Chapter 6 Tips and tricks

Learning goals

By the end of this chapter you should be able to:

  • Adjust RStudio so it is comfortable to use.
  • Use a few high-value keyboard shortcuts.
  • Export plots and tables cleanly.
  • Avoid common data-import problems.

Prerequisites

  • You can run code from a script in RStudio.

A tiny motivating example

Here is a small quality-of-life improvement you can apply immediately:

options(scipen = 999)

This strongly discourages R from using scientific notation when printing numbers. The setting lasts only for the current R session, so put it at the top of your script if you want it every time.

6.1 Appearance

What we’re about to do: Make RStudio easier to read.

You can change the appearance under Tools > Global Options > Appearance. There you can change the theme, the font and the font size.

  • Increase the font size if you are squinting.
  • Try a dark theme if you find a white background tiring.

Takeaway: A readable setup reduces mistakes and fatigue.

6.2 Shortcuts (top seven)

What we’re about to do: Learn the shortcuts you will use every session.

  • Run current line/selection: Ctrl+Enter (macOS: Cmd+Enter)
  • Run from start to current line: Ctrl+Alt+B (macOS: Cmd+Option+B)
  • Comment/uncomment: Ctrl+Shift+C (macOS: Cmd+Shift+C)
  • Insert the assignment operator <-: Alt+- (macOS: Option+-)
  • Restart R (a clean session): Ctrl+Shift+F10 (macOS: Cmd+Shift+0)
  • Auto-complete: start typing, then press Tab
  • Insert a pipe: Ctrl+Shift+M (macOS: Cmd+Shift+M)

This book uses the %>% pipe. You will also see R’s built-in (“native”) pipe, |>, in other people’s code. For everything we do in this course, you can treat them as equivalent: x %>% f() and x |> f() both mean f(x). Their differences matter only in more specialised code.

Which pipe the shortcut inserts depends on your RStudio version and settings: recent versions (2026.06 onwards) insert |> by default, older ones %>%. Either is fine. If you want the shortcut to match the book, untick Use R’s native pipe operator, |> under Tools > Global Options > Code > Editing. (In the newest versions, the same shortcut inserts + when the cursor is inside a ggplot() chain.)

Try this: Use Ctrl+Shift+C to comment out a block, then toggle it back.

Takeaway: Shortcuts save time and reduce clicking.

6.3 Code style (keep it readable)

What we’re about to do: Make your scripts easier to read.

A few habits help a lot:

  • One pipe step per line.
  • Clear, meaningful object names.
  • Blank lines between sections.

If you want a full guide, the tidyverse style guide is excellent: https://style.tidyverse.org.

Takeaway: Readable code is easier to debug and reuse.

6.4 The plots pane

What we’re about to do: Save plots cleanly.

Plots appear in the Plots pane (usually bottom right). Use the back/forward arrows to browse old plots and Export to save one. In the export window, choose suitable dimensions and check the preview before saving.

For a plot you will need again, or want at an exact size (for example in a report), save it from code with ggsave() instead, so the size is recorded in your script. The Pimping your plots chapter shows how.

Common mistake: Exporting a plot without checking its dimensions. A plot that looks fine in the pane may become cramped when saved at a different size.

6.5 Tables

If you need to use a summary table outside R, save it as a CSV file and open it in spreadsheet software such as Excel.

write.csv(my_table, file = "example.csv", row.names = FALSE)

Danish Excel: if Excel uses Danish settings, it expects ; between columns and , as the decimal mark, so a file made with write.csv() may open with everything in one column. Use write.csv2() instead to write a file in that format.

6.6 Importing data: common problems

What we’re about to do: Avoid the most common import errors.

  • Everything in one column? The file probably uses a different delimiter. CSV files saved from Excel with Danish settings use ; between columns and , as the decimal mark: read them with read.csv2() (or read.csv(..., sep = ";")).
  • Strange characters instead of æ, ø, å? The file uses a different text encoding. Try read.csv(..., fileEncoding = "UTF-8") or fileEncoding = "latin1".
  • A strange first column name, such as X.U.FEFF.Name or ï..Name? The file starts with an invisible “byte order mark”. Use read.csv(..., fileEncoding = "UTF-8-BOM").
  • Missing values coded as numbers, such as -999? Tell R with na.strings, for example read.csv(..., na.strings = "-999"). Otherwise these values are included in means and plots.
  • A numeric column imported as text or a factor? At least one value is not a number (a typo, a space, a word such as “unknown”). See the carnivora example in An R refresher.

Sanity checks:

  • str(data) to check column types
  • names(data) to check column names
  • nrow(data) and ncol(data) to check size

Takeaway: Always inspect data right after importing.

6.7 Key takeaways

  • Make RStudio comfortable and readable.
  • Learn a few shortcuts and use them daily.
  • Use Ctrl+Shift+M (macOS: Cmd+Shift+M) to insert a pipe (%>% or |>, depending on your settings).
  • Export plots and tables deliberately.
  • Always check imported data.

6.8 Common pitfalls recap

  • Forgetting to check the delimiter when importing (use read.csv2() for Danish Excel files).
  • Not telling R how missing values are coded.
  • Skipping basic checks after import.