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After working in
R, you often want

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to save your work so that you
can easily re-run commands

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and rebuild models.

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There are several
ways of doing this,

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but the method we
recommend is by using

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what's called a script file.

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Let's open a new script file.

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Go to File, New Document, on
a Mac, and File, New Script,

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on a PC.

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This will pop up a
blank script file.

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You can copy and
paste any commands

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that you've run in your
console into the script file.

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For example, say we want to
save the table commands we ran.

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Let's Copy the
first table command

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and then Paste it
into the script file.

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We can also take the
first tapply command

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we ran, Copy and Paste
it into our script file.

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Now, if you highlight
these lines of code

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in the script file and press
Command-Enter on a Mac,

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and Control-r on a PC, it
will run these lines of code

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in your R console.

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So if you save this
file and then reopen it

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when you start R next time, you
can easily rerun any commands

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that you ran in this R session.

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A lot of times it's
easier to just write

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commands in your script file
and run them in your console

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because then they'll always
be saved in your script file.

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You can add comments
in your script file

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by using the pound sign.

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If you type pound,
and then some comment

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describing your script file, and
then highlight the whole thing

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and press Control-r
or Command-Enter,

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you'll see that the comment
runs in the console,

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but nothing happens.

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R recognizes this as a comment.

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If you want to save the
output of your R session

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in your console, I recommend
just saving it as a text file.

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In your console, you can
go to file, Save to file,

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and this will save your
output as a text file.

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You won't be able
to easily rerun

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this in R like the
script file, but it

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will have the summary
output of what you did.

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When you close R,
it will ask you

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if you want to save
your workspace.

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Make sure you have everything
you want in a script file

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and then don't worry about
saving your workspace.

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These are the basics
of R. We'll see

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a lot more in the
rest of this class.

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But hopefully you've
seen in this lecture

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how powerful R can be.

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In just one lecture, we
performed insightful data

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analysis on an
interesting data set.