johnDorian

setwd("~/Documents/code/francesca")

library(TSAgg)

quality<-read.csv("Burke_worked.csv",header=T)

head(quality)

q2<-timeSeries(quality$date,"%d/%m/%Y %H:%M")

johnDorian

#####################################################################################################
### Aim: To test the significance of randomly allocating flow to 'midnight' water quality samples.
### Date Created: Thursday 2nd September 2010
### Author: Jason Lessels
### Packages required: TSAgg_0.2-1,geoR 
### Notes: The script can easily be modified for the other water quality parameters, and the amount of simulations. Things to check:
###Both WQ and discharge must have the same initial time stamp (hours) before aggregation.
###WQ variable modify lines 52,60,146,186
###line 99 changes the amount of simulations to run.
###line 156 determines when the bushfire occurred.

johnDorian

piper <-
function (data, group = NULL, colours = NULL, pch = NULL, numbersymbols = FALSE, 
    X = 300, ...) 
{
    p <- (X/11)
    q <- (X/22)
    over100 <- data[data$Ca + data$Mg > 100 | data$Cl + data$SO4 > 
        100, ]
    if (length(over100[, 1]) != 0) {
        print("ERROR")

johnDorian

### A piper diagram based on the ternary plot example here: http://srmulcahy.github.io/2012/12/04/ternary-plots-r.html
### This was written quickly, and most likely contains bugs - I advise you to check it first.
### Jason Lessels jlessels@gmail.com

### This now consists of two functions. transform_piper_data transforms the data to match 
### the coordinates of the piper diagram. ggplot_piper does all of the background.


transform_piper_data <- function(Mg, Ca, Cl,SO4, name=NULL){
  if(is.null(name)){

johnDorian

### simulate some dates
dates <- seq.Date(as.Date("2014-01-01"), as.Date("2014-12-31"), by = "1 day")
### simulate some rainfall
rain <- sample(c(0,0,0,2,3), length(dates), replace=T)

data <- data.frame(date = dates, 
                   daily_precip = rain)

# Use a rolling apply function to check the rainfall of the last 3 days including the day in question.
library(zoo)

johnDorian

library(ggplot2)
library(lubridate)
libarry(ggthemes)
# Create a series of dates (hourly)
hourly_date <- seq.POSIXt(dmy("01012012"), dmy("31122012"), by = "1 hour")
# Create some fictional fdom data.
fdom <- data.frame(date = hourly_date, fdom = sin(1:length(hourly_date)/300)+10)

# Create a series of dates (daily)
daily_date <- seq.POSIXt(dmy("01012012"), dmy("31122012"), by = "1 day")

johnDorian

library(ggplot2)
library(gtable)

library(grid)
library(ggthemes)

# extract gtable

ggplot_second_axis <- function(p1, p2){
  p2 <- p2 + theme() %+replace% 

johnDorian

# load the libraries
library(ggplot2)
library(gtable)
library(gridExtra)
# Create the plots 
p1 <- ggplot() + geom_point(aes(1,1))
p2 <- ggplot() + geom_point(aes(2,2))
p3 <- ggplot() + geom_point(aes(3,3))
p4 <- ggplot() + geom_point(aes(4,4))
# Firstly create two columns (the first and second column)

johnDorian

library(lubridate)
## A synthetic data set. 
doc <- data.frame(date=ymd_hm("2014-01-01 14:00") + (1:360)* 24*3600)
# Find all the dates before a certain time period. 
index <- doc$date < ymd("2014-06-01")
## use the hour function to extract the hour of each day e.g
hour(doc$date)
## Now using the index from above you can subtract an hour from each of the times
# using the index vector and reassiging the modified hour back to the time stamp. 
hour(doc$date[index]) <- hour(doc$date[index]) - 1

johnDorian

set.seed(1082)
# Create two weeks of 15 minute data
library(lubridate)
dates <- seq.POSIXt(dmy_hm("01-01-2013 00:00"), dmy_hm("31-12-2013 11:45"), by = "15 mins")
# Add a column with a random variable with mean = 10
temp <- data.frame(date = dates, temp = rnorm(length(dates))+10)
plot(temp, type='l')
# aggreagate the data to daily by converting the POSIX date to date format (i.e drop the timestamp)
daily_temp <- aggregate(temp$temp, list(date=as.Date(temp$date)), mean)
# fix the names up