I need to analyze some data about internet sessions for a DSL Line. I wanted to have a look at how the session durations are distributed. I figured a simple way to do this would be to begin by making a probability density plot of the duration of all the sessions.
I have loaded the data in R and used the density()
function. So, it was something like this
plot(density(data$duration), type = "l", col = "blue", main = "Density Plot of Duration",
xlab = "duration(h)", ylab = "probability density")
I am new to R and this kind of analysis. This was what I found from going through google. I got a plot but I was left with some questions. Is this the right function to do what I am trying to do or is there something else?
In the plot I found that the Y-axis scale was from 0...1.5. I don't get how it can be 1.5, shouldn't it be from 0...1?
Also, I would like to get a smoother curve. Since, the data set is really large the lines are really jagged. It would be nicer to have them smoothed out when I am presenting this. How would I go about doing that?
As nico said, you should check out hist
, but you can also combine the two of them. Then you could call the density with lines
instead.
Example:
duration <- rpois(500, 10) # For duration data I assume Poisson distributed
hist(duration,
probability = TRUE, # In stead of frequency
breaks = "FD", # For more breaks than the default
col = "darkslategray4", border = "seashell3")
lines(density(duration - 0.5), # Add the kernel density estimate (-.5 fix for the bins)
col = "firebrick2", lwd = 3)
Should give you something like:
Note that the kernel density estimate assumes a Gaussian kernel as default. But the bandwidth is often the most important factor. If you call density
directly it reports the default estimated bandwidth:
> density(duration)
Call:
density.default(x = duration)
Data: duration (500 obs.); Bandwidth 'bw' = 0.7752
x y
Min. : 0.6745 Min. :1.160e-05
1st Qu.: 7.0872 1st Qu.:1.038e-03
Median :13.5000 Median :1.932e-02
Mean :13.5000 Mean :3.895e-02
3rd Qu.:19.9128 3rd Qu.:7.521e-02
Max. :26.3255 Max. :1.164e-01
Here it is 0.7752. Check it for your data and play around with it as nico suggested. You might want to look at ?bw.nrd
.