[BLANK_AUDIO] Tapply is a very useful function and it's used to apply a function over subsets of a vector. So the idea is basically, imagine you have a vector usually it's going to to be numbers, so a numeric vector. And there are, there are pieces of this vector that you want to calculate a summary statistic over. So and so you, you're going to have to have another variable or another object which identifies which element of this, of your numeric vector belongs to which group. The idea is that for each group in the numeric vector you're going to calculate a summary statistic like a mean, or a standard deviation, or whatever. So the basic idea behind tapply is that the first argument is a numeric vector or a vector of some sort. The second argument is, is another vector of the same length which identifies which group each element of the numeric vector is in. So for example, suppose there are two groups suppose you have men and women, for example, in two groups, and maybe the first 50 observations are men and the second 50 observations are women. Then you need to have a factor variable which indicates, you know, which, which observations are men and which, which are women. And so if you want to take the, the, the mean of the numeric factor within men or within women, then you can use tapply to do this. So FUN is the function that you want to apply and so this is going to be the same as before. It's going to be the, either a function or you can pass in an ano-, an anonymous function. And then the dot, dot, dot contains the other arguments that may go to this function. And then the simplify argument indicates whether you want to simplify the argu-, simplify the results, kind of like the sapply simplification. So, here's a very simple example. I'm simulating a vector of normal random variables and uniform random variables and, and there's ten normals, ten uniforms, then ten normals that have a mean of one. So you can think of this vector as having three groups. So then I'm going to create another factor variable using the gl function, and its going to be, this factor variable going to have three levels. And each level is going to be repeated ten times. So when I print out the factor variable here, you can see that there's ten ones, ten twos, and there's ten threes. So each, so that the factor variable indicates kind of which group the, the observation is in. So now I can, I can, tapply on x, pass it the factor variable f in the mean function, that allows me to take the mean of each group of numbers in, in x. [BLANK_AUDIO] If you don't simplify the result, then what you get back is going to be a list. So so tapply applied for the same numeric vector and factor as on the previous slide. I want to calculate the mean and say simplify equals false then I get back a list with three elements and e in each element is the mean of that subgroup. I, I can pass at slightly more complicated summary statistics. So here, instead of calculating the mean, which, which returns one number I'm going to calculate the range of observations. So this gives me the min and the max of the observations within that subset of the vector x, and so here I'm getting a list where each element is a vector of length 2.
