One thing to keep in mind is that you can read specific rows and specific columns.
So for example, if you wanna read just the second and third columns and
you wanna read just the first through fourth rows, you can actually pass those
as variables to colIndex and rowIndex to this file read.xlsx.
And you'll actually only read a subset of the Excel file which might be
useful if you only wanna abstract a little part of the file that you have.
So some further notes that might be useful for you are that you can use write.xlsx.
So if you're working with people that like Excel files,
you can actually write them back out after your analysis.
Share them with people.
It's very similar and you pass it the object that you want to write out and
the file name and it will write that file out.
read.xlsx2 I found is quite a bit faster than read.xlsx.
But especially if you're reading subsets of rows,
it might be a little bit unstable at least in my experience.
XLConnect, as I mentioned, is a lot more flexible for reading, writing and
manipulating Excel files.
And so if you really need to do a lot of serious processing of Excel files,
I've found that XLConnect might be a little bit better.
If you're gonna be doing that,
the XL Connect vignette is actually a really great place to start.
It has lot of information about how you can do all sorts of different things.
How you can manipulate and create files directly from R.
In general, and for the purposes of this class but
also the purposes of most analyses it's a little bit faster and easier to
read files if you store them as comma separated, or tab separated flat files.
They're also a little bit easier to distribute, as not everybody has Excel.
It's not necessarily as cross-platform as using something
like a plain text file, with just comma separated or tab separated values.