23 RStudio Tips, Tricks, and Shortcuts for Data Science — Dataquest


1. Navigate Quickly Between Window Panes

RStudio window panes hold necessary details about your challenge accessible. Knowing find out how to toggle between panes with out touching your mouse to maneuver your cursor will save time and enhance your workflow. Use these shortcuts to immediately transfer between panes:

  • Control/Ctrl + 1: Source editor (your script)
  • Control/Ctrl + 2: Console
  • Control/Ctrl + 3: Help
  • Control/Ctrl + 4: History
  • Control/Ctrl + 5: Files
  • Control/Ctrl + 6: Plots
  • Control/Ctrl + 7: Packages
  • Control/Ctrl + 8: Environment
  • Control/Ctrl + 9: Viewer

If you favor to solely have one pane in view at a time, add Shift to any of the above instructions to maximise the pane. For instance, enter Control/Ctrl + Shift + 1 to maximise the R script, pocket book, or R Markdown file you’re working in.

(Side notice: The + we present within the shortcuts means “and”, so there’s no want to really kind the + key.)

But what if you wish to return to the usual four-pane view? No drawback! Enter Control/Ctrl + Shift + 0:

Four-Pane View

2. Keyboard Shortcuts

Knowing RStudio keyboard shortcuts will save a lot of time when programming. RStudio gives dozens of helpful shortcuts that you would be able to entry via the menu on the high: Tools > Keyboard Shortcuts Help.

Another option to entry RStudio keyboard shortcuts is with a shortcut! To entry shortcuts, kind Option + Shift + Okay on a Mac, or Alt + Shift + Okay on Linux and Windows.

Here are a few of our favourite RStudio shortcuts:

  • Insert the <- project operator with Option + - on a Mac, or Alt + - on Linux and Windows.
  • Insert the pipe operator %>% with Command + Shift + M on a Mac, or Ctrl + Shift + M on Linux and Windows.
  • Run the present line of code with Command + Enter on a Mac or Control + Enter on Linux and Windows.
  • Run all traces of code with Command + A + Enter on a Mac or Control + A + Enter on Linux and Windows.
  • Restart the present R session and begin recent with Command + Shift + F10 on a Mac or Control + Shift + F10 on Linux and Windows.
  • Comment or uncomment traces with Command + Shift + C on a Mac or Control + Shift + C on Linux and Windows.
  • Trying to recollect a command you submitted earlier? Search the command historical past from the Console with Command + [up arrow] on a Mac or Control + [up arrow] on Linux and Windows.

There are many extra helpful shortcuts out there, however by mastering the shortcuts above, you’ll be in your option to changing into an RStudio energy person!

Another nice useful resource for RStudio shortcuts is the official RStudio cheat sheet out there right here.

3. Save Time with Code Completion

After you start typing, a suggestion window will pop up with matching names of capabilities, objects, and snippets. You can toggle via the checklist utilizing the up or down arrows and hit return/Enter to make your choice.

Alternatively, you may make the most of a really cool function referred to as fuzzy matching, which lets you slender your search choices by coming into letters distinctive to the merchandise you’re matching. You don’t must enter the entire letters so long as your entry matches the order of the string.

Let’s check out how these code completion strategies work. First, we’ll choose the put in.packages() perform by typing a part of the perform identify, and then use arrows to make the choice. Next, we’ll use fuzzy matching to solely enter instd to slender our choice additional:

Code Completion

4. Quickly Find Files and Functions

In RStudio there’s no must fumble via your folder construction to seek out information, and there’s no must dig for capabilities! Enter the shortcut management/ctrl + . to open the Go to File/Function window and then use your fuzzy matching expertise to slender your choice:

Open File

5. Customize the Appearance

RStudio provides a wealth of choices to customise the looks to your liking. Under the RStudio tab, navigate to Preferences > Appearance to discover the numerous choices out there. A pleasant function of RStudio is that you would be able to shortly click on via the Editor theme window to preview every theme.

RStudio Theme

6. Easy Links to Documentation

Under the Help tab within the lower-right window, you’ll discover helpful hyperlinks to the web documentation for R capabilities and R packages. For instance, if we search for details about the set up.packages() perform utilizing the search bar, the official documentation is returned:

Help Pane

We may entry documentation within the Help tab by prepending a bundle or perform with ?, (e.g. ?set up.packages) and operating the command into the Console. With both strategy, RStudio auto-fills matching perform names as you kind!

7. Preview and Save Your Plots

Plots generated throughout an RStudio session are displayed below the Plots tab within the lower-right window. In this window, you may examine your plots by zooming in and out. If you wish to save your plot, it can save you the plot as a PDF or picture file.

Plot Pane

8. Import and Preview Datasets

RStudio makes it simple to import and preview datasets, no coding required! Under the Environment tab within the upper-right window, there’s function that lets you import a dataset. This function helps a wide range of codecs:

Import Data

You may even preview the dataset earlier than it’s loaded:

Import Options

And after the dataset is loaded into RStudio, you may view it with the View() command, or by clicking the identify of the dataset:

View Command

9. Review the Command History with One Click

Earlier, we discovered the shortcut to the command historical past from the console. RStudio additionally lets you view your whole command historical past within the upper-right window by clicking the History tab:

History Pane

10. Save Your “Real” Work. Delete the Rest.

Practice good housekeeping to keep away from unexpected challenges down the street. If you create an R object value saving, seize the R code that generated the thing in an R script file. Save the R script, however don’t save the surroundings, or workspace, the place the thing was created.
To forestall RStudio from saving your workspace, open Preferences > General and un-select the choice to revive .RData into workspace at startup. Be certain to specify that you just by no means wish to save your workspace, like this:

Never Save Your Workspace

Now, every time you open RStudio, you’ll start with an empty session. None of the code generated out of your earlier classes shall be remembered. The R script and datasets can be utilized to recreate the surroundings from scratch.

11. Organize Your Work with Projects

RStudio provides a strong function to maintain you organized; Projects. It is necessary to remain organized while you work on a number of analyses. Projects from RStudio permit you to hold your whole necessary work in a single place, together with code scripts, plots, figures, outcomes, and datasets.

Create a brand new challenge by navigating to the File tab in RStudio and choose New Project.... You have the choice to create your new challenge in a brand new listing, or an current listing. RStudio provides devoted challenge varieties if you’re engaged on an R bundle, or a Shiny Web Application.

RStudio Projects are helpful when it’s essential share your work with colleagues. You can ship your challenge file (ending in .Rproj) together with all supporting information, which can make it simpler for your colleagues to recreate the working surroundings and reproduce the outcomes.

But if you would like seamless collaboration, you could must introduce bundle administration into your workflow. Fortunately, RStudio provides a useful gizmo for bundle administration, renv, that’s now suitable with RStudio tasks. We’ll cowl renv subsequent.

12. Manage Package Versions with renv

We love R at Dataquest, however managing bundle variations could be a problem! Fortunately, R bundle administration is simpler than ever, because of the renv (“reproducible environment”) bundle from RStudio. And now, RStudio contains built-in help for renv.

We received’t get into the main points of find out how to use renv with RStudio tasks on this weblog as a result of RStudio gives you with the information you want within the hyperlink we offered and within the vignette. But utilizing renv with RStudio could make R bundle administration a lot simpler, so we wished to let you recognize!

The renv bundle is changing the Packrat bundle that RStudio used to take care of.

To use the renv bundle together with your RStudio tasks improve to the newest model of RStudio and then set up the renv bundle with library("renv"). From there you should have the choice to make use of renv with all new tasks:

Package Management

If you wish to use renv with an current challenge navigate to Tools > Project Options > Environments and test the field to allow renv:

Package Management

13. Manage Version Control with GitHub in RStudio

In addition to managing packages in RStudio, you can too use GitHub with RStudio to take care of model management of your tasks and R scripts. Check out this text from GitHub and this text from RStudio for all the data it’s essential combine Git into your RStudio workflow.

14. Code Snippets

RStudio provides a really helpful function for inserting frequent chunks of code, referred to as code snippets. One of our favorites is the lib snippet that saves you a little bit of typing when calling the library() perform to load an R bundle:

Code Snippets

After you hit return to pick the snippet, the library() perform is loaded and the cursor is positioned so you may instantly start typing the identify of the bundle you wish to load:


Our different favourite is the enjoyable snippet that gives a primary template for writing a customized perform. And you may even add snippets of your personal! To be taught extra, try this text on code snippets from RStudio.

15. Dig Into the Source Code of a Function

If you’d like to analyze the supply code of a perform, transfer your cursor to the perform of curiosity and enter F2 (on a Mac you could must enter fn + F2). This function even works for capabilities loaded from any R packages you utilize.

16. Function Extraction

If you’ve written a piece of code that you just wish to flip right into a perform, spotlight the code chunk and enter management + choice X on a Mac, Ctrl + Alt + X on Linux/Windows. A pop-up will seem that can ask you to pick a perform identify.

Extract Function

After the perform identify is chosen, the inputs and code construction wanted to show your code right into a perform shall be added robotically.

Extracted Function

If you could have a variable that you just wish to extract, spotlight the variable and enter management + choice V on a Mac, Ctrl + Alt + V on Linux/Windows.

17. Rename in Scope

At some level, you could want to vary the identify of a perform or a variable utilized in considered one of your capabilities. But utilizing discover and exchange to do that will be nerve-wracking! Fortunately, RStudio makes it doable to rename in scope. This means your adjustments shall be restricted to the variable or perform of curiosity. This will forestall you from by chance changing a variable of the identical identify elsewhere in your code script. To use this function choose the perform or variable you wish to change and enter management + shift + choice + M on a Mac, or Ctrl + Shift + Alt + M on Linux/Windows.


18. Multicursor Support

RStudio helps a number of cursors. Simply click on and drag your mouse whereas holding down choice on a Mac, or Alt on Windows/Linux.

Cursor Select

Cursor Edit

19. Use Python with RStudio and reticulate

RStudio helps coding in python. The course of to get python up and operating inside RStudio includes these basic steps:

  • Install a base model of Python
  • Install pip and virtualenv
  • Create a Python surroundings in your RStudio challenge
  • Activate your Python surroundings
  • Install desired Python packages in your surroundings
  • Install and configure the R reticulate bundle to make use of Python

This article gives the code you’ll want for the steps above. We tried it out and had been capable of run python in RStudio in just a few minutes:


For full particulars, try this RStudio tutorial.

20. Query SQL Using the DBI Package

There are some ways to run SQL queries in RStudio. Here are three of the preferred strategies, starting with the DBI bundle from R.

You’ll begin by producing an in-memory SQL database to make use of in all of your SQL question examples. You’ll generate a SQL database of the well-known “mtcars” dataset. Here’s the code:

Create DB

Now write a SQL question to pick all automobiles from the database with a four-cylinder engine. This command returns a dataframe that you just’ll save as dbi_query:

DBI Query

The dataframe seems like this:

21. Query SQL in R Markdown or Using an R Notebook

You can obtain the identical end in R Notebook or R Markdown by making a {sql} code chunk. Using the connection and database from the primary instance, run this code:

SQL Query

Specify output.var = "mt_cars_df" to save lots of the outcomes of your question to a dataframe. This dataframe is a typical R dataframe that’s similar to the one you generated within the earlier instance. You can use this dataframe in R code chunks to carry out evaluation or to generate a ggplot, for instance:

ggplot code


22. Query SQL with dbplyr

Finally, you’ll use the dbplyr bundle to put in writing commonplace dplyr instructions that get transformed to SQL! Once once more, utilizing the connection and database from the primary instance, you may write a typical filter() name to question the automobiles with 4 cylinders, this returns a listing object:


If you wish to see the SQL code that this command was transformed to, you should utilize the show_query() perform from dbplyr:


When you’re glad together with your question outcomes, you utilize the acquire() perform from dbplyr to save lots of your outcomes as a dataframe:


There you could have it! Three totally different approaches to querying a SQL database with related outcomes. The solely distinction between the examples is that the dbplyr technique returns a tibble, whereas the primary two strategies return a typical R dataframe.

To be taught extra about querying SQL databases with RStudio, try this text.

23. Take it to the Cloud!

RStudio now provides a cloud-based model of RStudio Desktop referred to as, you guessed it… RStudio Cloud. RStudio Cloud lets you code in RStudio with out putting in software program, you solely want an internet browser.

Work in RStudio Cloud is organized into tasks just like the desktop model, however RStudio Cloud lets you specify the model of R you want to use for every challenge.

RStudio Cloud additionally makes it simple and safe to share tasks with colleagues, and ensures that the working surroundings is totally reproducible each time the challenge is accessed.

As you may see, the format of RStudio Cloud is similar to RStudio Desktop:


Bonus: Cheatsheets

RStudio has revealed quite a few cheatsheets for working with R, together with an in depth cheatsheet on utilizing RStudio! Select cheatsheets will be accessed from inside RStudio by deciding on Help > Cheatsheets.

Additional Resources

RStudio has revealed quite a few in-depth find out how to articles about utilizing RStudio. Find them right here.


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