CODEWITHSUNDEEP
rcounttime-seriesaggregateplyr
May6
May6

Reputation: 47

R count days of exceedance per year

My aim is to count days of exceedance per year for each column of a dataframe. I want to do this with one fixed value for the whole dataframe, as well as with different values for each column. For one fixed value for the whole dataframe, I found a solution using with and another solution using the package with and . But I couldn't figure out how to do this with different values for each column.

Approach for one fixed value:

# create example data
date <- seq(as.Date("1961/1/1"), as.Date("1963/12/31"), "days") # create dates
date <- date[(format.Date(as.Date(date), "%m %d") !="02 29")]   # delete leap days 
TempX <- rep(airquality$Temp, length.out=length(date))
TempY <- rep(rev(airquality$Temp), length.out=length(date))
df <- data.frame(date, TempX, TempY)

# This approachs works fine for specific values using aggregate.
library(plyr)
dyear <- as.numeric(format(df$date, "%Y"))                      # year vector
fa80 <- function (fT) {cft <- count(fT>=80); return(cft[2,2])}; # function for counting days of exceedance
aggregate(df[,-1], list(year=dyear), fa80)                      # use aggregate to apply function to dataframe

# Another approach using ddply with colwise, which works fine for one specific value.
fd80 <- function (fT) {cft <- count(fT>=80); cft[2,2]}; # function to count days of exceedance
ddply(cbind(df[,-1], dyear), .(dyear), colwise(fd80))   # use ddply to apply function colwise to dataframe

In order to use specific values for each column separatly, I tried passing a second argument to the function, but this didn't work.

# pass second argument to function
Oc <- c(80,85)  # values 
fo80 <- function (fT,fR) {cft <- count(fT>=fR); return(cft[2,2])}; # function for counting days of exceedance
aggregate(df[,-1], list(year=dyear), fo80, fR=Oc)                  # use aggregate to apply function to dataframe

I tried using , but it didn't work with . I want to avoid using a , as it is slowly and I have a lot of dataframes with > 100 columns and long timeseries to process.

Furthermore the approach has to work for of the dataframe as well.

# subset of dataframe
dfmay <- df[(format.Date(as.Date(df$date),"%m")=="05"),]         # subset dataframe - only may
dyearmay <- as.numeric(format(dfmay$date, "%Y"))                 # year vector
aggregate(dfmay[,-1],list(year=dyearmay),fa80)                   # use aggregate to apply function to dataframe

I am out of ideas, how to solve this problem. Any help will be appreciated.

Upvotes: 3

Views: 648

Answers (1)

NicE
NicE

Reputation: 21435

You could try something like this:

#set the target temperature for each column
targets<-c(80,80)
dyear <- as.numeric(format(df$date, "%Y"))

#for each row of the data, check if the temp is above the target limit
#this will return a matrix of TRUE/FALSE          
exceedance<-t(apply(df[,-1],1,function(x){x>=targets}))

#aggregate by year and sum
aggregate(exceedance,list(year=dyear),sum)

Upvotes: 1

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