CODEWITHSUNDEEP
rinterpolationna
Kathiravan Meeran
Kathiravan Meeran

Reputation: 450

Filling NA in timeseries data with different interpolation techniques

Time = c("7/16/2017 18:46", "7/16/2017 21:52", 
"7/16/2017 23:16", "7/17/2017 4:03", "7/17/2017 5:13", "7/17/2017 5:27", 
"7/17/2017 18:57", "7/17/2017 19:25", "7/17/2017 23:58", "7/18/2017 2:59", 
"7/18/2017 3:27", "7/18/2017 3:59")  
Flux = c(NA, NA, 4.51263406, 
NA, NA, 2.291454049, NA, 4.568703192, NA, NA, 3.392520428, NA
), int = c(403.5413091, 421.5796345, NA, 410.0796897, NA, NA, 
363.5271212, NA, NA, 398.9564539, NA, NA)  
corr = c(422.745436, 
447.6726631, NA, 420.4392183, NA, NA, 408.7056493, NA, NA, 421.8799971, 
NA, NA)  
dat = c(NA, NA, NA, NA, 2.316481462, NA, NA, NA, 7.11779784, 
NA, NA, 2.953349661)

df$Time <- as.POSIXct(strptime(df$Timestamp, format="%m/%d/%Y %H:%M"))

which will look like...

Time    Flux    int corr    dat    
7/16/2017 18:46 NA  403.5413091 422.745436  NA    
7/16/2017 21:52 NA  421.5796345 447.6726631 NA   
7/16/2017 23:16 4.51263406  NA  NA  NA  
7/17/2017 4:03  NA  410.0796897 420.4392183 NA  
7/17/2017 5:13  NA  NA  NA  2.316481462  
7/17/2017 5:27  2.291454049 NA  NA  NA  
7/17/2017 18:57 NA  363.5271212 408.7056493 NA  
7/17/2017 19:25 4.568703192 NA  NA  NA  
7/17/2017 23:58 NA  NA  NA  7.11779784  
7/18/2017 2:59  NA  398.9564539 421.8799971 NA  
7/18/2017 3:27  3.392520428 NA  NA  NA  
7/18/2017 3:59  NA  NA  NA  2.953349661

I have four columns (1 time data, 3 continuous data). I have many NA values in each column. I want to interpolate and fill the NA for all columns. Since I dont know which interpolation method I need, I would like to many interpolation methods (linear, spline etc). I tried na.approx but it didnt work.

Any help?

Upvotes: 1

Views: 81

Answers (2)

Steffen Moritz
Steffen Moritz

Reputation: 7730

If you want to try and compare several interpolation methods as stated, you can use the na.interpolation() function from the imputeTS package.

For linear interpolation:

library("imputeTS")
na.interpolation(df, option = "linear")

For spline interpolation:

library("imputeTS")
na.interpolation(df, option = "spline")

For stineman interpolation:

library("imputeTS")
na.interpolation(df, option = "stine")

So as you can see, you just have to adapt the options parameter.

Upvotes: 1

Kathiravan Meeran
Kathiravan Meeran

Reputation: 450

df <- fill(df,direction = c (names(df)))

But i dont which technique it uses to fill the NA

Upvotes: 0

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