我正在尝试运行一个我写过的函数但是我意识到它只需要太长时间。任何人都可以告诉我在以下函数中删除i for循环的最佳方法。该功能旨在读取大量现场天气数据，然后找出特定日期超过该月平均极端天气的天气。
我试图使用ifelse但是失败了。
Extremes <- function (siteno){
for (j in siteno){
# reads in the weather data for the specipied site
Butterfly_data <- read.csv(paste("~/Project Data/Site Subsets/SITE", j, ".csv", sep = ""))
Precip <- read.csv(paste("//ueahome5/ressci5/zuw13bqu/data/NTProfile/Desktop/EOBS European data/SiteWeather/rr/Weather.Site.", j, ".csv", sep = ""))
Tmin <- read.csv(paste("//ueahome5/ressci5/zuw13bqu/data/NTProfile/Desktop/EOBS European data/SiteWeather/tn/Weather.Site.", j, ".csv", sep = ""))
Tmax <- read.csv(paste("//ueahome5/ressci5/zuw13bqu/data/NTProfile/Desktop/EOBS European data/SiteWeather/tx/Weather.Site.", j, ".csv", sep = ""))
Precip[Precip[,1] < -900,1] <- NA
Tmax[Tmax[,1] < -90, 1] <- NA
Tmin[Tmin[,1] < -90, 1] <- NA
z <- merge(Precip, Tmax, by.x = "Date", by.y = "Date") # merges the weather data into one dataframe
SiteWeather <- merge(z, Tmin, by.x = "Date", by.y = "Date" )
SiteWeather[,1] <- as.Date(SiteWeather[,1], "%d/%m/%Y")
SiteWeather[,5] <- as.numeric(format(SiteWeather[,1], "%m")) # takes out the month value from the Date so as to be able to assess means by month and create seasonal means throughout the dataset
JanWeather<- subset(SiteWeather, SiteWeather[,5] == 1)#split the weather into months
FebWeather<- subset(SiteWeather, SiteWeather[,5] == 2)
MarWeather<- subset(SiteWeather, SiteWeather[,5] == 3)
AprWeather<- subset(SiteWeather, SiteWeather[,5] == 4)
MayWeather<- subset(SiteWeather, SiteWeather[,5] == 5)
JunWeather<- subset(SiteWeather, SiteWeather[,5] == 6)
JulWeather<- subset(SiteWeather, SiteWeather[,5] == 7)
AugWeather<- subset(SiteWeather, SiteWeather[,5] == 8)
SepWeather<- subset(SiteWeather, SiteWeather[,5] == 9)
OctWeather<- subset(SiteWeather, SiteWeather[,5] == 10)
NovWeather<- subset(SiteWeather, SiteWeather[,5] == 11)
DecWeather<- subset(SiteWeather, SiteWeather[,5] == 12)
JanExtPrecip <- mean(JanWeather[,2], na.rm = TRUE) + sd(JanWeather[,2], na.rm = TRUE)
JanExtTmax <- mean(JanWeather[,3], na.rm = TRUE) + sd(JanWeather[,3], na.rm = TRUE)
JanExtTmin <- mean(JanWeather[,4], na.rm = TRUE) - sd(JanWeather[,4], na.rm = TRUE)
FebExtPrecip <- mean(FebWeather[,2], na.rm = TRUE) + sd(FebWeather[,2], na.rm = TRUE)
FebExtTmax <- mean(FebWeather[,3], na.rm = TRUE) + sd(FebWeather[,3], na.rm = TRUE)
FebExtTmin <- mean(FebWeather[,4], na.rm = TRUE) - sd(FebWeather[,4], na.rm = TRUE)
MarExtPrecip <- mean(MarWeather[,2], na.rm = TRUE) + sd(MarWeather[,2], na.rm = TRUE)
MarExtTmax <- mean(MarWeather[,3], na.rm = TRUE) + sd(MarWeather[,3], na.rm = TRUE)
MarExtTmin <- mean(MarWeather[,4], na.rm = TRUE) - sd(MarWeather[,4], na.rm = TRUE)
AprExtPrecip <- mean(AprWeather[,2], na.rm = TRUE) + sd(AprWeather[,2], na.rm = TRUE)
AprExtTmax <- mean(AprWeather[,3], na.rm = TRUE) + sd(AprWeather[,3], na.rm = TRUE)
AprExtTmin <- mean(AprWeather[,4], na.rm = TRUE) - sd(AprWeather[,4], na.rm = TRUE)
MayExtPrecip <- mean(MayWeather[,2], na.rm = TRUE) + sd(MayWeather[,2], na.rm = TRUE)
MayExtTmax <- mean(MayWeather[,3], na.rm = TRUE) + sd(MayWeather[,3], na.rm = TRUE)
MayExtTmin <- mean(MayWeather[,4], na.rm = TRUE) - sd(MayWeather[,4], na.rm = TRUE)
JunExtPrecip <- mean(JunWeather[,2], na.rm = TRUE) + sd(JunWeather[,2], na.rm = TRUE)
JunExtTmax <- mean(JunWeather[,3], na.rm = TRUE) + sd(JunWeather[,3], na.rm = TRUE)
JunExtTmin <- mean(JunWeather[,4], na.rm = TRUE) - sd(JunWeather[,4], na.rm = TRUE)
JulExtPrecip <- mean(JulWeather[,2], na.rm = TRUE) + sd(JulWeather[,2], na.rm = TRUE)
JulExtTmax <- mean(JulWeather[,3], na.rm = TRUE) + sd(JulWeather[,3], na.rm = TRUE)
JulExtTmin <- mean(JulWeather[,4], na.rm = TRUE) - sd(JulWeather[,4], na.rm = TRUE)
AugExtPrecip <- mean(AugWeather[,2], na.rm = TRUE) + sd(AugWeather[,2], na.rm = TRUE)
AugExtTmax <- mean(AugWeather[,3], na.rm = TRUE) + sd(AugWeather[,3], na.rm = TRUE)
AugExtTmin <- mean(AugWeather[,4], na.rm = TRUE) - sd(AugWeather[,4], na.rm = TRUE)
SepExtPrecip <- mean(SepWeather[,2], na.rm = TRUE) + sd(SepWeather[,2], na.rm = TRUE)
SepExtTmax <- mean(SepWeather[,3], na.rm = TRUE) + sd(SepWeather[,3], na.rm = TRUE)
SepExtTmin <- mean(SepWeather[,4], na.rm = TRUE) - sd(SepWeather[,4], na.rm = TRUE)
OctExtPrecip <- mean(OctWeather[,2], na.rm = TRUE) + sd(OctWeather[,2], na.rm = TRUE)
OctExtTmax <- mean(OctWeather[,3], na.rm = TRUE) + sd(OctWeather[,3], na.rm = TRUE)
OctExtTmin <- mean(OctWeather[,4], na.rm = TRUE) - sd(OctWeather[,4], na.rm = TRUE)
NovExtPrecip <- mean(NovWeather[,2], na.rm = TRUE) + sd(NovWeather[,2], na.rm = TRUE)
NovExtTmax <- mean(NovWeather[,3], na.rm = TRUE) + sd(NovWeather[,3], na.rm = TRUE)
NovExtTmin <- mean(NovWeather[,4], na.rm = TRUE) - sd(NovWeather[,4], na.rm = TRUE)
DecExtPrecip <- mean(DecWeather[,2], na.rm = TRUE) + sd(DecWeather[,2], na.rm = TRUE)
DecExtTmax <- mean(DecWeather[,3], na.rm = TRUE) + sd(DecWeather[,3], na.rm = TRUE)
DecExtTmin <- mean(DecWeather[,4], na.rm = TRUE) - sd(DecWeather[,4], na.rm = TRUE)
for(i in 1:length(SiteWeather[,1])) {
if(SiteWeather[i,5]== 1 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= JanExtPrecip) #checks whether the daily value exceeds the daily extreme for the average extreme of each month
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= JanExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= JanExtTmin)
}
if(SiteWeather[i,5]== 2 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= FebExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= FebExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= FebExtTmin)
}
if(SiteWeather[i,5]== 3 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= MarExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= MarExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= MarExtTmin)
}
if(SiteWeather[i,5]== 4 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= AprExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= AprExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= AprExtTmin)
}
if(SiteWeather[i,5]== 5 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= MayExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= MayExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= MayExtTmin)
}
if(SiteWeather[i,5]== 6 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= JunExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= JunExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= JunExtTmin)
}
if(SiteWeather[i,5]== 7 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= JulExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= JulExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= JulExtTmin)
}
if(SiteWeather[i,5]== 8 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= AugExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= AugExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= AugExtTmin)
}
if(SiteWeather[i,5]== 9 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= SepExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= SepExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= SepExtTmin)
}
if(SiteWeather[i,5]== 10 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= OctExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= OctExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= OctExtTmin)
}
if(SiteWeather[i,5]== 11 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= NovExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= NovExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= NovExtTmin)
}
if(SiteWeather[i,5]== 12 ){
SiteWeather[i,6] <- 1*(SiteWeather[i,2] >= DecExtPrecip) #checks whether the daily value exceeds the daily extreme
SiteWeather[i,7] <- 1*(SiteWeather[i,3] >= DecExtTmax)
SiteWeather[i,8] <- 1*(SiteWeather[i,4] <= DecExtTmin)
}
print(i)
}
colnames(SiteWeather)<- c("Date", "Precip", "Tmax", "Tmin","Month" ,"ExtPrecip", "ExtTmax", "ExtTmin" )
SiteWeather<- SiteWeather[order(as.Date(SiteWeather[,1])),]
write.table(SiteWeather, paste("Site.", j, ".csv", sep = ",", row.names = FALSE))
print(j)
}
}
这是我尝试使用ifelse：
Extremes<- function (siteno){
for (j in siteno){
# reads in the weather data for the specipied site
Butterfly_data <- read.csv(paste("~/Project Data/Site Subsets/SITE", j, ".csv", sep = ""))
Precip <- read.csv(paste("//ueahome5/ressci5/zuw13bqu/data/NTProfile/Desktop/EOBS European data/SiteWeather/rr/Weather.Site.", j, ".csv", sep = ""))
Tmin <- read.csv(paste("//ueahome5/ressci5/zuw13bqu/data/NTProfile/Desktop/EOBS European data/SiteWeather/tn/Weather.Site.", j, ".csv", sep = ""))
Tmax <- read.csv(paste("//ueahome5/ressci5/zuw13bqu/data/NTProfile/Desktop/EOBS European data/SiteWeather/tx/Weather.Site.", j, ".csv", sep = ""))
Precip[Precip[,1] < -900,1]<- NA
Tmax[Tmax[,1] < -90, 1]<- NA
Tmin[Tmin[,1] < -90, 1]<- NA
z<- merge(Precip, Tmax, by.x = "Date", by.y = "Date") # merges the weather data into one dataframe
SiteWeather <- merge(z, Tmin, by.x = "Date", by.y = "Date" )
SiteWeather[,1] <- as.Date(SiteWeather[,1], "%d/%m/%Y")
SiteWeather[,5] <- as.numeric(format(SiteWeather[,1], "%m")) # takes out the month value from the Date so as to be able to assess means by month and create seasonal means throughout the dataset
JanWeather<- subset(SiteWeather, SiteWeather[,5] == 1)#split the weather into months
FebWeather<- subset(SiteWeather, SiteWeather[,5] == 2)
MarWeather<- subset(SiteWeather, SiteWeather[,5] == 3)
AprWeather<- subset(SiteWeather, SiteWeather[,5] == 4)
MayWeather<- subset(SiteWeather, SiteWeather[,5] == 5)
JunWeather<- subset(SiteWeather, SiteWeather[,5] == 6)
JulWeather<- subset(SiteWeather, SiteWeather[,5] == 7)
AugWeather<- subset(SiteWeather, SiteWeather[,5] == 8)
SepWeather<- subset(SiteWeather, SiteWeather[,5] == 9)
OctWeather<- subset(SiteWeather, SiteWeather[,5] == 10)
NovWeather<- subset(SiteWeather, SiteWeather[,5] == 11)
DecWeather<- subset(SiteWeather, SiteWeather[,5] == 12)
JanExtPrecip <- mean(JanWeather[,2], na.rm = TRUE) + sd(JanWeather[,2], na.rm = TRUE)
JanExtTmax <- mean(JanWeather[,3], na.rm = TRUE) + sd(JanWeather[,3], na.rm = TRUE)
JanExtTmin <- mean(JanWeather[,4], na.rm = TRUE) - sd(JanWeather[,4], na.rm = TRUE)
FebExtPrecip <- mean(FebWeather[,2], na.rm = TRUE) + sd(FebWeather[,2], na.rm = TRUE)
FebExtTmax <- mean(FebWeather[,3], na.rm = TRUE) + sd(FebWeather[,3], na.rm = TRUE)
FebExtTmin <- mean(FebWeather[,4], na.rm = TRUE) - sd(FebWeather[,4], na.rm = TRUE)
MarExtPrecip <- mean(MarWeather[,2], na.rm = TRUE) + sd(MarWeather[,2], na.rm = TRUE)
MarExtTmax <- mean(MarWeather[,3], na.rm = TRUE) + sd(MarWeather[,3], na.rm = TRUE)
MarExtTmin <- mean(MarWeather[,4], na.rm = TRUE) - sd(MarWeather[,4], na.rm = TRUE)
AprExtPrecip <- mean(AprWeather[,2], na.rm = TRUE) + sd(AprWeather[,2], na.rm = TRUE)
AprExtTmax <- mean(AprWeather[,3], na.rm = TRUE) + sd(AprWeather[,3], na.rm = TRUE)
AprExtTmin <- mean(AprWeather[,4], na.rm = TRUE) - sd(AprWeather[,4], na.rm = TRUE)
MayExtPrecip <- mean(MayWeather[,2], na.rm = TRUE) + sd(MayWeather[,2], na.rm = TRUE)
MayExtTmax <- mean(MayWeather[,3], na.rm = TRUE) + sd(MayWeather[,3], na.rm = TRUE)
MayExtTmin <- mean(MayWeather[,4], na.rm = TRUE) - sd(MayWeather[,4], na.rm = TRUE)
JunExtPrecip <- mean(JunWeather[,2], na.rm = TRUE) + sd(JunWeather[,2], na.rm = TRUE)
JunExtTmax <- mean(JunWeather[,3], na.rm = TRUE) + sd(JunWeather[,3], na.rm = TRUE)
JunExtTmin <- mean(JunWeather[,4], na.rm = TRUE) - sd(JunWeather[,4], na.rm = TRUE)
JulExtPrecip <- mean(JulWeather[,2], na.rm = TRUE) + sd(JulWeather[,2], na.rm = TRUE)
JulExtTmax <- mean(JulWeather[,3], na.rm = TRUE) + sd(JulWeather[,3], na.rm = TRUE)
JulExtTmin <- mean(JulWeather[,4], na.rm = TRUE) - sd(JulWeather[,4], na.rm = TRUE)
AugExtPrecip <- mean(AugWeather[,2], na.rm = TRUE) + sd(AugWeather[,2], na.rm = TRUE)
AugExtTmax <- mean(AugWeather[,3], na.rm = TRUE) + sd(AugWeather[,3], na.rm = TRUE)
AugExtTmin <- mean(AugWeather[,4], na.rm = TRUE) - sd(AugWeather[,4], na.rm = TRUE)
SepExtPrecip <- mean(SepWeather[,2], na.rm = TRUE) + sd(SepWeather[,2], na.rm = TRUE)
SepExtTmax <- mean(SepWeather[,3], na.rm = TRUE) + sd(SepWeather[,3], na.rm = TRUE)
SepExtTmin <- mean(SepWeather[,4], na.rm = TRUE) - sd(SepWeather[,4], na.rm = TRUE)
OctExtPrecip <- mean(OctWeather[,2], na.rm = TRUE) + sd(OctWeather[,2], na.rm = TRUE)
OctExtTmax <- mean(OctWeather[,3], na.rm = TRUE) + sd(OctWeather[,3], na.rm = TRUE)
OctExtTmin <- mean(OctWeather[,4], na.rm = TRUE) - sd(OctWeather[,4], na.rm = TRUE)
NovExtPrecip <- mean(NovWeather[,2], na.rm = TRUE) + sd(NovWeather[,2], na.rm = TRUE)
NovExtTmax <- mean(NovWeather[,3], na.rm = TRUE) + sd(NovWeather[,3], na.rm = TRUE)
NovExtTmin <- mean(NovWeather[,4], na.rm = TRUE) - sd(NovWeather[,4], na.rm = TRUE)
DecExtPrecip <- mean(DecWeather[,2], na.rm = TRUE) + sd(DecWeather[,2], na.rm = TRUE)
DecExtTmax <- mean(DecWeather[,3], na.rm = TRUE) + sd(DecWeather[,3], na.rm = TRUE)
DecExtTmin <- mean(DecWeather[,4], na.rm = TRUE) - sd(DecWeather[,4], na.rm = TRUE)
#checks whether the daily value exceeds the daily extreme for each month
ifelse(SiteWeather[,5]== 1 & SiteWeather[,2] >= JanExtPrecip,SiteWeather[,6] <- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 1 & SiteWeather[,3] >= JanExtTmax,SiteWeather[,7] <- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 1 & SiteWeather[,4] <= JanExtTmin,SiteWeather[,8] <- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 2 & SiteWeather[,2] >= FebExtPrecip,SiteWeather[,6] <- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 2 & SiteWeather[,3] >= FebExtTmax,SiteWeather[,7] <- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 2 & SiteWeather[,4] <= FebExtTmin,SiteWeather[,8] <- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 3 & SiteWeather[,2] >= MarExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 3 & SiteWeather[,3] >= MarExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 3 & SiteWeather[,4] <= MarExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 4 & SiteWeather[,2] >= AprExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 4 & SiteWeather[,3] >= AprExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 4 & SiteWeather[,4] <= AprExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 5 & SiteWeather[,2] >= MayExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 5 & SiteWeather[,3] >= MayExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 5 & SiteWeather[,4] <= MayExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 6 & SiteWeather[,2] >= JunExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 6 & SiteWeather[,3] >= JunExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 6 & SiteWeather[,4] <= JunExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 7 & SiteWeather[,2] >= JulExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 7 & SiteWeather[,3] >= JulExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 7 & SiteWeather[,4] <= JulExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 8 & SiteWeather[,2] >= AugExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 8 & SiteWeather[,3] >= AugExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 8 & SiteWeather[,4] <= AugExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 9 & SiteWeather[,2] >= SepExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 9 & SiteWeather[,3] >= SepExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 9 & SiteWeather[,4] <= SepExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 10 & SiteWeather[,2] >= OctExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 10 & SiteWeather[,3] >= OctExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 10 & SiteWeather[,4] <= OctExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 11 & SiteWeather[,2] >= NovExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 11 & SiteWeather[,3] >= NovExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 11 & SiteWeather[,4] <= NovExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
ifelse(SiteWeather[,5]== 12 & SiteWeather[,2] >= DecExtPrecip,SiteWeather[,6]<- 1, SiteWeather[,6]<- 0)
ifelse(SiteWeather[,5]== 12 & SiteWeather[,3] >= DecExtTmax,SiteWeather[,7]<- 1, SiteWeather[,7]<- 0)
ifelse(SiteWeather[,5]== 12 & SiteWeather[,4] <= DecExtTmin,SiteWeather[,8]<- 1, SiteWeather[,8]<- 0)
colnames(SiteWeather)<- c("Date", "Precip", "Tmax", "Tmin","Month" ,"ExtPrecip", "ExtTmax", "ExtTmin" )
SiteWeather<- SiteWeather[order(as.Date(SiteWeather[,1])),]
write.csv(SiteWeather, paste("Site.", j, ".csv", sep = ""), sep = ",", row.names = FALSE)
}
print(j)
}
只是想加快我的功能，因为有很多网站数据需要阅读。