R: Presenting outputs
Dr N Green
14th June 2016
Scatter plot: add points of different colours and symbols
x <- rnorm(10, sd=5, mean=20)
y <- 2.5*x - 1.0 + rnorm(10, sd=9, mean=0)
plot(x,y,xlab="Independent", ylab="Dependent", main="Random Stuff")
x1 <- runif(8,15,25)
y1 <- 2.5*x1 - 1.0 + runif(8,-6,6)
x2 <- runif(8,15,25)
y2 <- 2.5*x2 - 1.0 + runif(8,-6,6)
Scatter plot: add points of different colours and symbols
x <- rnorm(10,sd=5,mean=20)
y <- 2.5*x - 1.0 + rnorm(10,sd=9,mean=0)
plot(x,y,xlab="Independent", ylab="Dependent", main="Random Stuff")
x1 <- runif(8,15,25)
y1 <- 2.5*x1 - 1.0 + runif(8,-6,6)
points(x1,y1,col=2)
x2 <- runif(8,15,25)
y2 <- 2.5*x2 - 1.0 + runif(8,-6,6)
points(x2,y2,col=3,pch=2)
Scatter plot: legend
plot(x,y,xlab="Independent",ylab="Dependent",main="Random Stuff")
points(x1,y1,col=2,pch=3)
points(x2,y2,col=4,pch=5)
Scatter plot: legend
plot(x,y,xlab="Independent",ylab="Dependent",main="Random Stuff")
points(x1,y1,col=2,pch=3)
points(x2,y2,col=4,pch=5)
legend(11,50,c("Original","one","two"),col=c(1,2,4),pch=c(1,3,5))
Simple fitted lines
x <- rnorm(10,sd=5,mean=20)
y <- 2.5*x - 1.0 + rnorm(10,sd=9,mean=0)
x2 <- runif(8,15,25)
y2 <- 2.5*x2 - 1.0 + runif(8,-6,6)
plot(x,y,xlab="Independent",ylab="Dependent",main="Random Stuff")
points(x1,y1,col=2,pch=3)
points(x2,y2,col=4,pch=5)
legend(25,80,c("Original","one","two"),col=c(1,2,4),pch=c(1,3,5))
Simple fitted lines
x <- rnorm(10,sd=5,mean=20)
y <- 2.5*x - 1.0 + rnorm(10,sd=9,mean=0)
x2 <- runif(8,15,25)
y2 <- 2.5*x2 - 1.0 + runif(8,-6,6)
plot(x,y,xlab="Independent",ylab="Dependent",main="Random Stuff")
points(x1,y1,col=2,pch=3)
points(x2,y2,col=4,pch=5)
legend(25,80,c("Original","one","two"),col=c(1,2,4),pch=c(1,3,5))
abline(lm(y~x), col="red")
lines(lowess(x,y), col="blue")
With error lines
plot(x,y,xlab="Independent",ylab="Dependent",main="Random Stuff")
xHigh <- x
yHigh <- y + abs(rnorm(10,sd=3.5))
xLow <- x
yLow <- y - abs(rnorm(10,sd=3.1))
With error lines
plot(x,y,xlab="Independent",ylab="Dependent",main="Random Stuff")
xHigh <- x
yHigh <- y + abs(rnorm(10,sd=3.5))
xLow <- x
yLow <- y - abs(rnorm(10,sd=3.1))
arrows(xHigh,yHigh,xLow,yLow,col=2,angle=90,length=0.1,code=3)
Adding jitter
numberWhite <-
numberChipped <-
par(mfrow=c(1,2))
plot(numberWhite,numberChipped,xlab="Number White Marbles Drawn",
ylab="Number Chipped Marbles Drawn",main="Pulling Marbles")
plot(jitter(numberWhite),jitter(numberChipped),xlab="Number White Marbles Drawn",
ylab="Number Chipped Marbles Drawn",main="Pulling Marbles With Jitter")
Adding jitter
numberWhite <- rhyper(400,4,5,3)
numberChipped <- rhyper(400,2,7,3)
par(mfrow=c(1,2))
plot(numberWhite,numberChipped,xlab="Number White Marbles Drawn",
ylab="Number Chipped Marbles Drawn",main="Pulling Marbles")
plot(jitter(numberWhite),jitter(numberChipped),xlab="Number White Marbles Drawn",
ylab="Number Chipped Marbles Drawn",main="Pulling Marbles With Jitter")
Mosaic plots
mosaicplot(,main="sixth plot")
Mosaic plots
mosaicplot(table(numberWhite,numberChipped),main="sixth plot")
Pair-wise scatter plots
uData <- rnorm(20)
vData <- rnorm(20,mean=5)
wData <- uData + 2*vData + rnorm(20,sd=0.5)
xData <- -2*uData+rnorm(20,sd=0.1)
yData <- 3*vData+rnorm(20,sd=2.5)
pairs(?)
Pair-wise scatter plots
uData <- rnorm(20)
vData <- rnorm(20,mean=5)
wData <- uData + 2*vData + rnorm(20,sd=0.5)
xData <- -2*uData+rnorm(20,sd=0.1)
yData <- 3*vData+rnorm(20,sd=2.5)
d <- data.frame(u=uData,v=vData,w=wData,x=xData,y=yData)
pairs(d)
Shaded areas
Shaded areas
Shaded areas
stdDev <- 0.75;
x <- seq(-5,5,by=0.01)
y <- dnorm(x,sd=stdDev)
right <- qnorm(0.95,sd=stdDev)
plot(x,y,type="l",xaxt="n",ylab="p",xlab=expression(paste('Assumed Distribution of ',bar(x))), axes=FALSE,ylim=c(0,max(y)*1.05),xlim=c(min(x),max(x)),frame.plot=FALSE)
axis(1,at=c(-5,right,0,5), pos = c(0,0), labels=c(expression(' '),expression(bar(x)[cr]),expression(mu[0]),expression(' ')))
axis(2)
xReject <- seq(right,5,by=0.01); yReject <- dnorm(xReject,sd=stdDev)
polygon(c(xReject,xReject[length(xReject)],xReject[1]), c(yReject,0, 0), col='red')
Different types of lines
x <- c(1:5); y <- x
par(pch=22, col="red")
par(mfrow=c(2,4))
opts = ?
for(i in 1:length(opts)){
heading = paste("type=",opts[i])
plot(x, y, type="n", main=heading)
lines(x, y, type=opts[i])
}
Different types of lines
x <- c(1:5); y <- x
par(pch=22, col="red")
par(mfrow=c(2,4))
opts = c("p","l","o","b","c","s","S","h")
for(i in 1:length(opts)){
heading = paste("type=",opts[i])
plot(x, y, type="n", main=heading)
lines(x, y, type=opts[i])
}
Barplot
numberWhite <- rhyper(30,4,5,3)
numberWhite <- as.factor(numberWhite)
barplot(?,main="Number Draws",ylab="Frequency",xlab="Draws")
numberWhite
0 1 2 3
3 16 9 2
Barplot
numberWhite <- rhyper(30,4,5,3)
numberWhite <- as.factor(numberWhite)
plot(numberWhite)
totals <- table(numberWhite)
totals
numberWhite
0 1 2 3
2 15 12 1
barplot(totals,main="Number Draws",ylab="Frequency",xlab="Draws")
plotly
ggplot2: structure
qplot: overlapping densities
library(ggplot2)
mtcars$gear <- factor(mtcars$gear,levels=c(3,4,5), labels=c("3gears","4gears","5gears"))
mtcars$am <- factor(mtcars$am,levels=c(0,1), labels=c("Automatic","Manual"))
mtcars$cyl <- factor(mtcars$cyl,levels=c(4,6,8), labels=c("4cyl","6cyl","8cyl"))
qplot(mpg, data=, geom="", fill=, alpha=, main="Distribution of Gas Milage", xlab="Miles Per Gallon", ylab="Density")
qplot: overlapping densities
library(ggplot2)
data("mtcars")
mtcars$gear <- factor(mtcars$gear,levels=c(3,4,5), labels=c("3gears","4gears","5gears"))
mtcars$am <- factor(mtcars$am,levels=c(0,1), labels=c("Automatic","Manual"))
mtcars$cyl <- factor(mtcars$cyl,levels=c(4,6,8), labels=c("4cyl","6cyl","8cyl"))
qplot(mpg, data=mtcars, geom="density", fill=gear, alpha=I(.5), main="Distribution of Gas Milage", xlab="Miles Per Gallon", ylab="Density")
qplot: facets and points
library(ggplot2)
data("mtcars")
mtcars$gear <- factor(mtcars$gear,levels=c(3,4,5), labels=c("3gears","4gears","5gears"))
mtcars$am <- factor(mtcars$am,levels=c(0,1), labels=c("Automatic","Manual"))
mtcars$cyl <- factor(mtcars$cyl,levels=c(4,6,8), labels=c("4cyl","6cyl","8cyl"))
qplot(, , data=mtcars, shape=, color=, facets=gear~cyl, size=I(3), xlab="Horsepower", ylab="Miles per Gallon")
qplot: facets and points
library(ggplot2)
data("mtcars")
mtcars$gear <- factor(mtcars$gear,levels=c(3,4,5), labels=c("3gears","4gears","5gears"))
mtcars$am <- factor(mtcars$am,levels=c(0,1), labels=c("Automatic","Manual"))
mtcars$cyl <- factor(mtcars$cyl,levels=c(4,6,8), labels=c("4cyl","6cyl","8cyl"))
qplot(hp, mpg, data=mtcars, shape=am, color=am, facets=gear~cyl, size=I(3), xlab="Horsepower", ylab="Miles per Gallon")
hist vs geom_histogram
hist vs geom_histogram
ggplot(diamonds, aes(x = carat)) + geom_histogram(bins = 500)
ggplot2: Box plot and points with legend
plot(carat ~ clarity,
data=subset(diamonds, cut == "Good"))
points(carat ~ clarity, col="red",
data=subset(diamonds, cut == "Ideal"))
legend(0,3,
c("Good", "Ideal"), title="cut",
col=c("black", "red"),
pch=c(1, 1))
ggplot2
ggplot(subset(diamonds, cut %in% c("Good", "Ideal")),
aes(x=clarity,
y=carat,
color=cut))+
geom_point()
Symbols
## A look at all 25 symbols
df2 <- data.frame(x = 1:5 , y = 1:25, z = 1:25)
s <- ggplot(df2, aes(x = x, y = y))
s + geom_point(aes(shape = z), size = 4) + scale_shape_identity()
## While all symbols have a foreground colour, symbols 19-25 also take a
## background colour (fill)
s + geom_point(aes(shape = z), size = 4, colour = "Red") +
scale_shape_identity()
s + geom_point(aes(shape = z), size = 4, colour = "Red", fill = "Black") +
scale_shape_identity()
