(This article was originally published at The Analysis Factor, and syndicated at StatsBlogs.)
by David Lillis, Phd.
In Part 1 we installed R and used it to create a variable and summarize it using a few simple commands. Today let’s re-create that variable and also create a second variable, and see what we can do with them.
As before, we take height to be a variable that describes the heights (in cm) of ten people. Copy and paste the following code to the R command line to create this variable.
height = c(176, 154, 138, 196, 132, 176, 181, 169, 150, 175)
Now let’s take weight to be a variable that describes the weights (in kg) of the same ten people. Copy and paste the following code to the R command line to create the weight variable.
weight = c(82, 49, 53, 112, 47, 69, 77, 71, 62, 78)
Both variables are now stored in the R workspace. To view them, enter:
height
weight
We can now create a simple plot of the two variables as follows:
plot(weight, height)
However, this is a rather simple plot and we can embellish it a little. Copy and paste the following code into the R workspace:
plot(weight, height, pch = 16, cex = 1.3, col = “red”, main = “MY FIRST PLOT USING R”, xlab = “WEIGHT (kg)”, ylab = “HEIGHT (cm)”)
In the above code, the syntax pch = 16 creates solid dots, while cex = 1.3 creates dots that are 1.3 times bigger than the default (where cex = 1). More about these commands later.
Now let’s perform a linear regression on the two variables by adding the following text at the command line:
lm(height, weight)
We see that the intercept is 98.0054 and the slope is 0.9528. By the way – lm stands for “linear model”.
Finally, we can add a best fit line to our plot by adding the following text at the command line:
abline(98.0054, 0.9528)
None of this was so difficult!
In Part 3 we will look again at regression and create more sophisticated plots.
About the Author: David Lillis has taught R to many researchers and statisticians. His company, Sigma Statistics and Research Limited, provides both on-line instruction and face-to-face workshops on R, and coding services in R. David holds a doctorate in applied statistics.
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