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# 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) |
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library(ggplot2) | |
library(gtable) | |
library(grid) | |
library(ggthemes) | |
# extract gtable | |
ggplot_second_axis <- function(p1, p2){ | |
p2 <- p2 + theme() %+replace% |
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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") |
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### 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) |
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### 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)){ |
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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") |
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##################################################################################################### | |
### 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. |
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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") |
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