/
6-degree_days.R
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6-degree_days.R
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######################################################################################################################
#--------------------------------------------------------------------------------------------------------------------
# Author : A. John Woodill
# Date : 08/16/2016
# Filename : degree_days.R
# Code : Days in each degree during a day
# (1) Get days in each degree
#
#--------------------------------------------------------------------------------------------------------------------
######################################################################################################################
library(dplyr)
# C to F conversion
fartocel <- function(x) (x-32) * 5/9
bound_list <- c(0:35)
comp <- function(x){
df <- readRDS(paste0("/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/Base/tmax_tmin/fips/", x))
# Turn to celcius
df$tmax <- fartocel(df$tmax)
df$tmin <- fartocel(df$tmin)
# Generate average temp
df$tavg <- (df$tmax + df$tmin)/2
# Generate new columns for degree days from bould_list
nc <- ncol(df)
for (i in 1:length(bound_list)){
col <- paste0("dday", bound_list[i], "C")
cadd <- i + nc
b <- bound_list[i]
df[,cadd] <- 0
df[,cadd] <-ifelse(b <= df$tmin, df$tavg - b, 0)
temp <- acos((2*b - df$tmax - df$tmin)/(df$tmax - df$tmin))
df[,cadd] <- ifelse(df$tmin < b & b < df$tmax, ((df$tavg - b)*temp + (df$tmax - df$tmin)*sin(temp)/2)/pi, df[,cadd])
df[,cadd] <- round(df[,cadd], 5)
colnames(df)[cadd] <- col
}
# Sum over each grid days
df <- df %>%
group_by(gridNumber, fips, year, month) %>%
summarize(dday0C = sum(dday0C),
dday1C = sum(dday1C),
dday2C = sum(dday2C),
dday3C = sum(dday3C),
dday4C = sum(dday4C),
dday5C = sum(dday5C),
dday6C = sum(dday6C),
dday7C = sum(dday7C),
dday8C = sum(dday8C),
dday9C = sum(dday9C),
dday10C = sum(dday10C),
dday11C = sum(dday11C),
dday12C = sum(dday12C),
dday13C = sum(dday13C),
dday14C = sum(dday14C),
dday15C = sum(dday15C),
dday16C = sum(dday16C),
dday17C = sum(dday17C),
dday18C = sum(dday18C),
dday19C = sum(dday19C),
dday20C = sum(dday20C),
dday21C = sum(dday21C),
dday22C = sum(dday22C),
dday23C = sum(dday23C),
dday24C = sum(dday24C),
dday25C = sum(dday25C),
dday26C = sum(dday26C),
dday27C = sum(dday27C),
dday28C = sum(dday28C),
dday29C = sum(dday29C),
dday30C = sum(dday30C),
dday31C = sum(dday31C),
dday32C = sum(dday32C),
dday33C = sum(dday33C),
dday34C = sum(dday34C),
dday35C = sum(dday35C),
tmin = mean(tmin),
tmax = mean(tmax),
tavg = mean(tavg))
# Average over each fips
df <- df %>%
group_by(fips, year, month) %>%
summarize(dday0C = mean(dday0C),
dday1C = mean(dday1C),
dday2C = mean(dday2C),
dday3C = mean(dday3C),
dday4C = mean(dday4C),
dday5C = mean(dday5C),
dday6C = mean(dday6C),
dday7C = mean(dday7C),
dday8C = mean(dday8C),
dday9C = mean(dday9C),
dday10C = mean(dday10C),
dday11C = mean(dday11C),
dday12C = mean(dday12C),
dday13C = mean(dday13C),
dday14C = mean(dday14C),
dday15C = mean(dday15C),
dday16C = mean(dday16C),
dday17C = mean(dday17C),
dday18C = mean(dday18C),
dday19C = mean(dday19C),
dday20C = mean(dday20C),
dday21C = mean(dday21C),
dday22C = mean(dday22C),
dday23C = mean(dday23C),
dday24C = mean(dday24C),
dday25C = mean(dday25C),
dday26C = mean(dday26C),
dday27C = mean(dday27C),
dday28C = mean(dday28C),
dday29C = mean(dday29C),
dday30C = mean(dday30C),
dday31C = mean(dday31C),
dday32C = mean(dday32C),
dday33C = mean(dday33C),
dday34C = mean(dday34C),
dday35C = mean(dday35C),
tmin = mean(tmin),
tmax = mean(tmax),
tavg = mean(tavg))
df <- filter(df, year >= 1900 & year <= 2012)
saveRDS(df, paste0("/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/Base/degreedays/fips/", x))
}
files <- list.files("/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/Base/tmax_tmin/fips/")
infiles <- list.files("/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/Base/degreedays/fips/")
i <- files
i <- setdiff(files, infiles)
library(parallel)
cl <- makeCluster(16)
clusterExport(cl, c("fartocel"))
clusterExport(cl, c("bound_list"))
clusterCall(cl, function() library(dplyr))
parLapply(cl, X = i, fun = comp)
stopCluster(cl)
files <- list.files("/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/Base/degreedays/fips/", full.names = TRUE)
library(data.table)
full_degree_days <- rbindlist(lapply(files, readRDS))
# Save fips
library(readr)
library(foreign)
saveRDS(full_degree_days, "/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/fips_degree_days_1900-2013.rds")
write_csv(full_degree_days, "/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/fips_degree_days_1900-2013.csv")
write.dta(full_degree_days, "/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/fips_degree_days_1900-2013.dta")
# For error checking .rds files
files <- list.files("/home/johnw/Projects/Fine-Scale-Weather-Interpolation/Data/Base/degreedays/fips/", full.names = TRUE)
test <- as.data.frame(files)
files <- files[1890:3105]
for (i in files){
df <- readRDS(i)
print(i)
}