Here's a simple but useful function for quickly reviewing distributions and missing data. Suitable for preliminary data QC and exploratory analysis.

qc_plots.R

# (c) 2013 dwt | terminus data science, LLC
# freely licensed for non-commercial use

qc.plots <- function(df, max.levels=25, ask=TRUE, col="white")
{
    old.par <- par(ask=ask)
    on.exit(par(old.par))

    for (var in names(df)) {
        vals <- df[[var]]

        if (is.numeric(vals)) {
            n.missing <- sum(is.na(vals))
            pct.missing <- n.missing / length(vals) * 100

            hist(vals, main=var, col=col)

            if (n.missing != 0) {
                legend("topright",
                       paste("# missing: ", n.missing, "\n",
                             "% missing: ", format(pct.missing, digits=3),
                             sep=""),
                       bty="n",
                       text.col="red")
            }
        } else {
            with.NAs <- factor(vals, exclude=NULL)
            n.levels <- length(attr(with.NAs, "levels"))
            cols <- rep(col, n.levels)

            if (any(is.na(vals))) {
                attr(with.NAs, "levels")[n.levels] <- "<missing>"
                cols[n.levels] <- "red"
            }

            if (n.levels > max.levels) {
                plot(1, type="n", axes=F, xlab="", ylab="", main=var)
                legend("top", paste("too many levels:", n.levels), text.col="red", bty="n")
                legend("center", paste(attr(with.NAs, "levels"), "\n", sep=""), bty="n")
            } else {
                barplot(table(with.NAs), main=var, ylab="Frequency", col=cols)
            }
        }
    }
}