I have a data.frame composed of observations and modelled predictions of data. A minimal example dataset could look like this:
myData <- data.frame(tree=c(rep("A", 20)), doy=c(seq(75, 94)), count=c(NA,NA,NA,NA,0,NA,NA,NA,NA,1,NA,NA,NA,NA,2,NA,NA,NA,NA,NA), pred=c(0,0,0,0,1,1,1,2,2,2,2,3,3,3,3,6,9,12,20,44))
The count column represents when observations were made and predictions are modelled over a complete set of days, in effect interpolating the data to a day level (from every 5 days).
I would like to conditionally filter this dataset so that I end up truncating the predictions to the same range as the observations, in effect keeping all predictions between when count starts and ends (i.e. removing preceding and trailing rows/values of pred when they correspond to an NA in the count column). For this example, the ideal outcome would be:
tree doy count pred 5 A 79 0 1 6 A 80 NA 1 7 A 81 NA 1 8 A 82 NA 2 9 A 83 NA 2 10 A 84 1 2 11 A 85 NA 2 12 A 86 NA 3 13 A 87 NA 3 14 A 88 NA 3 15 A 89 2 3
I have tried to solve this problem through combining
last, thinking about using a conditional
mutate to create a column that determines if there is an observation in the previous doy (probably using
lag) and filling that with 1 or 0 and using that output to then filter, or even creating a second data.frame that contains the proper doy range that can be joined to this data.
In my searches on StackOverflow I have come across the following questions that seemed close, but were not quite what I needed:
My actual dataset is much larger with multiple trees over multiple years (with each tree/year having different period of observation depending on elevation of the sites, etc.). I am currently implementing the
dplyr package across my code, so an answer within that framework would be great but would be happy with any solutions at all.