We provide here an R function that draws a grid of plots, revealing relationships between the variables in a dataset and a given target variable.

Scatterplots in the grid include regression lines, loess curves and the adjusted R-squared statistic.

Boxplots have points indicating the group means. Box widths are proportional to the square-root of the number of observations in the relevant group. The p-value is shown for an F-test: p < 0.05 indicates a significant difference between the means of the groups. But don't take this p-value on faith: Be sure to check the assumptions of the one-way ANOVA model.

Mosaic plots include the p-value of a chi-square test of independence: p < 0.05 indicates that there is a significant relationship between the two variables under consideration. The number of plot cells with a count under five is shown; if this is greater than zero, the chi-square test may be invalid. Here's an example using a continuous target variable:

```
mtcars2 <- mtcars
mtcars2$cyl <- as.factor(mtcars2$cyl)
mtcars2$vs <- as.factor(mtcars2$vs)
mtcars2$am <- as.factor(mtcars2$am)
mtcars2$gear <- as.factor(mtcars2$gear)
mtcars2$carb <- as.factor(mtcars2$carb)
multiplot(mtcars2, 'disp', c(2, 5))
```

This example has a categorical target variable:

```
multiplot(mtcars2, 'gear', c(2, 5))
```

Finally, here’s the multiplot function:

```
multiplot <- function(df_data, y_column, mfrow=NULL){
#
# Plots the data in column y_column of df_data against every other column in df_data, a dataframe.
# By default the plots are drawn next to each other (i.e. in a row). Use mfrow to overide this. E.g. mfrow=c(2, 3).
#
# Set the layout
if (is.null(mfrow)) mfrow <- c(1, ncol(df_data) - 1)
op <- par(mfrow=mfrow, mar=c(5.1, 4.1, 1.1, 1.1), mgp = c(2.2, 1, 0))
on.exit(par(op))
for (icol in which(names(df_data) != y_column)){
x_column <- names(df_data)[icol]
y_x_formula <- as.formula(paste(y_column, "~", x_column))
x_y_formula <- as.formula(paste(x_column, "~", y_column))
x <- df_data[[x_column]]
y <- df_data[[y_column]]
subtitle <- ""
if (is.factor(x)){
if (is.factor(y)){
# Mosaic plot.
tbl <- table(x, y)
chi_square_test_p <- chisq.test(tbl)$p.value
problem_cell_count <- sum(tbl < 5)
subtitle <- paste("Chi-Sq. Test P:", round(chi_square_test_p, 3)," (< 5 in ", problem_cell_count, " cells.)")
plot(y_x_formula, data=df_data)
} else {
# Vertical boxplot.
fit <- aov(y_x_formula, data=df_data)
f_test_p <- summary(fit)[[1]][["Pr(>F)"]][[1]]
subtitle <- paste("F-Test P:", round(f_test_p, 3))
boxplot(y_x_formula, data=df_data, horizontal=FALSE, varwidth=TRUE)
means <- tapply(y, x, function(z){mean(z, na.rm=TRUE)})
points(x=means, col="red", pch=18)
}
} else {
if (is.factor(y)){
# Horizontal boxplot.
fit <- aov(x_y_formula, data=df_data)
f_test_p <- summary(fit)[[1]][["Pr(>F)"]][[1]]
subtitle <- paste("F-Test P:", round(f_test_p, 3))
boxplot(x_y_formula, data=df_data, horizontal=TRUE, varwidth=TRUE)
means <- tapply(x, y, function(z){mean(z, na.rm=TRUE)})
points(x=means, y=1:length(levels(y)), col="red", pch=18)
} else {
# Scatterplot with straight-line regression and lowess line.
adj_r_squared <- summary(lm(y_x_formula, df_data))$adj.r.squared
subtitle <- paste("Adj. R Squared:", round(adj_r_squared, 3))
plot(y_x_formula, data=df_data, pch=19, col=rgb(0, 0, 0, 0.2))
abline(lm(y_x_formula, data=df_data), col="red", lwd=2)
lines(lowess(x=x, y=y), col="blue", lwd=2)
}
}
title(sub=subtitle, xlab=x_column, ylab=y_column)
}
}
```