Data visualization with R

1. Preparedby
Volkan OBAN
BOXPLOT EXAMPLES in R
AND BEESWARM:

2. CODES:
1-str(airquality)
'data.frame': 153 obs. of 6 variables:
$ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
$ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
$ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
$ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
$ Month : int 5 5 5 5 5 5 5 5 5 5 ...
$ Day : int 1 2 3 4 5 6 7 8 9 10 ...
> boxplot(airquality$Ozone)
> boxplot(airquality$Ozone,
+ main="Mean ozone in parts per billion at Roosevelt Island",
+ xlab="Parts Per Billion",
+ ylab="Ozone",
+ col="orange",
+ border="brown",
+ horizontal=TRUE,
+ notch=TRUE
+ )
> ozone <- airquality$Ozone
> temp <- airquality$Temp
> ozone_norm <- rnorm(200,mean=mean(ozone, na.rm=TRUE), sd=sd(ozone, na.rm=
TRUE))
> temp_norm <- rnorm(200,mean=mean(temp, na.rm=TRUE), sd=sd(temp, na.rm=TRU
E))
> boxplot(ozone, ozone_norm, temp, temp_norm,
+ main="Multiple boxplots for comparision",
+ at=c(1,2,4,5),
+ names=c("ozone", "normal", "temp", "normal"),
+ las=2,
+ col=c("orange","red"),
+ border="brown",
+ horizontal=TRUE,
+ notch=TRUE)

6. Example:
2-qplot(class, hwy, fill=factor(year), data=mpg, geom="boxplot", position
="dodge")+theme_bw()

7. 3-data<-data.frame(Stat11=rnorm(100,mean=3,sd=2),
+ Stat21=rnorm(100,mean=4,sd=1),
+ Stat31=rnorm(100,mean=6,sd=0.5),
+ Stat41=rnorm(100,mean=10,sd=0.5),
+ Stat12=rnorm(100,mean=4,sd=2),
+ Stat22=rnorm(100,mean=4.5,sd=2),
+ Stat32=rnorm(100,mean=7,sd=0.5),
+ Stat42=rnorm(100,mean=8,sd=3),
+ Stat13=rnorm(100,mean=6,sd=0.5),
+ Stat23=rnorm(100,mean=5,sd=3),
+ Stat33=rnorm(100,mean=8,sd=0.2),
+ Stat43=rnorm(100,mean=4,sd=4))
> boxplot(data)

8. 4-> library(ggplot2)
> a <- runif(10)
> b <- runif(12)
> c <- runif(7)
> d <- runif(15)
> group <- factor(rep(1:4, c(10, 12, 7, 15)))
> mydata <- data.frame(c(a,b,c,d), group)
> names(mydata) <- c("value", "group")
> min.mean.sd.max <- function(x) {
+ r <- c(min(x), mean(x) - sd(x), mean(x), mean(x) + sd(x), max(x))
+ names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
+ r
+ }
> p1 <- ggplot(aes(y = value, x = factor(group)), data = mydata)
> p1 <- p1 + stat_summary(fun.data = min.mean.sd.max, geom = "boxplot") + g
eom_jitter(position=position_jitter(width=.2), size=3) + ggtitle("Boxplot c
on media, 95%CI, valore min. e max.") + xlab("Gruppi") + ylab("Valori")
> p1

9. 5-install.packages("GGally")
ds = read.csv("http://www.math.smith.edu/r/data/help.csv")
> ds$sex = as.factor(ifelse(ds$female==1, "female", "male"))
> ds$housing = as.factor(ifelse(ds$homeless==1, "homeless", "housed"))
> smallds = subset(ds, select=c("housing", "sex", "i1", "cesd"))
library(GGally)
> ggpairs(smallds, diag=list(continuous="density", discrete="bar"), axisLab
els="show") #1.graph
>ggpairs(ds, columns=c("housing", "sex", "i1", "cesd"),
+ diag=list(continuous="density", discrete="bar"), axisLabels="sh
ow") [#2.graph]
1.graph

10. Secondgraph

11. 6-
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)

12. 7- library(ggplot2)
>
> # Plot Skeleton
> p <- ggplot(diamonds, aes(x=factor(color), y=carat))
>
> # Boxplot of diamond carat as a function of diamond color
> p + geom_boxplot()

13. LAST EXAMPLE:
install.packages("beeswarm")
#data
set.seed(1234)
bimodal <- c(rnorm(250, -2, 0.6), rnorm(250, 2, 0.6))
uniform <- runif(500, -4, 4)
normal <- rnorm(500, 0, 1.5)
dataf <- data.frame (group = rep(c("bimodal","uniform", "normal"), each =
500), xv = c(bimodal, uniform, normal), cg = rep( c("A","B"), 750))
require(beeswarm)
beeswarm(xv ~ group, data = dataf,method = 'swarm',
pch = 16, pwcol = as.numeric(cg),
xlab = '', ylab = 'xv', labels = c('bimodal', 'uniform', 'normal'))