Bar Plot with 95% Conficence Interval Error Bars in R

R is a great plotting program for making publication quality bar plots of your data.  I often need to make quick bar plots to help collaborators quickly visualize data, so I thought I would put together a very generalized script that can be modified and built on as a template to make future bar plots in R.

In this script you manually enter your data (as you would see it in a 2x2 table) and then calculate the estimated frequency and 95% CI around that frequency using the binom.confint function in the binom package.  Next, a parametric and non-parametric p-value is calculated with the binom.test and fisher.test commands, respectively.  These statistics are then plotted using the R's barplot function.  The example code is below along with what the plot will look like.  P-values and N's are automatically filled and the y limits are calculated to ensure the graph includes all the plotted details.

	# Input Data
	a=35 # Exposed Cases
	b=500 # Unexposed Cases
	c=10 # Exposed Controls
	d=490 # Unexposed Controls
	# Make 2x2 Table
	data=
	matrix(c(a, c, b, d),
	nrow = 2,
	dimnames = list(Case=c("Yes", "No"),
	Exposed=c("Yes", "No")))
	data
	# Calculate 95% CI around estimate
	library(binom)
	cases <- binom.confint(a,a+b, methods=c("asymptotic"))
	controls <- binom.confint(c,c+d, methods=c("asymptotic"))
	estimates <- as.data.frame(rbind(cases,controls))
	# Conduct association tests
	f=fisher.test(data)
	c=chisq.test(data)
	# Make barplot
	b=barplot(estimates$mean, main="Differences in Frequency",
	xlab="", names.arg=c(paste("Cases (N=",estimates$n[1],")",sep=""),paste("Controls (N=",estimates$n[2],")",sep="")),
	ylab="Frequency", ylim=c(0,max(estimates$upper)*1.05),
	border=c("white","white"))
	arrows(x0=b,y0=estimates$lower,x1=b,y1=estimates$upper, angle=90, code=3)
	mtext(paste("Pearson p-value=",round(c$p.value,4)),3, line=-2)
	mtext(paste("Fisher p-value=",round(f$p.value,4)),3, line=-3)
	box()

view raw barplot_template.R hosted with ❤ by GitHub