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hw_Summer_Annual_Mean_trends_anomaly_loop_over_files copy.R
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hw_Summer_Annual_Mean_trends_anomaly_loop_over_files copy.R
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################################################################################################################################################################
# creating dataframe with monthly aggregates: one extra year before the time period of the data added
# variables mapped to one year before the actual period it represents.
# e.g., 2001 Jan-feb was considered as part of 2001 summer starting from December 2000
library(plyr)
library(grid)
library(gridExtra)
library(zoo)
library(data.table)
# for loop to process all CSV files in a folder
# make a list of all files in a folder on terminal$ ls *.csv > filenames
# set working directory
setwd('/Volumes/Extended/INMET/allstations/')
filenames = read.table('filenames')
nsites=dim(filenames)[1]
n = 5
for (n in 1:nsites){
file_n = filenames[n,]
data_frame = read.csv(as.character(file_n))
####### start working on the loop here
yrmax <- max(data_frame[,4], na.rm = T)
yrmin <- min(data_frame[,4], na.rm = T)
yeartot <- yrmax - yrmin + 1
yrlist <- c(yrmin:yrmax)
names(data_frame)[1] <- "station"
names(data_frame)[2] <- "day"
names(data_frame)[3] <- "month"
names(data_frame)[4] <- "year"
names(data_frame)[5] <- "hour"
names(data_frame)[6] <- "prcp"
names(data_frame)[7] <- "tmax"
names(data_frame)[8] <- "tmin"
names(data_frame)[9] <- "insul" # not sure what this variable is
# calculate seasonal averages using library(plyr)
# additional info: http://stackoverflow.com/questions/15105670/how-to-calculate-average-values-large-datasets
# anual aggergates
Tx_annual <- ddply(data_frame, .(year), summarise, Tx_annual <- mean(tmax, na.rm = TRUE))
Tx_annual <- na.omit(Tx_annual)
names(Tx_annual)[2] <- "Tx_annual"
Tx_annual$Tx_annual_anom <- Tx_annual$Tx_annual - mean(Tx_annual$Tx_annual, na.rm = TRUE)
Tn_annual <- ddply(data_frame, .(year), summarise, Tn_annual <- mean(tmin, na.rm = TRUE))
Tn_annual <- na.omit(Tn_annual)
names(Tn_annual)[2] <- "Tn_annual"
Tn_annual$Tn_annual_anom <- Tn_annual$Tn_annual - mean(Tn_annual$Tn_annual, na.rm = TRUE)
# 90th percentile subsets: subset of dataframe with tmax and tmin > 90th percentile respectively
Tx90q <- quantile(data_frame[,7], 0.90, na.rm = TRUE) # 90th percentile tmax
Tn90q <- quantile(data_frame[,8], 0.90, na.rm = TRUE) # 90th percentile tmin
Tx90p <- data_frame[data_frame[,"tmax"] > Tx90q,] # dataframe with tmax > 90th percentile
Tn90p <- data_frame[data_frame[,"tmin"] > Tn90q,] # dataframe with tmax > 90th percentile
Tx90p_count <- ddply(Tx90p, .(year), summarise, Tx90p_count <- length(tmax))
Tx90p_count <- na.omit(Tx90p_count)
names(Tx90p_count)[2] <- "Tx90p_count"
Tn90p_count <- ddply(Tn90p, .(year), summarise, Tn90p_count <- length(tmin))
Tn90p_count <- na.omit(Tn90p_count)
names(Tn90p_count)[2] <- "Tn90p_count"
# using subset function to extract summer months: december, january and february
jf <- subset(data_frame, month < 3, select=c(day, month, year, tmax, tmin, prcp))
dec <- subset(data_frame, month > 11, select=c(day, month, year, tmax, tmin, prcp))
# summer averages
Tx_sum_jf <- ddply(jf, .(year), summarise, Tx_sum_jf <- mean(tmax, na.rm = TRUE))
Tx_sum_jf <- na.omit(Tx_sum_jf)
names(Tx_sum_jf)[2] <- "Tx_sum_jf"
Tx_sum_dec <- ddply(dec, .(year), summarise, Tx_sum_dec <- mean(tmax, na.rm = TRUE))
Tx_sum_dec <- na.omit(Tx_sum_dec)
names(Tx_sum_dec)[2] <- "Tx_sum_dec"
Tn_sum_jf <- ddply(jf, .(year), summarise, Tn_sum_jf <- mean(tmin, na.rm = TRUE))
Tn_sum_jf <- na.omit(Tn_sum_jf)
names(Tn_sum_jf)[2] <- "Tn_sum_jf"
Tn_sum_dec <- ddply(dec, .(year), summarise, Tn_sum_dec <- mean(tmin, na.rm = TRUE))
Tn_sum_dec <- na.omit(Tn_sum_dec)
names(Tn_sum_dec)[2] <- "Tn_sum_dec"
Tn_sum_jf[,1] <- Tn_sum_jf[,1]-1 # subtracts year length by one - shifting the values by one year down
Tx_sum_jf[,1] <- Tx_sum_jf[,1]-1 # subtracts year length by one - shifting the values by one year down
# merge dataframes with irregular timeseries using library(zoo)
# reference: http://stackoverflow.com/questions/7089444/r-merge-two-irregular-time-series
xx <- read.zoo(Tx_sum_dec)
yy <- read.zoo(Tx_sum_jf)
zz <- read.zoo(Tn_sum_dec)
aa <- read.zoo(Tn_sum_jf)
bb <- read.zoo(Tx_annual)
cc <- read.zoo(Tn_annual)
dd <- read.zoo(Tx90p_count)
ee <- read.zoo(Tn90p_count)
df <- merge.zoo(xx, yy, zz, aa, bb, cc, dd, ee)
df <- as.data.frame(df, row.names = NULL)
# converts zoo object to dataframe again
# the output dataframe uses year as row names. to extract row names as a variable vector
# use library(data.table) setDT function http://stackoverflow.com/questions/29511215/convert-row-names-into-first-column
# this adds an extra row at the end that needs deletion
df <- setDT(df, keep.rownames = TRUE)
df$Tx_sum <- rowMeans(subset(df, select = c(xx, yy)), na.rm = TRUE)
df$Tn_sum <- rowMeans(subset(df, select = c(zz, aa)), na.rm = TRUE)
df$xx <- NULL
df$yy <- NULL
df$zz <- NULL
df$aa <- NULL
df$rn <- as.numeric(df$rn)
df$Tx_sum_anom <- df$Tx_sum - mean(df$Tx_sum, na.rm = TRUE)
df$Tn_sum_anom <- df$Tn_sum - mean(df$Tn_sum, na.rm = TRUE)
tmax_sum <- mean(df$Tx_sum, na.rm = TRUE)
tmax_sum <- round(tmax_sum, 2)
tmax_ann <- mean(df$Tx, na.rm = TRUE)
tmax_ann <- round(tmax_ann, 2)
tmin_sum <- mean(df$Tn_sum, na.rm = TRUE)
tmin_sum <- round(tmin_sum, 2)
tmin_ann <- mean(df$Tn, na.rm = TRUE)
tmin_ann <- round(tmin_ann, 2)
summary(df)
# Tx_annual$Tx_annual_anom <- Tx_annual$Tx_annual - mean(Tx_annual$Tx_annual, na.rm = TRUE)
# df$Tn_anom <- df$ann_mean_tmin - mean(df$ann_mean_tmin, na.rm = TRUE)
names(df)[1] <- "year"
names(df)[2] <- "Tx"
names(df)[3] <- "Tx_anom"
names(df)[4] <- "Tn"
names(df)[5] <- "Tn_anom"
names(df)[6] <- "Tx90p_count"
names(df)[7] <- "Tn90p_count"
tstr <- substr(as.character(file_n), 1, nchar(as.character(file_n))-10)
df$station <- tstr
df$Tx90p_value <- Tx90q
df$Tn90p_value <- Tn90q
# write.table(df, file="output.csv", eol = "\r", na = "NA", row.names = FALSE, append = TRUE, sep=",", col.names = !file.exists("output.csv"))
df_plot <- df
df_plot$Tx <- NULL
df_plot$Tn <- NULL
df_plot$Tx90p_count <- NULL
df_plot$Tx90p_value <- NULL
df_plot$Tn90p_count <- NULL
df_plot$Tn90p_value <- NULL
df_plot$station <- NULL
df_plot$Tn_sum <- NULL
x1 <- max(max(df$Tx90p_count, na.rm = TRUE), max(df$Tn90p_count, na.rm = TRUE))
#txr2 <- summary(lm(df$Tx90p_count ~ df$year))$r.squared
#tnr2 <- summary(lm(df$Tn90p_count ~ df$year))$r.squared
txslope <- summary(lm(df$Tx90p_count ~ df$year))$coefficients[2,1]
tnslope <- summary(lm(df$Tn90p_count ~ df$year))$coefficients[2,1]
txslope <- round(txslope, 3)
tnslope <- round(tnslope, 3)
quartz()
png(filename = paste(tstr,"_anom.png"), width = 900, height= 500, res = 90)
plot(y=df_plot$Tx_sum_anom, x=df_plot$year, type = "l", col="red", xlab = "", xlim = c(yrmin,yrmax),ylab = expression("Temperature anomaly"~degree~C), main=NULL, ylim = c(-5, 5), cex.main = 0.8)
lines(y=df_plot$Tx_anom, x=df_plot$year, col="red", lty="dotted", lwd = 1.5)
lines(y=df_plot$Tn_sum_anom, x=df_plot$year, col="blue")
lines(y=df_plot$Tn_anom, x=df_plot$year, col="blue", lty="dotted", lwd = 1.5)
abline(h=0, col="grey")
abline(v = 2016, col = "gray90")
leg_txt <- c("maximum", "minimum")
col_code <- c("red", "blue")
legend(((min(df_plot$year))-0.5), -4, adj=0, legend = leg_txt, fill=col_code, horiz = TRUE, cex = 0.8, border="white", box.col = NULL, bty = "n")
text(((min(df_plot$year))+0.5), -5, adj=0, "solid lines = summer, dotted = annual", cex=0.8, col="grey30")
text((min(df_plot$year)), 4.9, tstr, cex=0.9, col="grey20", adj = 0)
text((min(df_plot$year)), 4.4, paste0(c("Tx annual mean : "), tmax_ann), cex=0.8, col="grey50", adj = 0)
text((min(df_plot$year)), 4.0, paste0(c("Tx summer mean : "), tmax_sum), cex=0.8, col="grey50", adj = 0)
text((min(df_plot$year)), 3.6, paste0(c("Tn annual mean : "), tmin_ann), cex=0.8, col="grey50", adj = 0)
text((min(df_plot$year)), 3.2, paste0(c("Tn summer mean : "), tmin_sum), cex=0.8, col="grey50", adj = 0)
dev.off()
quartz()
png(filename = paste(tstr,"_90p.png"), width = 900, height= 500, res = 90)
plot(y=df$Tx90p_count, x=df$year, type = "p", pch = 19, col="red", fill="red", xlab = "", xlim = c(yrmin,yrmax),ylab = "Temperature >90p (count)", main=NULL, ylim = c(0, x1), cex.main = 0.8)
points(y=df$Tn90p_count, x=df$year, pch = 19, col="blue", fill="blue")
abline(lm(df$Tx90p_count ~ df$year), col="red", lty="dotted", lwd=2)
abline(lm(df$Tn90p_count ~ df$year), col="blue", lty="dotted", lwd=2)
abline(v = 2016, col = "gray90")
text((min(df$year, na.rm = T)), x1, tstr, cex=0.9, col="grey20", adj = 0)
leg_txt <- c("maximum", "minimum")
col_code <- c("red", "blue")
mtext(paste0(c("Tx slope : "), txslope), side = 3, line = -2, at = (min(df$year, na.rm = T)), cex=0.8, col="red", adj = 0)
mtext(paste0(c("Tn slope : "), tnslope), side = 3, line = -3, at = (min(df$year, na.rm = T)), cex=0.8, col="blue", adj = 0)
# legend("bottomright", legend = c("maximum", "minimum"), pch = c(16,16), fill=c("red", "blue"), horiz = FALSE, cex = 1, border="white", box.col = NULL, bty = "n")
legend("top", cex = 0.8, bty = "n", legend = c("maximum", "minimum"), text.col = c("red", "blue"), col = c("red", "blue"), pch = c(19,19), horiz = TRUE)
dev.off()
graphics.off()
#write.table(df, file="output.csv", eol = "\r", na = "NA", row.names = FALSE, append = TRUE, sep=",", col.names = !file.exists("output.csv"))
}
# need to add the slope over 2016-2006 and 2005-1995
# write.csv(df, file=paste("out",tstr,".csv"), eol = "\r", na = "NA", row.names = FALSE)
# slope of a chunk of dataframe http://stackoverflow.com/questions/31059043/how-can-i-calculate-the-slope-of-multiple-subsets-of-a-data-frame-more-efficient
# write.table(df, file="output.csv", eol = "\r", na = "NA", row.names = FALSE, append = TRUE, sep=",", col.names = !file.exists("output.csv"))