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

I'm using R's psych library and I'm plotting correlation pairs in R.

I would like to save the plot generated by this function and export it, in word document for example using ReporteRs but I can't do that. This question have been already discussed here.

As I dig a bit into why I was failing to export it I realized that writing this in R:

plot <- pairs.panel(...)

was giving me when printing plot :NULL

So it appears that whatever is the object generated by pairs.panels it can be stored in a variable or be re-used to be exported in a report.

As a workaroung I am using png() to store the plot in a image, and then import the image and inserting it in the report... It is inefficient and much slower so any workaround would be helpful Thanks,

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