Part 3 Meet our toolbox!

You can follow along with the slides here if they do not appear below. I recommend installing R, Rstudio, git, and github before starting the Bechdal activity

3.1 R and RStudio

3.1.1 Install R and RStudio

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  • Install R, a free software environment for statistical computing and graphics from CRAN, the Comprehensive R Archive Network. I highly recommend you install a precompiled binary distribution for your operating system – use the links up at the top of the CRAN page linked above!

  • Install RStudio’s IDE (stands for integrated development environment), a powerful user interface for R. Get the Open Source Edition of RStudio Desktop.

    • You can run either the Preview version or the official releases available here.
    • RStudio comes with a text editor, so there is no immediate need to install a separate stand-alone editor.
    • RStudio can interface with Git(Hub). However, you must do all the Git(Hub) set up described elsewhere before you can take advantage of this.

If you have a pre-existing installation of R and/or RStudio, I highly recommend that you reinstall both and get as current as possible. It can be considerably harder to run old software than new.

  • When you upgrade R, you generally also need to update any packages you have installed.

3.1.2 Testing testing

  • Do whatever is appropriate for your OS to launch RStudio. You should get a window similar to the screenshot you see here, but yours will be more boring because you haven’t written any code or made any figures yet!

  • Put your cursor in the pane labeled Console, which is where you interact with the live R process. Create a simple object with code like x <- 3 * 4 (followed by enter or return). Then inspect the x object by typing x followed by enter or return. You should see the value 12 print to screen. If yes, you’ve succeeded in installing R and RStudio.

3.1.3 Add-on packages

R is an extensible system and many people share useful code they have developed as a package via CRAN and GitHub. To install a package from CRAN, for example the dplyr package for data manipulation, here is one way to do it in the R console (there are others).

install.packages("dplyr", dependencies = TRUE)

By including dependencies = TRUE, we are being explicit and extra-careful to install any additional packages the target package, dplyr in the example above, needs to have around.

You could use the above method to install the following packages, all of which we will use:

3.1.4 Further resources

The above will get your basic setup ready but here are some links if you are interested in reading a bit further.