Learning Statistics with R - A tutorial for psychology students and other beginners, 2018a

An alternative method is to use the who() function, as long as you tell it to look at the variables inside
data frames. If you set expand = TRUE then it will not only list the variables in the workspace, but it will
“expand” any data frames that you’ve got in the workspace, so that you can see what they look like.
That is:
> who(expand = TRUE)
-- Name -- -- Class -- -- Size --
expt data.frame 9 x 4
$age numeric 9
$gender factor 9
$group factor 9
$score numeric 9
or, since expand is the first argument in the who() function you can just type who(TRUE). I’ll do that a lot
in this book.
4.8.4 Looking for more on data frames?
There’s a lot more that can be said about data frames: they’re fairly complicated beasts, and the
longer you use R the more important it is to make sure you really understand them. We’ll talk a lot more
about them in Chapter 7.
4.9
Lists
ThenextkindofdataIwanttomentionarelists. Lists are an extremely fundamental data structure
in R, and as you start making the transition from a novice to a savvy R user you will use lists all the
time. I don’t use lists very often in this book – not directly – but most of the advanced data structures
in R are built from lists (e.g., data frames are actually a specific type of list). Because lists are so
important to how R stores things, it’s useful to have a basic understanding of them. Okay, so what is a
list, exactly? Like data frames, lists are just “collections of variables.” However, unlike data frames –
which are basically supposed to look like a nice “rectangular” table of data – there are no constraints on
what kinds of variables we include, and no requirement that the variables have any particular relationship
to one another. In order to understand what this actually means, the best thing to do is create a list,
which we can do using the list() function. If I type this as my command:
> Dan print( Dan )
$age
[1] 34
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