the temperature each meter, e.g. 1m, 2m and 3m. The original data looks like this:
df
# A tibble: 5 x 3
date d_0.9 d_2.5
<dttm> <dbl> <dbl>
1 2004-01-05 03:00:00 7 8
2 2004-01-05 04:00:00 7.5 9
3 2004-01-05 05:00:00 7 8
4 2004-01-05 06:00:00 6.92 NA
What I would like to get is something like :
df_int
# A tibble: 5 x 5
date d_0.9 d_1 d_2 d_2.5
<dttm> <dbl> <dbl> <dbl> <dbl>
1 2004-01-05 03:00:00 7 7.0625 7.6875 8
2 2004-01-05 04:00:00 7.5 7.59375 8.53125 9
3 2004-01-05 05:00:00 7 7.0625 7.6875 8
4 2004-01-05 06:00:00 6.92 NA NA NA
I have to do this for a very large data frame. Is there an efficient way of doing it?
Many thanks in advance

Answer 1

One option is to convert the data to long format, use a join to add rows for the depths we want to interpolate at, and then use approx for the interpolation:
library(tidyverse)
# Data
df = tibble(date=seq(as.POSIXct("2004-01-05 03:00:00"),
as.POSIXct("2004-01-05 06:00:00"),
by="1 hour"),
d_0.9 = c(7,7.5,7,6.92),
d_2.5 = c(8,NA,8,NA),
d_5.0 = c(10,10.5,9.4,NA))
# Create a data frame with all of the times and depths we want to interpolate at
depths = sort(unique(c(c(0.9, 2.5, 5), seq(ceiling(0.9), floor(5), 1))))
depths = crossing(date=unique(df$date), depth = depths)
# Convert data to long format, join to add interpolation depths, then interpolate
df.interp = df %>%
gather(depth, value, -date) %>%
mutate(depth = as.numeric(gsub("d_", "", depth))) %>%
full_join(depths) %>%
arrange(date, depth) %>%
group_by(date) %>%
mutate(value.interp = if(length(na.omit(value)) > 1) {
approx(depth, value, xout=depth)$y
} else {
value
})
In the code above, the if statement is inclduded to prevent approx throwing an error when a given date has only one non-missing value.
df.interp
date depth value value.interp
1 2004-01-05 03:00:00 0.9 7.00 7.000000
2 2004-01-05 03:00:00 1.0 NA 7.062500
3 2004-01-05 03:00:00 2.0 NA 7.687500
4 2004-01-05 03:00:00 2.5 8.00 8.000000
5 2004-01-05 03:00:00 3.0 NA 8.400000
6 2004-01-05 03:00:00 4.0 NA 9.200000
7 2004-01-05 03:00:00 5.0 10.00 10.000000
8 2004-01-05 04:00:00 0.9 7.50 7.500000
9 2004-01-05 04:00:00 1.0 NA 7.573171
10 2004-01-05 04:00:00 2.0 NA 8.304878
11 2004-01-05 04:00:00 2.5 NA 8.670732
12 2004-01-05 04:00:00 3.0 NA 9.036585
13 2004-01-05 04:00:00 4.0 NA 9.768293
14 2004-01-05 04:00:00 5.0 10.50 10.500000
15 2004-01-05 05:00:00 0.9 7.00 7.000000
16 2004-01-05 05:00:00 1.0 NA 7.062500
17 2004-01-05 05:00:00 2.0 NA 7.687500
18 2004-01-05 05:00:00 2.5 8.00 8.000000
19 2004-01-05 05:00:00 3.0 NA 8.280000
20 2004-01-05 05:00:00 4.0 NA 8.840000
21 2004-01-05 05:00:00 5.0 9.40 9.400000
22 2004-01-05 06:00:00 0.9 6.92 6.920000
23 2004-01-05 06:00:00 1.0 NA NA
24 2004-01-05 06:00:00 2.0 NA NA
25 2004-01-05 06:00:00 2.5 NA NA
26 2004-01-05 06:00:00 3.0 NA NA
27 2004-01-05 06:00:00 4.0 NA NA
28 2004-01-05 06:00:00 5.0 NA NA