This is the “imputeTS” package for interpolating missing values in time series. It is easier to use R commands to manipulate missing values, but you may want to try this package first.
Package version is 3.3. Checked with R version 4.2.2.
Install Package
Run the following command.
#Install Package
install.packages("imputeTS")
Example
See the command and package help for details.
#Loading the library
library("imputeTS")
#Create Data
#Converting to time series data with the ts command
TestData <- ts(c(2,3,4,5,6,NA,7,8))
TestData
Time Series:
Start = 1
End = 8
Frequency = 1
[1] 2 3 4 5 6 NA 7 8
#Interpolate missing values in linear or in splines: na_interpolation command
#Specified by option
na_interpolation(TestData, option = "linear")
Time Series:
Start = 1
End = 8
Frequency = 1
[1] 2.0 3.0 4.0 5.0 6.0 6.5 7.0 8.0
#Interpolate missing values by the value
#before or after the missing value: na_locf command
#locf: interpolate by previous value,nocb: interpolate by backward value
na_locf(TestData, option = "locf")
Time Series:
Start = 1
End = 8
Frequency = 1
[1] 2 3 4 5 6 6 7 8
#Interpolate missing values by mean, median, mode: na_mean command
#option:mean,median,mode
na_mean(TestData, option = "mean")
Time Series:
Start = 1
End = 8
Frequency = 1
[1] 2 3 4 5 6 5 7 8
#Randomly interpolate missing values: na_random command
#upper_bound: lower bound, upper_bound: upper bound
na_random(TestData, lower_bound = 100, upper_bound = 1000)
Time Series:
Start = 1
End = 8
Frequency = 1
[1] 2.0000 3.0000 4.0000 5.0000 6.0000 911.4419 7.0000 8.0000
#Remove NA: na_remove command
na_remove(TestData)
Time Series:
Start = 1
End = 7
Frequency = 1
[1] 2 3 4 5 6 7 8
#Replace NA: na_replace command
na_replace(TestData, fill = 99999)
Time Series:
Start = 1
End = 8
Frequency = 1
[1] 2 3 4 5 6 99999 7 8
I hope this makes your analysis a little easier !!
