If you would like to return to information from the previous session, please click [here]((Knowledge-management-and-version-control.html).
By now, you should have already installed R
onto your
computer and R Studio
. For version control, we also need to
install git.
There are some individual R
packages that we will need
to install. These provide the base functionality for data manipulation,
visualisation and reporting for this training course.
This wiki page provides some background to the various components of
R
and R Studio
and some base instruction on
how to call packages from our library()
to get ready for
working on the data project itself.
For people new to R
, the interface can be somewhat
intimidating. Part of this has to do with the modular design of
R
, where there is a separation of code in the
editor and the console, which
basically brings the code “to life”. In R Studio
there is also a number of tabs that allow users to see files, plots
(i.e. output), objects in the environment, et
cetera:
In R Studio
, there are also a number of menus and
buttons that allow users to access routine functions, similar to Excel
or other software packages.
If you are using the stand-alone R
application, this can
be somewhat more confronting:
As there are few buttons or any really instructions on “what to do”.
And, for the more extreme, starting R
in a terminal
window is even more stark:
…with no buttons or anything obvious “to do”.
For this training course, we will be working with a mixed
model with the idea that course participants become familiar with
different ways to code and interact with R
. Most users will
find R Studio
provides a suitable platform for coding,
keeping track of the workspace environment, and graphical outputs.
However, the design of a data integration project should be
independent of the platform and the elegance of running base
R
in a terminal should not be ignored.
Now that we have the basic setup for R
, we can create a copy of the project
repository.