We can also write comments in-line with our code, like this: It is often better to simply start a new section of code to tinker with until you get it working as expected, rather than commenting out individual lines of code. To comment out a line of code, you can place a pound sign (also called an octothorpe) # in front of the line of code that you want to exclude when you’re running your script.īe careful when excluding certain lines of code, especially in longer files, as it can be easy to forget where you’ve commented out code. Moreover, writing comments in your code as you work through the examples in this book is a great way to help reinforce what you’re learning.Ĭomments are ignored by R when running a script, so they will not affect your code or analysis. R script.Įven if you are the only person to ever work on your code, it can be helpful to write yourself notes about what you were trying to do with a specific piece of code. It is considered good practice to comment your code when working in an. R script in the source pane by going to “File”, selecting “New File”, and then selecting “R Script”: This pane contains the Stata equivalents of the Plots Manager and Project Manager windows.Īs you work with R more, you’ll find yourself using the tabs within each of the panes. To learn more about packages see Section 20. The Packages pane allows you to see, install, update, delete, load/unload R packages, and see which version of the package you have. The Files pane is a browser which can be used to open or delete files. The Help pane can display documentation and help files. Interactive or HTML outputs will display in the Viewer pane. Typical plot graphics including maps will display in the Plot pane. The lower-right pane includes several important tabs. It also has a “Connections” pane for external connections, and can have a “Git” pane if you choose to interface with Github. It also has a “Tutorial” tab where you can complete interactive R tutorials if you have the learnr package installed. This pane also contains History where you can see commands that you can previously. In Stata, this is most similar to the Variables Manager window. You can click on the arrow next to a data frame name to see its variables. These objects could include imported, modified, or created datasets, parameters you have defined, or vectors or lists you have defined during analysis (e.g. names of regions). This pane, by default in the upper-right, is most often used to see brief summaries of objects in the R Environment in the current session. If you are familiar with Stata, the R Console is like the Command Window and also the Results Window. You can directly enter and run commands in the R Console, but realize that these commands are not saved as they are when running commands from a script. This is where the commands are actually run and non-graphic outputs and error/warning messages appear. The R Console, by default the left or lower-left pane in R Studio, is the home of the R “engine”. This pane can also display datasets (data frames) for viewing.įor Stata users, this pane is similar to your Do-file and Data Editor windows. Scripts contain the commands you want to run. This pane, by default in the upper-left, is a space to edit, run, and save your scripts. If your RStudio displays only one left pane it is because you have no scripts open yet. We have chosen to use RStudio in this text in order to standardize the experience, but we encourage you to choose the IDE that best suits your needs! However we bring up alternative IDEs-particularly ESS-because RStudio, as of this writing, is not fully accessible for learners who utilize screen readers. This is a non-exhaustive list, and most of these options require a good deal of familiarity with a given IDE. EMACS Speaks Statistics (ESS) (https :///).IntelliJ IDEA (https :///plugin/6632-r-language-for-intellij).VisualStudio (https :///services/visual-studio-online/).You do not have to use RStudio to access R, and many people don’t! This means that RStudio comes with built-in features that make using R a little easier. RStudio interfaces directly with R, and is an Integrated Development Environment (IDE). Whenever we want to work with R, we’ll open RStudio.
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