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Data Visualization with R.ggplot2 and its extensions examples.
The document provides examples of data visualization in R using the ggplot2 library, including techniques for creating correlation plots and customizing themes. It also demonstrates how to simulate data and build a structural equation model (SEM) while showcasing the use of various libraries such as semplot and lavaan. Additionally, references to external resources for further learning are included.
Data Visualization with R.ggplot2 and its extensions examples.
- 1. prepared by VOLKANOBAN DATA VİSUALIZATION with R ggplot2 and Its Extension Examples:
- 8. theme(panel.background = element_rect(fill= 'paleturquoise'), panel.grid.major = e lement_line(colour = "purple2", size=3), panel.grid.minor = element_line(colour = " red4", size=1))
- 12. 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)
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- 14. Code: library(semPlot) library(lavaan) library(clusterGeneration) #this isto generate a positive definite covariance matrix #simulate some data set.seed(1222) sig<-genPositiveDefMat("onion",dim=5,eta=4)$Sigma #the covariance matrix mus<-c(10,5,120,35,6) #the vector of the means data<-as.data.frame(mvrnorm(100,mu=mus,Sigma=sig)) #the dataset names(data)<-c("CO2","Temp","Nitro","Biom","Rich") #giving it some names #building an SEM with a latent variable m<-'Abiot =~ CO2 + Temp + Nitro Biom ~ Abiot Rich ~ 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 coefficients semPaths(m.fit,what="std",layout="circle")
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