In Part 8, let’s look at some basic commands in R. Set up the following vectors by cutting and pasting from this document: a <- c(3,-7,-3,-9,3,-1,2,-12, -14) b <- c(3,7,-5, 1, 5,-6,-9,16, -8) d <- c(1,2,3,4,5,6,7,8,9) Now figure out what each of the following commands do. You should not need me to explain each command, …

In Part 7, let’s look at further plotting in R. Try entering the following three commands to create three variables. X <- c(3, 4, 6, 6, 7, 8, 9, 12) B1 <- c(4, 5, 6, 7, 17, 18, 19, 22) B2 <- c(3, 5, 8, 10, 19, 21, 22, 26) Graph B1 using a y …

In Part 6, let’s look at basic plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line). x <- seq(-4, 4, 0.2) ;  y <- 2*x^2 + 4*x – 7 plot(x, y) This is a very basic plot, but we can do much …

In Parts 3 and 4 we used the lm() command to perform least squares regressions. We saw how to check for non-linearity in our data by fitting polynomial models and checking whether they fit the data better than a linear model. Now let’s see how to fit an exponential model in R. As before, we …

In Part 3 we used the lm() command to perform least squares regressions. In Part 4 we will look at more advanced aspects of regression models and see what R has to offer. One way of checking for non-linearity in your data is to fit a polynomial model and check whether the polynomial model fits …

In Part 2 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(186, 165, 149, 206, 143, 187, 191, 179, 162, 185) weight = c(89, 56, …

In Part 1 we installed R and used it to create a variable and summarise 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) …

Many of you have heard of R (the R statistics language and environment for scientific and statistical computing and graphics). Perhaps you know that it uses command line input rather than pull-down menus. Perhaps you feel that this makes R hard to use and somewhat intimidating! Indeed, R has a longer learning curve than other …