Last time we created two variables and used the lm() command to perform a least squares regression on them, and diagnosing our regression using the plot() command. Here are the data again. height = c(176, 154, 138, 196, 132, 176, 181, 169, 150, 175) bodymass = c(82, 49, 53, 112,…

Last time we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again. height = c(176, 154, 138, 196, 132, 176, 181, 169, 150, 175)…

In my last blog we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again. height = c(176, 154, 138, 196, 132, 176, 181, 169,…

Today let’s re-create two variables and see how to plot them and include a regression line. 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

## Quick start with R: Symbol colours and legend in qplot (Part 25)

In Blog 24 we saw how to use qplot to map symbol size to a categorical variable. Now we see how to control symbol colours and create legend titles. Copy in the same dataset of Blog 24 (a medical data set relating to patients in a randomised controlled trial: M

In Blog 24, let’s see how to use qplot to map symbol colour to a categorical variable. Copy in the following dataset (a medical dataset relating to patients in a randomised controlled trial): M

In Part 23, let’s see how to use qplot to create a simple scatterplot. The qplot (quick plot) system is a subset of the ggplot2 (grammar of graphics) package which you can use to create nice graphs. It is great for creating graphs of categorical data, because you can map…