I have a dataframe with the European states where each state occurs 10 times (for 10 days). I want to interpolate the NA values of multiple columns, which I could achieve using
library("imputeTS")
na_interpolation(dataframe)
But I want to interpolate all NA values by state. How can that be done? I have already tried a lot of different solutions, but none did work for me.
As pseudo-code I would like to have something like
na_interpolation(dataframe, groupby=state)
Anything that could work?
These code samples did unfortunaetly not work for me
interpolation <- dataframe %>%
group_by(state-name) %>%
na_interpolation(dataframe)
You could use the split-apply-bind method:
do.call(rbind, lapply(split(dataframe, dataframe$state), na_interpolation))
As a worked example, take the following dummy data:
set.seed(3)
dataframe <- data.frame(state = rep(c("A", "B", "C"), each = 5),
value = rnorm(15))
dataframe$value[sample(15, 4)] <- NA
dataframe
#> state value
#> 1 A -0.96193342
#> 2 A NA
#> 3 A 0.25878822
#> 4 A -1.15213189
#> 5 A 0.19578283
#> 6 B 0.03012394
#> 7 B 0.08541773
#> 8 B NA
#> 9 B NA
#> 10 B 1.26736872
#> 11 C -0.74478160
#> 12 C NA
#> 13 C -0.71635849
#> 14 C 0.25265237
#> 15 C 0.15204571
Then we can do:
library(imputeTS)
do.call(rbind, lapply(split(dataframe, dataframe$state), na_interpolation))
#> state value
#> A.1 A -0.96193342
#> A.2 A -0.35157260
#> A.3 A 0.25878822
#> A.4 A -1.15213189
#> A.5 A 0.19578283
#> B.6 B 0.03012394
#> B.7 B 0.08541773
#> B.8 B 0.47940140
#> B.9 B 0.87338506
#> B.10 B 1.26736872
#> C.11 C -0.74478160
#> C.12 C -0.73057004
#> C.13 C -0.71635849
#> C.14 C 0.25265237
#> C.15 C 0.15204571
Created on 2020-12-12 by the reprex package (v0.3.0)