rstuff: Utilities and stuff

### ggpairs_custom.R --- 
## Filename: ggpairs_custom.R
## Description: 
## Author: Noah Peart
## Created: Mon Feb  1 15:19:56 2016 (-0500)
## Last-Updated: Mon Feb  1 15:20:00 2016 (-0500)
##           By: Noah Peart
######################################################################
library(GGally)
data(iris)

library(ggplot2)

## First create combinations of variables and extract those for the lower matrix
cols <- expand.grid(names(iris)[1:4], names(iris)[1:3])    
cols <- cols[c(2:4, 7:8, 12),]  # indices will be in column major order

## These parameters are applied to each plot we create
pars <- list(geom_point(alpha=0.8, color="blue"),              
             geom_smooth(method="lm", color="red", lwd=1.1))

## Create the plots (dont need the lower plots in the ggpairs call)
plots <- apply(cols, 1, function(cols)                    
    ggplot(iris[,cols], aes_string(x=cols[2], y=cols[1])) + pars)
gg <- ggpairs(iris[, 1:4],
              diag=list(continuous="bar", params=c(colour="blue")), 
              upper=list(params=list(corSize=6)), axisLabels='show')

## Now add the new plots to the figure using putPlot
colFromRight <- c(2:4, 3:4, 4)                                    
colFromLeft <- rep(c(1, 2, 3), times=c(3,2,1))
for (i in seq_along(plots)) 
    gg <- putPlot(gg, plots[[i]], colFromRight[i], colFromLeft[i])
gg

## If you want the slope of your lines to correspond to the 
## correlation, you can scale your variables
scaled <- as.data.frame(scale(iris[,1:4]))
fit <- lm(Sepal.Length ~ Sepal.Width, data=scaled)
coef(fit)[2]
# Sepal.Length 
#  -0.1175698 


################################################################################
##
##                            Generalized Version
##
################################################################################
## colInds is indices of columns in data.frame
.ggpairs <- function(colInds, data=iris, pars=pars) {
    require(ggplot2)
    require(GGally)
    n <- length(colInds)
    cols <- expand.grid(names(data)[colInds], names(data)[colInds])
    cInds <- unlist(mapply(function(a, b, c) a*n+b:c, 0:max(0,n-2), 2:n, rep(n, n-1)))
    cols <- cols[cInds,]  # indices will be in column major order

    ## These parameters are applied to each plot we create
    pars <- list(geom_point(alpha=0.8, color="blue"),              
                 geom_smooth(method="lm", color="red", lwd=1.1))

    ## Create the plots (dont need the lower plots in the ggpairs call)
    plots <- apply(cols, 1, function(cols)                    
        ggplot(data[,cols], aes_string(x=cols[2], y=cols[1])) + pars)
    gg <- ggpairs(data[, colInds],
                  diag=list(continuous="bar", params=c(colour="blue")), 
                  upper=list(params=list(corSize=6)), axisLabels='show')

    rowFromTop <- unlist(mapply(`:`, 2:n, rep(n, n-1)))
    colFromLeft <- rep(1:(n-1), times=(n-1):1)
    for (i in seq_along(plots)) 
        gg <- putPlot(gg, plots[[i]], rowFromTop[i], colFromLeft[i])
    return( gg )
}

.ggpairs(1:3)