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Styleguide R code

This style guide is a recommendation for all R code written for the Research Institute for Nature and Forest (INBO). The goal of this style guide is twofold. First of all applying the guidelines will result in readable code. Secondly, it is much easier to work together on code when everyone is using the same guidelines.


This style guide is a recommendation for all R code written for the Research Institute for Nature and Forest (INBO). The goal of this style guide is twofold. First of all applying the guidelines will result in readable code. Secondly, it is much easier to work together on code when everyone is using the same guidelines. It is likely that applying these guidelines will have consequences on the current style used by many R users at INBO. Therefore this style guide should be applied within reason.

  1. Don’t apply the style guide to existing code.
  2. R users are free to apply the style guide to new personal R code.
  3. Using the style guide is highly recommended for new or revised R code intended to be distributed and used by other R users.
  4. The style guide is mandatory for new or revised R packages distributed by INBO.

Please note that the RStudio editor has some handy features that automatically highlights errors against the code style in a non intrusive way. RStudio hints in this document are the instructions to activate these diagnostics in RStudio.


RStudio hint: Tools > Global options > Code > Diagnostics: Check everything


  • lines should not exceed 80 characters
    • split the command over multiple lines if the command is longer than 80 characters
    • RStudio hint: Tools > Global options > Code > Display: Check Show margin and set Margin column to 80
  • object names should be meaningful
  • object names should not exceed 20 characters
  • object names should be lowercase
  • use _ to separate words in object names
    • function names with a single dot are allowed
  • use double quotes (") around characters and not single quotes (')
  • don’t add commented code
    • use version control if you want to keep old versions of code
# Good
example_text <- example_function(
  first_argument = "Some text",
  second_argument = "More text"

# Bad <- MyCoolFunction(FirstArgument = 'Some text', second.argument = 'More text')



Natural languages use whitespace and punctuation to make texts more readable. Programming languages are no exception to this rule.

  • don’t use tabs, use two spaces instead
    • RStudio hint: Tools > Global options > Code > Editing: Check Insert spaces for tab and set Tab width to 2
  • no space before a comma, one space after a comma
  • one space before and after an infix operator (+, -, *, /, ^, &, |, %%, %/%, %*%, %in%, …)
  • no spaces a the end of a line
    • RStudio hint: Tools > Global options > Code > Saving: Check Strip trailing horizontal whitespace when saving
  • end the script file with a single blank line
    • RStudio hint: Tools > Global options > Code > Saving: Check Ensure that source files end with newline


  • only create an object when you will use it later on
  • always use <- for assignment
  • always put the new variable on the left and never use ->
  • use = only for passing arguments in a function
  • at least one space before and at least one space after <- and =
    • use multiple spaces if it improves readability
# Good
x <- data.frame(z = 1:10)

# Bad
5 -> a

# Improved readability example
a   <-   5
ab  <-  10
abc <-   7
d   <- 245


R uses three types of brackets: round (...), square [...] and curly {...}.

  • no spaces after opening a bracket
  • no spaces before closing a bracket
  • no spaces before opening a bracket except:
    • one space with control flow functions (if, else, for, while)
  • no spaces after closing a bracket except:
    • one space with control flow functions (if, else, for, while)
  • { should not start on a newline and is always the end of a line
  • apply indentation when splitting long text inside brackets over multiple lines
# Good
y <- seq(0, 2)
if (max(y) <= 10) {
  x <- 1
} else {
  x <- 2

# Bad
y <- seq (0, 2   )
if(   max( y ) <= 10    )        
{ x <- 1 } else 
{ x <- 2 }
sapply( y , function ( x ) { return(x)})

Special cases and exceptions

  • selecting rows with square brackets df[selection, ]
    • this results in two conflicting rules
      1. a single space after a comma
      2. no space before a bracket
    • solution in case of a short command: add # nolint after the command
      • df[selection, ] # nolint
    • solution in case of a long command: split the command over several lines
# Good
relevant_subset <- original_dataframe[
  original_dataframe$x > some_value | original_dataframe$y < some_other_value,
# Recommended dplyr alternative
relevant_subset <- original_dataframe %>%
  filter(x > some_value | y < some_other_value)

# Bad
relevant_subset <- original_dataframe[original_dataframe$x > some_value | original_dataframe$y < some_other_value, ] # nolint
  • a really long text
    • text shorter than 80 characters but passed the 80 character limit due to the indentitation
      • solution: remove all indentation
    • text longer than 80 characters
      • solution: add # nolint at the end of the line
  • functions from other packages with names that don’t comply with this style guide
    • solution: add # nolint at the end of the line

Important notice

Adding # nolint at the end of a line excludes that line from the automatic checks for coding styles. Therefore use it only when you have no other options.


  • always explicitly mention return in functions
# Good
sum <- function(x, y) {
  z <- x + y
sum <- function(x, y) {
  return(x + y)

# Bad
sum <- function(x, y) {
  x + y

Validating syntax

The code below validate the syntax for an R file, an RMarkdown file or an R package.

RStudio hint: Running this code within RStudio will open a Markers pane, indicating the filename, line number and the kind of syntax error that occurs. Double clicking on the error will open the file at the correct location, making it easy to rectify the problem.

Extra hint: start correcting for the last lines and work your way forward. This leaves the line numbers of the errors intact until you solve them.

An example to clarify this. Suppose you have an error at line 10 and an error at line 100. Both errors are lines should not be more than 80 characters, so to solve them we have to split the lines over multiple lines.

Let say that we start with solving line 10 by splitting it over four lines. So the old line 10 becomes the new lines 10 to 13. Hence the old line 11 becomes the new line 14, and the old line 100 becomes the new line 103. When we now click on the marker for line 100, RStudio will go the current line 100 which is the old line 97. So you end up looking for an error at the wrong position.

Starting at the back solved this issue. In the same example we would start by solving line 100. Let’s assume we split this over two lines. So old line 100 because new line 100 and 101. Old line 101 becomes new line 102 but more importantly all line numbers before 100 are unchanged. So clicking on the marker for line 10 will take you the current line 10 which is the old line 10.

# validate a single file
lintr::lint(filename = "file.R")
lintr::lint(filename = "file.Rmd")

# validate a package
lintr::lint_package(path = ".")



  • Add documentation above each function with Roxygen markup
  • Add inline comments where relevant

Required Roxygen tags

#' @title Title of the helpfile
#' @description Description of the function in the helpfile
#' @param define a parameter
#' @export is the function exported by the package NAMESPACE
#' @importFrom import a function from another package

Optional Roxygen tags

#' @seealso link to other functions
#' @section section title
#' @alias other name for the topic
#' @keywords a set of standardised keywords. See file.path(R.home("doc"), "KEYWORDS")
#' @inheritParams inherit the definition of parameters from another function
#' @examples a working example of the function
#' @return a description of the output from the function

See for more information on Roxygen

INBO extra requirements for package DESCRIPTION

  • license: MIT or GPL-3? In case of MIT a LICENSE file should be added and License: MIT to the DESCRIPTION. In case of GPL-3 it is sufficient to add License: GPL-3 to the DESCRIPTION
  • list of authors in Authors@R format
    • INBO is listed as copyright holder
    • one or more roles are atributed to each person
      • cre: package maintainer (only one person)
      • aut: main author (at least one person)
      • ctb: contributor (if relevant)
      • cph: copyright holder (must be INBO)

Authors@R: c(person(“Els”, “Lommelen”, email = “”, role = c(“aut”, “cre”)), person(“Thierry”, “Onkelinx”, email = “”, role = “aut”), person(“Anja”, “Leyman”, email = “”, role = “ctb”), person(family = “Research Institute for Nature and Forest (INBO)”, email = “”, role = “cph”))


  • Add one or more how-to’s to a package
  • Add them as RMarkdown vignettes

File structure

R Package


  • all generic R functions should be distributed as an R package
  • use devtools::create() to start a new pacakge
    • RStudio hint: File > New project > New directory > R Package: Type the name in Package name
  • keep source files compact
    • create a separate file for each function, with the file name equal to the function name. This makes it easy to find the correct source file.
    • exception: very short auxilary functions with related functionality
      • related functions can be bundled into one R script
      • file name is either equal to the most important function or describes the related functionality
  • split large functions into several subfunctions


  • place scripts in the inst folder
    • the scripts will be available for the user after installing the package
    • the location of the scripts can be found with system.file("script-name.R", package = "yourpackage")
    • use a relevant folder structure when adding lots of files to inst

Unit tests

  • use the testthat package for unit tests
    • use devtools::use_testthat() to setup the test infrastructure
  • all unit tests are stored in tests/testthat
  • all files should have either a test_ or helper_ prefix
    • files with helper_ prefix contain auxiliary function for the tests but no tests
  • the test files will be run in alphabetical order
    • setting the order of the files is easy by adding 3 letters to the prefix (eg. test_aaa_, test_baa, test_zzz_)
    • 3 letters offers quite some flexibility to insert new files at the correct location without having to rename at lot of files. If the first file is test_aaa_ and the second test_baa, they you can 675 files between the two.
  • unit test files can be larger than source files
  • a unit test file can contain tests for several functions in case the functions are strongly related (e.g. subfunctions) and reuse test cases
  • each package should contain the unit for coding style as listed below
    • store this in a file tests/testthat/test_zzz_coding_style.R
    • add this file to .gitignore
      • the coding style will be tested separately when using continuous integration
if (requireNamespace("lintr", quietly = TRUE)) {
  test_that("Package Style", {


To make data available for users, they can be stored in a package in 3 different file types:

  • plain text file (.txt, .csv,…): use if
    • your package is under version control and the data often change
    • you want to keep track on the changes using version control
    • consider to keep row and column order fixed
  • binary file (.Rdata or .rda): use if
    • your dataset is large and data do not change between different versions
    • you are not interested in keeping track on the exact changes
    • you want to keep the exact format of the data (e.g. factors with levels) (possible in plain text with git2rdata)
  • code (.r) generating a table: use if the data can easily be generated with code

Data can be stored in 3 places:

  • to make data available for loading and parsing examples, store them in the folder inst/extdata. Access this data with system.file("xyz", package = "abc"). Possible for all data types.
  • to make data available to package users, store them in the data folder. Access this data with data(xyz). Possible only for data formats that can be handled by data(). Binary .rda-files can be stored by using usethis::use_data(xyz).
  • to keep data internal for use by your functions, store them in the file R/Sysdata.rda by using usethis::use_data(xyz, internal = TRUE). Access this data with abc::xyz.

(In the above examples, xyz are data and abc is the package in which they are stored.)

Add scripts for generating these data in the folder data-raw and create this folder by using usethis::use_data_raw() (ignores folder during build).

R script

  • group a long set of commands with similar functionality into a dedicated function
    • e.g. prepare_data(), do_analysis(), create_figure(), …
  • place the user defined functions in a separate file which you source() into the main script
    • it is better to use the same file structure as an R package
    • consider writing a simple package in case you have a lot of functions


  • each chunk has only one output (figure, table, summary, …)
  • don’t mix (heavy) calculations and output in the same chunk: this is more interesting for caching the results
  • give chunks a relevant name: this make debugging easier and file name of figures and Bookdown label will be based on the chunk name
  • avoid writing code that generates Markdown
    • use (parametrised) child documents instead
  • use the bookdown version for long reports: this makes it easy to split a long report into several child documents

Data import

  • readr: import text files
  • readxl: import Excel files
  • googlesheets: import Google Sheets
  • DBI: connect to databases PostgreSQL, SQLite, MySQL, Oracle, …
  • RODBC: connect to databases SQL Server, Access

Data manipulation & transformation

  • dplyr:
    • subsetting observations
    • subsetting variables
    • changing variables
    • aggregation
    • combining dataframes
  • tidyr:
    • changing a dataframe from wide to long format and vice versa
    • nesting and unnesting dataframes
    • splitting a single variable into multiple variables


  • ggplot2:all static graphics, charts and plots
  • INBOtheme: INBO corporate identity for ggplot2 graphics

Quality control

  • lintr: checking coding style
  • testthat: writing unit tests
  • covr: check which part of the code is not covered by unit tests