Question

I have a data that gives the mean
and SD:

#info mean sd
info1 20.84 4.56
info2 29.18 5.41
info3 38.90 6.22

Actually there are more than 100 lines of this.
How can I plot normal distributions for each one of the line in one figure
given the above data?

Answer 1

Depending on how large N truly gets, you may want to split this up over a set of multiple charts. But, here’s the basic approach. First, you need to generate some random data according to your mean and sd. I chose 1000 random points, you can adjust as necessary. Next, set up a blank plot with the appropriate dimensions, then use lines and density to add the data. I used a for loop because it provided a nice way to specify the linetype for each data point. Finally, add a legend at the end:

dat <- read.table(text = "info mean sd
info1 20.84 4.56
info2 29.18 5.41
info3 38.90 6.22
", header = TRUE)

densities <- apply(dat[, -1], 1, function(x) rnorm(n = 1000, mean = x[1], sd = x[2]))
colnames(densities) <- dat$info

plot(0, type = "n", xlim = c(min(densities), max(densities)), ylim = c(0, .2))
for (d in 1:ncol(densities)){
  lines(density(densities[, d]), lty = d)
}
legend("topright", legend=colnames(densities), lty=1:ncol(densities))

Or, use ggplot2 which can have lots of benefits, namely it will specify reasonable xlim and ylim values for you automagically, and do sensible things with the legend without much fuss.

library(reshape2)
library(ggplot2)
#Put into long format
densities.m <- melt(densities)
#Plot
ggplot(densities.m, aes(value, linetype = Var2)) + geom_density()