In R, we can create a line plot using the same
plot() function by adding a plot type of “l”. This will plot the (x,y) paired observations and connect them with lines.
Let’s generate our own data for this lesson. We will use the
rnorm() function to generate a set of 100 random numbers that follow a normal distribution. These random numbers will be plotted on y-axis. x-axis will be a sequence of numbers from 1 to 100.
> x <- c(1:100) > rand_data <- rnorm(100, mean = 0) > data <- data.frame(x, rand_data) > plot(data$x, data$rand_data, type = "l", xlab="x", ylab = "Data")
The resulting line plot is displayed below:
You can change the appearance of the line by using the
- lty=”solid” or lty=1 (default)
- lty=”dashed” or lty=2
- lty=”dotted” or lty=3
- lty=”dotdash” or lty=4
- lty=”longdash” or lty=5
- lty=”twodash” or lty=6
- lty=”blank” or lty=0
The line width can be changed using the
lwd parameter. The default width is 1. So, you can plot a thicker line using a higher number.
The line color can be changed using the
Below we replot the line plot with a dotted line, thickness of 2, and in orange color.
> plot(data$x, data$rand_data, type = "l", xlab="x", ylab = "Data",lty=3,lwd=2,col="orange")
We know that numerical data generally conforms to a normal probability distribution characterized by a bell curve. In this exercise you are asked to create a bell curve using the normal data.
- Use the
rnorm()function to generate random numbers that follow a normal distribution (mean 0 and standard deviation of 1). Generate upto 5000 numbers. Store these numbers in a variable x.
- Plot the density of these random numbers using
plot(density(x))This will plot the bell curve.
- Try different values for mean and standard deviation and observe how the graph changes.
Note: You can also compute the Gaussian density using the
dnorm() function, for example
dnorm(x, mean = 0, sd = 1).