Time Series

#Load your data.

mydata <- read.csv("ExampleTestData.csv", sep=",")

attach(mydata)

plot(MW01~Year,data=mydata[!is.na(mydata$MW01),],

#ylim sets the range for the y axis

ylim=range(0,60),

#xlim sets the range for the x axis

xlim=range(1994,2008),

# sets the marker type, size and color for 1st series of data MW01

pch=1,cex=0.9,col=1,

#sets the X and Y axis labels

xlab="Year",ylab="EMD (mg/L)")

#Plots the other datasets points. col is the color, pch is the marker type, cex is the size of the marker.

with(mydata[!is.na(mydata$MW02),],points(Year,MW02,col=2,pch=2,cex=0.8))

with(mydata[!is.na(mydata$MW03),],points(Year,MW03,col=3,pch=3,cex=0.8))

with(mydata[!is.na(mydata$MW04),],points(Year,MW04,col=4,pch=4,cex=0.8))

with(mydata[!is.na(mydata$MW05),],points(Year,MW05,col=5,pch=5,cex=0.8))

with(mydata[!is.na(mydata$MW06),],points(Year,MW06,col=6,pch=6,cex=0.8))

with(mydata[!is.na(mydata$MW07),],points(Year,MW07,col=7,pch=7,cex=0.8))

with(mydata[!is.na(mydata$MW08),],points(Year,MW08,col=8,pch=8,cex=0.8))

with(mydata[!is.na(mydata$MW09),],points(Year,MW09,col=9,pch=9,cex=0.8))

with(mydata[!is.na(mydata$MW10),],points(Year,MW10,col=10,pch=10,cex=0.8))

with(mydata[!is.na(mydata$MW11),],points(Year,MW11,col=11,pch=11,cex=0.8))

with(mydata[!is.na(mydata$MW12),],points(Year,MW12,col=12,pch=12,cex=0.8))

with(mydata[!is.na(mydata$MW13),],points(Year,MW13,col=13,pch=13,cex=0.8))

with(mydata[!is.na(mydata$MW14),],points(Year,MW14,col=14,pch=14,cex=0.8))

#Plots the Lines. Lty is the line type, lwd is the line weight/thickness, col is the color

with(mydata[!is.na(mydata$MW01),],lines(Year,MW01,lty=c(1),lwd=c(1),col=1))

with(mydata[!is.na(mydata$MW02),],lines(Year,MW02,lty=c(2),lwd=c(1),col=2))

with(mydata[!is.na(mydata$MW03),],lines(Year,MW03,lty=c(3),lwd=c(1),col=3))

with(mydata[!is.na(mydata$MW04),],lines(Year,MW04,lty=c(4),lwd=c(1),col=4))

with(mydata[!is.na(mydata$MW05),],lines(Year,MW05,lty=c(5),lwd=c(1),col=5))

with(mydata[!is.na(mydata$MW06),],lines(Year,MW06,lty=c(6),lwd=c(1),col=6))

with(mydata[!is.na(mydata$MW07),],lines(Year,MW07,lty=c(7),lwd=c(1),col=7))

with(mydata[!is.na(mydata$MW08),],lines(Year,MW08,lty=c(8),lwd=c(1),col=8))

with(mydata[!is.na(mydata$MW09),],lines(Year,MW09,lty=c(9),lwd=c(1),col=9))

with(mydata[!is.na(mydata$MW10),],lines(Year,MW10,lty=c(10),lwd=c(1),col=10))

with(mydata[!is.na(mydata$MW11),],lines(Year,MW11,lty=c(11),lwd=c(1),col=11))

with(mydata[!is.na(mydata$MW12),],lines(Year,MW12,lty=c(12),lwd=c(1),col=12))

with(mydata[!is.na(mydata$MW13),],lines(Year,MW13,lty=c(13),lwd=c(1),col=13))

with(mydata[!is.na(mydata$MW14),],lines(Year,MW14,lty=c(14),lwd=c(1),col=14))

#Add Legend to graph.

legend("bottomleft",

# adds the text for the legend

c("MW01","MW02", "MW03", "MW04", "MW05","MW06","MW07","MW08","MW09","MW10","MW11","MW12","MW13","MW14"),

#makes the colors

col = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14),

# Sets the size of the legend. You can change the size of the box by changing cex = 0.75 Large # makes it larger.

cex = 1,

#sets the text color

text.col = "black",

#sets the line type

lty = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14),

#Sets the line weight

lwd=c(2),

#sets the marker type

pch = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14),

merge = TRUE, bg = 'gray90')

#Add title

title(main="Time Series Plot")

#done

#The following is for Individual graphs of each well. You can just search and replace MW01 with whatever well you need.

#loads the zoo Library. Note you have to have it installed 1st.

library(zoo)

#Sets the path / dir you will be working in.

setwd("c:/R")

#Load your data. The data is in a spreadsheet named KW-spreadsheet and we are going to call it "data" in R

mydata <- read.csv("ExampleTestData.csv", sep=",")

#Deals with missing data

mydata$MW01<-na.approx(mydata$MW01)

#Plots MW01 data.

plot(MW01~Year,data=mydata,col=ifelse(D_MW01,"black","red"),ylab="END (mg/L)",pch=ifelse(D_MW01,19,24),cex=1)

with(mydata,lines(Year,MW01,lty=c(1),col="black"))

#calculates 2 standard errors one each side of the loess line

plx<-predict(loess(MW01 ~ Year, data=mydata), se=T)

lines(mydata$Year,plx$fit+2*plx$s, lty=3)

lines(mydata$Year,plx$fit-2*plx$s, lty=3)

#Plots the Loess line. You can change the smoothing factor by changing the span number

y.loess <- loess(y ~ x, span=0.8, data.frame(x=mydata$Year,y=mydata$MW01))

y.predict <- predict(y.loess, data.frame(x=mydata$Year))

lines(mydata$Year,y.predict, lty=2, lwd=2)

#Add Legend to MW01. You can change the size of the box by changing cex = 0.75 Large # makes it larger.

legend ("topleft",

c("Smoothing Curve","Confidence Interval","Detect","Non-Detect"),

col = c(1,1,1,2),

# Sets the size of the legend. You can change the size of the box by changing cex = 0.75 Large # makes it larger.

cex = 0.7,

text.col = "black",

#sets the line type

lty = c(2,3,-1,-1),

#sets the line weight

lwd = c(2,1,-1,-1),

#sets the marker type

pch = c(-1,-1,19,24),

merge = TRUE, bg = 'gray90')

#Add title

title(main="MW01")

#Turns off the image storage up at top.

# Done

#The following is for Individual graphs of each well and saving it as image. Code here for the 1st 3 wells. You can just keep searching and replacing and adding new code.

#The following is for Individual graphs of each well. You can just search and replace MW01 with whatever well you need.

#loads the zoo Library. Note you have to have it installed 1st.

library(zoo)

#Sets the path / dir you will be working in.

setwd("c:/R")

#Load your data. The data is in a spreadsheet named KW-spreadsheet and we are going to call it "data" in R

mydata <- read.csv("ExampleTestData.csv", sep=",")

#Deals with missing data

mydata$MW01<-na.approx(mydata$MW01)

#Plots MW01 data.

plot(MW01~Year,data=mydata,col=ifelse(D_MW01,"black","red"),ylab="END (mg/L)",pch=ifelse(D_MW01,19,24),cex=1)

with(mydata,lines(Year,MW01,lty=c(1),col="black"))

#calculates 2 standard errors one each side of the loess line

plx<-predict(loess(MW01 ~ Year, data=mydata), se=T)

lines(mydata$Year,plx$fit+2*plx$s, lty=3)

lines(mydata$Year,plx$fit-2*plx$s, lty=3)

#Plots the Loess line. You can change the smoothing factor by changing the span number

y.loess <- loess(y ~ x, span=0.8, data.frame(x=mydata$Year,y=mydata$MW01))

y.predict <- predict(y.loess, data.frame(x=mydata$Year))

lines(mydata$Year,y.predict, lty=2, lwd=2)

#Add Legend to graph. You can change the size of the box by changing cex = 0.75 Large # makes it larger.

legend ("topright",

c("Detect", "Non-Detect", "LOWESS Curve", "Confidence Interval"),

col = c(1,2,1,1),

cex = 0.75,

text.col = "black",

lty = c(-1,-1,2,3),

pch = c(19, 24, -1, -1),

merge = TRUE, bg = 'gray90')

#Add title

title(main="Locally Weighted Scatterplot Smoothing Curve")

# Done

#Now we do the same for MW02

#The following is for Individual graphs of each well. You can just search and replace MW02 with whatever well you need.

#loads the zoo Library. Note you have to have it installed 1st.

library(zoo)

#Sets the path / dir you will be working in.

setwd("c:/R")

#Load your data. The data is in a spreadsheet named KW-spreadsheet and we are going to call it "data" in R

mydata <- read.csv("ExampleTestData.csv", sep=",")

#Deals with missing data

mydata$MW02<-na.approx(mydata$MW02)

#Plots MW02 data.

plot(MW02~Year,data=mydata,col=ifelse(D_MW02,"black","red"),ylab="END (mg/L)",pch=ifelse(D_MW02,19,24),cex=1)

with(mydata,lines(Year,MW02,lty=c(1),col="black"))

#calculates 2 standard errors one each side of the loess line

plx<-predict(loess(MW02 ~ Year, data=mydata), se=T)

lines(mydata$Year,plx$fit+2*plx$s, lty=3)

lines(mydata$Year,plx$fit-2*plx$s, lty=3)

#Plots the Loess line. You can change the smoothing factor by changing the span number

y.loess <- loess(y ~ x, span=0.8, data.frame(x=mydata$Year,y=mydata$MW02))

y.predict <- predict(y.loess, data.frame(x=mydata$Year))

lines(mydata$Year,y.predict, lty=2, lwd=2)

#Add Legend to graph. You can change the size of the box by changing cex = 0.75 Large # makes it larger.

legend ("topright",

c("Detect", "Non-Detect", "LOWESS Curve", "Confidence Interval"),

col = c(1,2,1,1),

cex = 0.75,

text.col = "black",

lty = c(-1,-1,2,3),

pch = c(19, 24, -1, -1),

merge = TRUE, bg = 'gray90')

#Add title

title(main="Locally Weighted Scatterplot Smoothing Curve")

# Done

#Now we do the same for MW03

#loads the zoo Library. Note you have to have it installed 1st.

library(zoo)

#Sets the path / dir you will be working in.

setwd("c:/R")

#Load your data. The data is in a spreadsheet named KW-spreadsheet and we are going to call it "data" in R

mydata <- read.csv("ExampleTestData.csv", sep=",")

#Deals with missing data

mydata$MW03<-na.approx(mydata$MW03)

#Plots MW03 data.

plot(MW03~Year,data=mydata,col=ifelse(D_MW03,"black","red"),ylab="END (mg/L)",pch=ifelse(D_MW03,19,24),cex=1)

with(mydata,lines(Year,MW03,lty=c(1),col="black"))

#calculates 2 standard errors one each side of the loess line

plx<-predict(loess(MW03 ~ Year, data=mydata), se=T)

lines(mydata$Year,plx$fit+2*plx$s, lty=3)

lines(mydata$Year,plx$fit-2*plx$s, lty=3)

#Plots the Loess line. You can change the smoothing factor by changing the span number

y.loess <- loess(y ~ x, span=0.8, data.frame(x=mydata$Year,y=mydata$MW03))

y.predict <- predict(y.loess, data.frame(x=mydata$Year))

lines(mydata$Year,y.predict, lty=2, lwd=2)

#Add Legend to graph. You can change the size of the box by changing cex = 0.75 Large # makes it larger.

legend ("topright",

c("Detect", "Non-Detect", "LOWESS Curve", "Confidence Interval"),

col = c(1,2,1,1),

cex = 0.75,

text.col = "black",

lty = c(-1,-1,2,3),

pch = c(19, 24, -1, -1),

merge = TRUE, bg = 'gray90')

#Add title

title(main="Locally Weighted Scatterplot Smoothing Curve")

# Done