reading_hilltop.R

#Required R modules
library(XML)
library(ggplot2)

url <- "http://hilltopserver.horizons.govt.nz/data.hts?Service=Hilltop&Request=GetData&Site=Manawatu%20at%20Teachers%20College&Measurement=Stage&From=1/08/2017&To=5/08/2017"
#dataxml<-xmlParse(url)
#print(dataxml)
measxml<-xmlInternalTreeParse(url)


error<-as.character(sapply(getNodeSet(doc=measxml, path="//Error"), xmlValue))
#print(error)
if(length(error)==0){
  
  m<-getNodeSet(measxml, "//Hilltop/Measurement")
  
  
  for(ms in 1:length(m)){
    d <- getNodeSet(measxml, paste("//Measurement[",ms,"]/Data",sep=""))
    
    
    for(ds in 1:length(d)){
      e <- getNodeSet(measxml, paste("//Measurement[",ms,"]/Data[",ds,"]/E",sep=""))
      
      mes_df = NULL
      for(es in 1:length(e)){
        timexmldf <- xmlToDataFrame(getNodeSet(measxml, paste("//Measurement[",ms,"]/Data[",ds,"]/E[",es,"]/T",sep="")))
        #t = rbind(t, data.frame(timexmldf))
        #Time1<-as.POSIXct(strptime(timexmldf,format="%Y-%m-%dT%H:%M:%S"))
        valuexmldf <- xmlToDataFrame(getNodeSet(measxml, paste("//Measurement[",ms,"]/Data[",ds,"]/E[",es,"]/I1",sep="")))
        measurment <- "Stage"
        #print(valuexmldf)
        #v = rbind(v, data.frame(valuexmldf))
        mes_df = rbind(mes_df,data.frame(measurment,timexmldf,valuexmldf))
    
      }
      #print(mes_df)
      names(mes_df) <- c("Measurement", "Time", "Value") # renaming the fields
      
      New_mes_df = NULL
      NewMes<-(mes_df$Measurement)
      NewTime<-as.POSIXct(strptime(mes_df$Time,format="%Y-%m-%dT%H:%M:%S")) #confirming time format
      NewValue<-(as.numeric(as.character(mes_df$Value))) #make sure that the values are numbers
      New_mes_df = rbind(New_mes_df,data.frame(NewMes,NewTime,NewValue)) #make a new dataframe
      #print(New_mes_df)
      
      
      print(ggplot(New_mes_df, aes(NewTime, NewValue)) +
              geom_line(na.rm=TRUE, color="blue", size=1) +
              ggtitle(paste("Stage (mm) at","\n ","Manawatu at Teachers College")) +
              xlab("Date") + ylab("Stage (mm)"))
    }
  }
}

######################
url1 <- "http://hilltopserver.horizons.govt.nz/data.hts?Service=Hilltop&Request=GetData&Site=Manawatu%20at%20Teachers%20College&Measurement=Turbidity:%20Point%20Sample&From=1/08/2017&To=5/08/2017"
#dataxml<-xmlParse(url)
#print(dataxml)
measxml1<-xmlInternalTreeParse(url1)


error<-as.character(sapply(getNodeSet(doc=measxml1, path="//Error"), xmlValue))
#print(error)
if(length(error)==0){
  
  m<-getNodeSet(measxml1, "//Hilltop/Measurement")
  
  
  for(ms in 1:length(m)){
    d <- getNodeSet(measxml1, paste("//Measurement[",ms,"]/Data",sep=""))
    
    
    for(ds in 1:length(d)){
      e <- getNodeSet(measxml1, paste("//Measurement[",ms,"]/Data[",ds,"]/E",sep=""))
      
      mes_df1 = NULL
      for(es in 1:length(e)){
        timexmldf1 <- xmlToDataFrame(getNodeSet(measxml1, paste("//Measurement[",ms,"]/Data[",ds,"]/E[",es,"]/T",sep="")))
        #t = rbind(t, data.frame(timexmldf1))
        #Time1<-as.POSIXct(strptime(timexmldf1,format="%Y-%m-%dT%H:%M:%S"))
        valuexmldf1 <- xmlToDataFrame(getNodeSet(measxml1, paste("//Measurement[",ms,"]/Data[",ds,"]/E[",es,"]/I1",sep="")))
        measurment1 <- "Turbidity"
        #print(valuexmldf1)
        #v = rbind(v, data.frame(valuexmldf1))
        mes_df1 = rbind(mes_df1,data.frame(measurment1,timexmldf1,valuexmldf1))
        
      }
      #print(mes_df1)
      names(mes_df1) <- c("Measurement", "Time", "Value") # renaming the fields
      
      New_mes_df1 = NULL
      NewMes<-(mes_df1$Measurement)
      NewTime<-as.POSIXct(strptime(mes_df1$Time,format="%Y-%m-%dT%H:%M:%S")) #confirming time format
      NewValue<-(as.numeric(as.character(mes_df1$Value))) #make sure that the values are numbers
      New_mes_df1 = rbind(New_mes_df1,data.frame(NewMes,NewTime,NewValue)) #make a new dataframe
      #print(New_mes_df1)
      
      
      print(ggplot(New_mes_df1, aes(NewTime, NewValue)) +
              geom_line(na.rm=TRUE, color="brown", size=1) +
              ggtitle(paste("Turbidity (NTU) (mm) at","\n ","Manawatu at Teachers College")) +
              xlab("Date") + ylab("Turbidity (NTU)"))
    }
  }
}

summary(New_mes_df$NewValue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     982    1164    1280    1380    1533    2409

summary(New_mes_df1$NewValue)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    8.70   14.10   32.40   95.96   83.30  672.00

p <- ggplot()
  p <- p + geom_line(data = New_mes_df, aes(x = NewTime, y = NewValue, colour= "Stage"))
  
  # adding the relative humidity data, transformed to match roughly the range of the temperature
  p <- p + geom_line(data = New_mes_df1, aes(x = NewTime, y = NewValue/.28, colour= "Turbidity"), size=.75, linetype="F1")

  
  # now adding the secondary axis, following the example in the help file ?scale_y_continuous
  # and, very important, reverting the above transformation
  p <- p + scale_y_continuous(sec.axis = sec_axis(~.*.28, name = "Turbidity [NTU]"))
  #print(p)
  
  # modifying colours and theme options
  p <- p + scale_colour_manual(values = c("blue", "brown"))
  p <- p + labs(y = "Stage[mm]",
                x = "Date and time",
                colour = "Parameter")
  p <- p + theme(legend.position = c(0.8, 0.9))
p

#Line types
d=data.frame(lt=c("blank", "solid", "dashed", "dotted", "dotdash", "longdash", "twodash", "1F", "F1", "4C88C488", "12345678"))
ggplot() +
  scale_x_continuous(name="", limits=c(0,1), breaks=NULL) +
  scale_y_discrete(name="linetype") +
  scale_linetype_identity() +
  geom_segment(data=d, mapping=aes(x=0, xend=1, y=lt, yend=lt, linetype=lt))