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This is example shows a The code below generates the plot of the Dutch tide gauges. Notice that after the last 6 years of (average) sea-level decline we are about to see a rising sea level at the Dutch coast again for the next 12 years.
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# install.packages("sealevel", repos="http://r-forge.r-project.org")
library(sealevel)
library(ggplot2)
library(plyr)
# Load the Dutch sealevel data
data(dutch)
# Let's plot them all
# The stations are a list of objects, but we can make a data frame like this
station2df <- function(x){
df <- x$data
df$name <- x$name
# set -999 to missing
df$waterlevel[df$waterlevel==-999.9] <- NA
return(df)
}
# Let's do bit of map reducing, list -> station2df -> data.frame
dutchdf <- ldply(dutch, station2df)
# Now make a factor instead from the strings
dutchdf$name <- as.factor(dutchdf$name)
# so we can use it to shift the waterlevels a bit
dutchdf$waterlevel <- dutchdf$waterlevel - as.numeric(dutchdf$name)*10
# Use the gramar of graphics approach
p <- ggplot(dutchdf, aes(year, waterlevel, color=name))
# Fit a nodal cycle (long term tide), See Baart et al 2012 in JCR.
p + geom_point() + geom_smooth(method='lm', formula=y~x+I(cos(2*pi*x/18.613))+I(sin(2*pi*x/18.613)))
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