Exporting Multiple Panels of Plots and Data to *.Png (In the Style Layout() Works Within R)

Exporting multiple panels of plots and data to *.png (in the style layout() works within R)

If you move your calls to par or layout after you open your graphics device, it should work correctly.

png(file='plot2.png')
par(mfrow= c(3, 1))
curve(dnorm, -3, 4)
textplot(testMat)
textplot(testMatDF)
dev.off()

How to keep the proportions when switching from png to pdf in R

The size of the png device is given in pixels, the pdf in inches. Using the fact that the (default) resolution is 72 points per inch (ppi) (see ?png), you get the same proportions for the pdf if you use:

pdf("plot.pdf",width=500/72,height=500/72)

How to save plot generated by pairs.panels from R's psych library

If you look at the code base for psych, specifically at the pairs.panels you will see that it is using base graphics to do it's work, and drawing all the elements there. There is no dependency on ggplot2. The base file was formed in 2007.

I think that you are going to have to continue to use saving the image out with something like png() if you are committed to using this package. Theoretically, one could fork this and try to port.....

Not certain what you are trying to do, but another option if you are trying to do pairwise comparisons is leverage other libraries.

For example:

ggcorplot by Mike Lawrence at Dalhousie University back in 2011 (but 4 years newer than pairs.panels.R) uses ggplot2.

library(ggplot2)

#define a helper function (borrowed from the "ez" package)
ezLev=function(x,new_order){
    for(i in rev(new_order)){
        x=relevel(x,ref=i)
    }
    return(x)
}

ggcorplot = function(data,var_text_size,cor_text_limits){
    # normalize data
    for(i in 1:length(data)){
        data[,i]=(data[,i]-mean(data[,i]))/sd(data[,i])
    }
    # obtain new data frame
    z=data.frame()
    i = 1
    j = i
    while(i<=length(data)){
        if(j>length(data)){
            i=i+1
            j=i
        }else{
            x = data[,i]
            y = data[,j]
            temp=as.data.frame(cbind(x,y))
            temp=cbind(temp,names(data)[i],names(data)[j])
            z=rbind(z,temp)
            j=j+1
        }
    }
    names(z)=c('x','y','x_lab','y_lab')
    z$x_lab = ezLev(factor(z$x_lab),names(data))
    z$y_lab = ezLev(factor(z$y_lab),names(data))
    z=z[z$x_lab!=z$y_lab,]
    #obtain correlation values
    z_cor = data.frame()
    i = 1
    j = i
    while(i<=length(data)){
        if(j>length(data)){
            i=i+1
            j=i
        }else{
            x = data[,i]
            y = data[,j]
            x_mid = min(x)+diff(range(x))/2
            y_mid = min(y)+diff(range(y))/2
            this_cor = cor(x,y)
            this_cor.test = cor.test(x,y)
            this_col = ifelse(this_cor.test$p.value<.05,'<.05','>.05')
            this_size = (this_cor)^2
            cor_text = ifelse(
                this_cor>0
                ,substr(format(c(this_cor,.123456789),digits=2)[1],2,4)
                ,paste('-',substr(format(c(this_cor,.123456789),digits=2)[1],3,5),sep='')
            )
            b=as.data.frame(cor_text)
            b=cbind(b,x_mid,y_mid,this_col,this_size,names(data)[j],names(data)[i])
            z_cor=rbind(z_cor,b)
            j=j+1
        }
    }
    names(z_cor)=c('cor','x_mid','y_mid','p','rsq','x_lab','y_lab')
    z_cor$x_lab = ezLev(factor(z_cor$x_lab),names(data))
    z_cor$y_lab = ezLev(factor(z_cor$y_lab),names(data))
    diag = z_cor[z_cor$x_lab==z_cor$y_lab,]
    z_cor=z_cor[z_cor$x_lab!=z_cor$y_lab,]
    #start creating layers
    points_layer = layer(
        geom = 'point'
        , data = z
        , mapping = aes(
            x = x
            , y = y
        )
    )
    lm_line_layer = layer(
        geom = 'line'
        , geom_params = list(colour = 'red')
        , stat = 'smooth'
        , stat_params = list(method = 'lm')
        , data = z
        , mapping = aes(
            x = x
            , y = y
        )
    )
    lm_ribbon_layer = layer(
        geom = 'ribbon'
        , geom_params = list(fill = 'green', alpha = .5)
        , stat = 'smooth'
        , stat_params = list(method = 'lm')
        , data = z
        , mapping = aes(
            x = x
            , y = y
        )
    )
    cor_text = layer(
        geom = 'text'
        , data = z_cor
        , mapping = aes(
            x=y_mid
            , y=x_mid
            , label=cor
            , size = rsq
            , colour = p
        )
    )
    var_text = layer(
        geom = 'text'
        , geom_params = list(size=var_text_size)
        , data = diag
        , mapping = aes(
            x=y_mid
            , y=x_mid
            , label=x_lab
        )
    )
    f = facet_grid(y_lab~x_lab,scales='free')
    o = opts(
        panel.grid.minor = theme_blank()
        ,panel.grid.major = theme_blank()
        ,axis.ticks = theme_blank()
        ,axis.text.y = theme_blank()
        ,axis.text.x = theme_blank()
        ,axis.title.y = theme_blank()
        ,axis.title.x = theme_blank()
        ,legend.position='none'
    )
    size_scale = scale_size(limits = c(0,1),to=cor_text_limits)
    return(
        ggplot()+
        points_layer+
        lm_ribbon_layer+
        lm_line_layer+
        var_text+
        cor_text+
        f+
        o+
        size_scale
    )
}

#set up some fake data
library(MASS)
N=100

#first pair of variables
variance1=1
variance2=2
mean1=10
mean2=20
rho = .8
Sigma=matrix(c(variance1,sqrt(variance1*variance2)*rho,sqrt(variance1*variance2)*rho,variance2),2,2)
pair1=mvrnorm(N,c(mean1,mean2),Sigma,empirical=T)

#second pair of variables
variance1=10
variance2=20
mean1=100
mean2=200
rho = -.4
Sigma=matrix(c(variance1,sqrt(variance1*variance2)*rho,sqrt(variance1*variance2)*rho,variance2),2,2)
pair2=mvrnorm(N,c(mean1,mean2),Sigma,empirical=T)

my_data=data.frame(cbind(pair1,pair2))

ggcorplot(
    data = my_data
    , var_text_size = 30
    , cor_text_limits = c(2,30)
)

Example usage and output:

ggcorplot(
  data = iris[1:4],
  var_text_size = 5,
  cor_text_limits = c(5,10))

yields

hyphen length depends on device type (pdf, png) - how can I get short hyphens using pdf?

Try the cairo-based PDF device from the grDevices package (cairo_pdf):

library("grid")
txt <- "a dash-in between"

cairo_pdf("test.pdf")
grid.text(txt)
dev.off()

Check capabilities() to see if cairo is supported on your system.

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