#Example scripts to read the datasets for the statistics lecture
#You have to adapt the path
#Questions: tlaepple@awi.de


#Read the global mean temperature from the GISS dataset. The first 8 lines are comments, the global mean temperature is
#in column 2
data<-read.table("e:/data/lecture/statistics/data/GlobTemp_GISS.txt",skip=8,header=T)
globtemp.giss<-ts(data[,2],start=data[1,1])
plot(globtemp.giss)

#Read the NAO dataset, simple comma separated file
data<-read.csv("e:/data/lecture/statistics/data/nao.csv")
nao<-ts(data[,2],start=data[1,1])

#Read the hurricane number datasets, simple comma separated file
data<-read.csv("e:/data/lecture/statistics/data/TC_basin.1.5.csv")
basin<-ts(data[,2],start=data[1,1])

data<-read.csv("e:/data/lecture/statistics/data/TC_landfall.1.5.csv")
landfall<-ts(data[,2],start=data[1,1])


#Option 1 for the NGRIP data, skip 71 lines, than read the tab-separated data
data<-read.table("e:/data/lecture/statistics/data/NGrip.txt",skip=71,na.strings = "NaN")
# Put it into a timeseries object. The timestep between two samples is 20 years
dO18<-ts(data[,3],start=data[1,1],deltat=20)

#Option 2 for the NGRIP data, skip 71 lines, than read the tab-separated data
data<-read.table("e:/data/lecture/statistics/data/NGrip.txt",comment.char="%",na.strings = "NaN")
# Put it into a timeseries object. The timestep between two sam
dO18<-ts(data[,3],start=data[1,1],deltat=20)

#Read the global mean temperature from the IPCC models. Simple text file with a header line:
data<-read.table("e:/data/lecture/statistics/data/GlobTemp_IPCC.txt",header=T)
globtemp.ipcc<-ts(data[,2],start=data[1,1])