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First let’s start with using the ’t test’ to compare means of some of the data we’ve been working on. test()’ function. To try this out, load the ’MASS’ library and the ’quine’ data. We’ll compare the mean number of days absent by various characteristics, starting with sex. The first example is of a one-sample test. mean <- mean(quine$Days[quine$Sex=="F"])’. mean)’. The first argument is a vector of the data for boys and the second argument is the mean value that is the reference. The results of the t test are printed out at the prompt.

This is easy to in R since everything is stored as an object. par() is just a list so it can be assigned to an object by just typing "opar <- par()". Then use "par(opar)" to set the values of par to the values of the opar list. All graphical data must be sent to a graphical device. When you plot() some data, the default device opened by R is a window. A window is simply the plot window that we have been using in this lesson. A new window with no data can be created with "windows()". But other graphical devices can receive data.

We can then apply functions to the resulting list of data frames with the lapply() function. For example: "lapply(x, function(x) mean(x[,2]))" will return the mean calories of all the cereals produced by manufacturer. UNIQUE & DUPLICATED Sometimes it is necessary to find the unique values of a data set. The unique() function returns all the unique values of a vector. Thus, "unique(UScereal[,4])" will return all the unique values of fat content. The unique() function returns a vector, so you can then apply other functions to it if necessary.

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R for data analysis and modeling by Gregor, Staber

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