PSMSL Tide Data

PSMSL_tide_data.R

# PSMSL Tide Data

library(lubridate)
library(data.table)

# Individual File Reading 
# Read the gauge file list (location, names)
file_list = read.csv("C:/Users/Andrew.Bannerman/Desktop/PSMSL Tidal Data/rlr_monthly/rlr_monthly/filelist.txt",stringsAsFactors = FALSE, header=F,sep=";")

# Read 1x data set - oldest station with 1807 start date (sets the initial date)
data = read.csv("C:/Users/Andrew.Bannerman/Desktop/PSMSL Tidal Data/rlr_monthly/rlr_monthly/data/1.rlrdata",stringsAsFactors = FALSE, header=F,sep=";")
data$V2[data$V2 == -99999] <- NA
names = c("Date","Tidal_Level","flag_attention_1","flag_attention_2")
colnames(data) = names
head(data)

# Convert decimal date to R date using lubridate function
# Date format = year + (month-0.5)/12.0
data$V1 = date_decimal(data$V1) 
plot(data$V1,data$V2,type="b")

# Loop to join the data by common date - shall take the average value across all tidal gauges 
# Loop through the directory

# Set initial data frame 
df = data.frame(data$Date)
colnames(df) = "Date"
head(df)

path = "C:/Users/Andrew.Bannerman/Desktop/PSMSL Tidal Data/rlr_monthly/rlr_monthly/data"
out.file<-""
file.names <- dir(path, pattern =".rlrdata")
i=1
head(file_list)
for(i in 1:length(file.names)){
  file <- read.csv(paste0("C:/Users/Andrew.Bannerman/Desktop/PSMSL Tidal Data/rlr_monthly/rlr_monthly/data/",file.names[i]),stringsAsFactors = FALSE, header=F,sep=";")
  file$V2[file$V2 == -99999] <- NA
  file$V1 = date_decimal(file$V1)
  new_df = data.frame(file$V1,file$V2)
  #names = c("Date",paste0("Tide_Level_",file_list[i,4]),paste0("flag_1",file_list[i,4]),paste0("flag_2",file_list[i,4]))
  names = c("Date",paste0("Tide_Level_",file_list[i,4]))
  colnames(new_df) = names
  head(new_df)
  setDT(df)
  setDT(new_df)
  df = new_df[df, on = c('Date')]
  cat("this is iteration",i,"\n")
}

# Perform Row Means 
row_mean_df = df[1:nrow(df),2:length(df)]
row_mean <- rowMeans(row_mean_df, na.rm=TRUE)
final = data.frame(df$Date,row_mean)
colnames(final) = c("Date","World_Wide_Mean")
head(final)

# Standard Plot
plot(df$Date,row_mean,type="b")
final$na_count <- apply(!is.na(row_mean_df), 1, sum)
plot(df$Date,row_mean_df$na_count,type="l")

str(final)
# Tidal data plot 
library(ggplot2)
library(scales)
p1 = ggplot(data = final, aes(x=Date,y=World_Wide_Mean,col=World_Wide_Mean)) +
  geom_point(color="red")+
geom_line(color="red")+
  theme_bw()+
  ggtitle("Mean Sea Level")+
  ylab("Annual Monthly Mean - All RLR Stations")+
  xlab("Date")+
  scale_y_continuous(breaks = seq(0, max(final$World_Wide_Mean), by = 100))+
  labs(caption = "Data Source: Permanent Service for Mean Sea Level")
 # scale_x_date(breaks = date_breaks("year"), 
  #             labels=date_format(format = "%YYYY", tz = "UTC"))
  scale_x_date(date_breaks = "year")
  labs(colour="Bedroom Size")
  
 p2 = ggplot(data = final, aes(x=Date,y=na_count,col=World_Wide_Mean)) +
    geom_line(color="red")+
    theme_bw()+
    ggtitle("Total Stations")+
    ylab("#No. Stations")+
    xlab("Date")+
    scale_y_continuous(breaks = seq(0, max(final$World_Wide_Mean), by = 100))
  
 multiplot(p1, p2,cols=1)
 
 library(gridExtra)
 grid.arrange(p1, p2, ncol = 1)
 

# Seasonal Component subset all data North and South Hemisphere