Visualization methods in R to examine correlation of labels against response

Question

Question

What are some good plotting methods in R for examining the relationship between a target variable and various explanatory variables? In particular, I'm looking for visualization techniques that scale to more variables than the traditional scatterplot matrix.

More details

The scatterplot matrix is a great tool for visualizing pairwise relationships between variables. For example, with the swiss dataset in R, we can easily plot a matrix of scatterplots.

library(datasets)
data(swiss)
plot(swiss[1:3])

which yields

I am interested in the case where I want to predict some response, say Fertility using some combination of explanatory variables. I want to closely examine how each explanatory variable correlates with Fertility. If I have many columns in my dataframe, using plot(swiss) becomes unwieldy.

For example, the following plot (generated following instructions here) shows pairwise correlations for all columns in a dataframe. If I could plot something like this but only showing correlations between Fertility and other columns, that would be useful.

library(datasets)
data(swiss)
plot(swiss[1:3])

library(devtools)
library(inspectdf)
library(tidyverse)
library(readr)

show_plot(inspect_cor(swiss))

which yields

Answer 1

Below are two functions using my favorite packages:

• The first one shows a scatterplot of every column against the target column
• The second one shows the correlation of every column with the target column, with confidence intervals (I found how to do that with ggplot here).

Code:

library(ggplot2)
library(reshape2)
library(plyr)

scatterplot <- function(data, targetColumn='Fertility') {
  d<-melt(data,id.vars = targetColumn)
#  ggplot(d, aes_string('value',targetColumn))+geom_point()+facet_grid(variable~.)
  ggplot(d, aes_string('value',targetColumn))+geom_point()+facet_wrap(variable~.)
}


corplotCI <- function(data, targetColumn='Fertility', method='pearson') {
  d<-ldply(colnames(data), function(col) {
    if (col != targetColumn) {
      r <- cor.test(data[,col], data[,targetColumn],method=method)
      data.frame(variable=col,cor=r$estimate, lowerCI=r$conf.int[1],upperCI=r$conf.int[2])
    }
  })
  ggplot(d,aes(cor,variable))+geom_point(size=3)+geom_errorbarh(aes(xmin = lowerCI,xmax = upperCI),height=.5)+coord_cartesian(xlim=c(-1,1))
}

Usage:

scatterplot(swiss)

corplotCI(swiss)