prepared by VOLKAN OBAN

DATA VİSUALIZATION with Rggplot2 and Its Extension Examples:

theme(panel.background = element_rect(fill = 'paleturquoise'), panel.grid.major = element_line(colour = "purple2", size=3), panel.grid.minor = element_line(colour = "red4", size=1))

corr <- round(cor(mtcars), 2)

df <- reshape2::melt(corr)

gg <- ggplot(df, aes(x=Var1, y=Var2, fill=value, label=value)) + geom_tile() + theme_bw() + geom_text(aes(label=value, size=value), color="white") + labs(title="mtcars - Correlation plot") + theme(text=element_text(size=20), legend.position="none")

library(RColorBrewer)

p2 <- gg + scale_fill_distiller(palette="Reds")

p3 <- gg + scale_fill_gradient2()

gridExtra::grid.arrange(gg, p2, p3, ncol=3)

lattice example:

Code:

library(semPlot)library(lavaan)library(clusterGeneration) #this is to generate a positive definite covariance matrix#simulate some dataset.seed(1222)sig<-genPositiveDefMat("onion",dim=5,eta=4)$Sigma #the covariance matrixmus<-c(10,5,120,35,6) #the vector of the meansdata<-as.data.frame(mvrnorm(100,mu=mus,Sigma=sig)) #the datasetnames(data)<-c("CO2","Temp","Nitro","Biom","Rich") #giving it some names#building an SEM with a latent variablem<-'Abiot =~ CO2 + Temp + NitroBiom ~ AbiotRich ~ Abiot + Biom'm.fit<-sem(m,data)#the plot#basic version, the what arguments specify what should be plotted, here we choose to look at the standardized path coefficientssemPaths(m.fit,what="std",layout="circle")

Reference: http://r-statistics.co/

http://r-statistics.co/ggplot2-cheatsheet.html#Annotationhttp://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf