## Figure Empirical Data Density
library(plyr)
wtr = read.table('../supporting files/wtemp.obs.tsv', sep='\t', header=TRUE)
wtr$DATETIME = as.POSIXct(wtr$DATETIME)
wtr$year = as.POSIXlt(wtr$DATETIME)$year+1900
data.by.year = ddply(wtr,'year',function(df) length(unique(df$WBIC)))
tiff('data.by.year.1979.tiff', width=1800, height=1200, res=300, compression='lzw')
plot(data.by.year$year, data.by.year$V1, xlim=c(1979, 2011), xlab="Year",
ylab="Unique Lakes", type='b', lwd=2)
dev.off()
tiff('data.by.year.1950.tiff', width=1800, height=1200, res=300, compression='lzw')
plot(data.by.year$year, data.by.year$V1, xlim=c(1950, 2011), xlab="Year",
ylab="Unique Lakes", type='b', lwd=2)
dev.off()
better.JAS = function(df){
if(nrow(df) > 4){
return(mean(df$V1))
}else{
return(NA)
}
}
wtr.near.surf = ddply(wtr, c("WBIC", "DATETIME"), function(df) mean(df$WTEMP[df$DEPTH<=2], na.rm=TRUE))
mons = as.POSIXlt(wtr.near.surf$DATETIME)$mon+1
wtr.near.surf = wtr.near.surf[mons >=7 & mons <= 9,]
wtr.near.surf$year = as.POSIXlt(wtr.near.surf$DATETIME)$year+1900
JAS.mean = ddply(wtr.near.surf, c("WBIC", "year"), better.JAS)
JAS.mean = JAS.mean[!is.na(JAS.mean$V1),]
data.by.year = ddply(JAS.mean,'year',function(df) nrow(df))
tiff('data.JAS.yearly.1979.tiff', width=1800, height=1200, res=300, compression='lzw')
plot(data.by.year$year, data.by.year$V1, xlim=c(1979, 2011), xlab="Year",
ylab="Lakes with JAS", type='b', lwd=2)
dev.off()
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