I have a large hourly time series data set showing temperatures at different times. There were a number of missing values (NA) in the series so I used linear interpolation to impute the missing values using the imputeTS package. Before the interpolation I was told to create a column for the imputed values as a zoo object. This replaced any NA temperatures with imputed ones.
I am doing heating degree day analysis which is the heating required to heat a building to room temperature. If the outside temperature is below 15.5 degrees then heating is required. I am looking to ignore (or set to NA) values above 15.5 and only focus on the temperatures below. I then would like to calculate the heating degree days which would be (15.5-Temp)*1/24 (24 hours in a day). This is usually simple however I am having trouble with the zoo object. Can anyone help??
An example of the data is:
DateTimes <- as.POSIXct(c("2009-01-01 00:00:00", "2009-01-01 01:00:00", "2009-01-01 02:00:00", "2009-01-01 03:00:00", "2009-01-01 04:00:00", "2009-01-01 05:00:00", "2009-01-01 06:00:00"))
MeanTemp <- c(0.8, 0.7, 0.7, NA, 0.8, 0.9, 1.1)
HourTemp <- data.frame(DateTimes, MeanTemp)
These are my imputation steps:
#Use linear interpolation to impute missing values
TempImp <- zoo(HourTemp$MeanTemp, HourTemp$DateTimes)
TempImp <- imputeTS::na.interpolation(TempImp, option = "linear")
#Add imputed values to data
as.data.frame(HourTemp)
HourTemp$airTempImp <- round(TempImp,1)
#Add imputed flag
HourTemp$Imputed <- ifelse(is.na(HourTemp$MeanTemp), "Imputed", "Observed")
HourTemp
The imputations worked successfully, replacing NA values with estimates but I cannot manipulate the zoo object 'airTempImp' to create a heating degree days column as specified in the opening paragraph.
I have tried using ifelse, ifelse.zoo, transform but none seem to be working!
Thanks!
It sounds like you haven't converted the zoo object to a more generic R object (but you haven't given an error message or code that produces it, so I can't be 100% sure).
In that case, you can use the as.vector function (see https://www.rdocumentation.org/packages/zoo/versions/1.8-6/topics/as.zoo), to convert a zoo object into a vector, which you can add to a data.frame.
The example code below removes imputeTS, like what G. Grothendieck says in his comment, since zoo's na.approx does linear interpolation.
# install.packages("zoo")
library("zoo")
DateTimes <- as.POSIXct(c(
"2009-01-01 00:00:00", "2009-01-01 01:00:00",
"2009-01-01 02:00:00", "2009-01-01 03:00:00",
"2009-01-01 04:00:00", "2009-01-01 05:00:00", "2009-01-01 06:00:00"))
MeanTemp <- c(0.8, 0.7, 0.7, NA, 0.8, 0.9, 1.1)
HourTemp <- data.frame(DateTimes, MeanTemp)
TempImp <- zoo(HourTemp$MeanTemp, HourTemp$DateTimes)
# use zoo's linear interpolation
HourTemp$airTempImp <- as.vector(na.approx(TempImp))
HourTemp$Imputed <- ifelse(is.na(HourTemp$MeanTemp), "Imputed", "Observed")
# calculates the heating degree day per hour if temp > 15.5,
# else sets to 0 (no heating)
HourTemp$HeatingDegreeDay <- ifelse(
HourTemp$airTempImp > 15.5,
0, # no heating
(15.5 - HourTemp$airTempImp) / 24
)
which will output:
HourTemp
DateTimes MeanTemp airTempImp Imputed HeatingDegreeDay
1 2009-01-01 00:00:00 0.8 0.80 Observed 0.6125000
2 2009-01-01 01:00:00 0.7 0.70 Observed 0.6166667
3 2009-01-01 02:00:00 0.7 0.70 Observed 0.6166667
4 2009-01-01 03:00:00 NA 0.75 Imputed 0.6145833
5 2009-01-01 04:00:00 0.8 0.80 Observed 0.6125000
6 2009-01-01 05:00:00 0.9 0.90 Observed 0.6083333
7 2009-01-01 06:00:00 1.1 1.10 Observed 0.6000000