Creating a Line Chart in R

1> x <- c(1:100)
2> rand_data <- rnorm(100, mean = 0)
3> data <- data.frame(x, rand_data)
4> plot(data$x, data$rand_data, type = "l", xlab="x", ylab = "Data")
5

The resulting line plot is displayed below:

Line Type

You can change the appearance of the line by using the lty parameter.

• 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

Line Width

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.

Line Color

The line color can be changed using the col parameter.

Below we replot the line plot with a dotted line, thickness of 2, and in orange color.

1> plot(data$x, data$rand_data, type = "l", xlab="x", ylab = "Data",lty=3,lwd=2,col="orange")
2

Line Chart

Exercise

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).