Good practice R coding

Day 1 - Introduction to Data Analysis with R

Selina Baldauf

Freie Universität Berlin - Theoretical Ecology

March 14, 2025

Chaotic projects and workflows …

… can make even small changes frustrating and difficult.

A frustrated looking little monster in front of a very disorganized cooking area, with smoke and fire coming from a pot surrounded by a mess of bowls, utensils, and scattered ingredients.

Artwork by Allison Horst, CC BY 4.0

Background

Often, we want to share and publish our projects.

  • Reproducibility 🔄
    • Can someone else reproduce my results?
  • Reliability 🏋
    • Will my code work in the future?
  • Reusability ⚙️
    • Can someone else actually use my code?

Set up your project properly

Consistent structure and filenames

Have a clear project structure

  • One directory with all files relevant for project
    • Scripts, data, plots, documents, …
  • Choose a meaningful project structure 1
  • Add a readme file (usually README.md) in which you document the project structure
MyProject
|
|- data
|
|- docs
|   |
|   |- notes
|   |
|   |- reports
|
|- R
|   |
|   |- clean_data.R 
|   |
|   |- statistics.R
|
|- MyProject.RProj
|
|- README.md

Example RStudio project structure

Use RStudio projects

Always make your project an RStudio Project (if possible)!

✅ You already did that.

Set up your project

R Studio offers a lot of settings and options.

So have a ☕ and check out Tools -> Global Options and all the other buttons.

  • R Studio cheat sheet that explains all the buttons
  • Update R Studio from time to time to get new settings (Help -> Check for Updates)

Name your files properly

Your collaborators and your future self will love you for this.

Principles 1

File names should be

  1. Machine readable
  2. Human readable
  3. Working with default file ordering

1. Machine readable file names

Names should allow for easy searching, grouping and extracting information from file names.

  • No space & special characters

Bad examples ❌

📄 2023-04-20 temperature göttingen.csv
📄 2023-04-20 rainfall göttingen.csv

Good examples ✔️

📄 2023-04-20_temperature_goettingen.csv
📄 2023-04-20_rainfall_goettingen.csv

2. Human readable file names

Which file names would you like to read at 4 a.m. in the morning?

  • File names should reveal the file content
  • Use separators to make it readable

Bad examples ❌

📄 01preparedata.R
📄 01firstscript.R

Good examples ✔️

📄 01_prepare-data.R
📄 01_temperature-trend-analysis.R

3. Default ordering

If you order your files by name, the ordering should make sense:

  • (Almost) always put something numeric first
    • Left-padded numbers (01, 02, …)
    • Dates in YYYY-MM-DD format

Chronological order

📄 2023-04-20_temperature_goettingen.csv
📄 2023-04-21_temperature_goettingen.csv

Logical order

📄 01_prepare-data.R
📄 02_lm-temperature-trend.R

Let’s start coding

Good practice R coding

Write beautiful code

  • Try to write code that others (i.e. future you) can understand
  • Follow standards for readable and maintainable code

Artwork by Allison Horst, CC BY 4.0

Standard code structure

  1. General comment with purpose of the script, author, …
  2. library() calls on top
  3. Set default variables and global options
  4. Source additional code
  5. Write the actual code, starting with loading all data files
# This code replicates figure 2 from the
# Baldauf et al. 2022 Journal of Ecology
# paper.
# Authors: Selina Baldauf and Jane Doe
# Copyright Selina Baldauf (2024)

library(tidyverse)
library(vegan)

# set defaults
input_file <- "data/results.csv"

# source files
source("R/my_cool_function.R")

# read input
input_data <- read_csv(input_file)

Mark sections

  • Use comments to break up your file into sections
# Load data ---------------------------------------------------------------

input_data <- read_csv(input_file)

# Plot data ---------------------------------------------------------------

ggplot(input_data, aes(x = x, y = y)) +
  geom_point()
  • Insert a section label with Ctrl/Cmd + Shift + R
  • Navigate sections in the file outline

Break down large scripts

  • If your scripts become too big, split them
  • You can use source() in R to load the content of another script
    • You can source the same script into multiple other scripts

R/analyse_species.R:

# This code ....
# Authors: Selina Baldauf and Jane Doe
# Copyright Selina Baldauf (2024)

library(tidyverse)
library(vegan)

# source file for data reading and cleaning
# reads in the species data and cleans it for
# further analysis
# output is the tibble species_clean
source("R/prepare_data/read_and_clean_data.R")

# Analyse species data ----------------------

# code for the analysis

R/prepare_data/read_and_clean_data.R:

# Script to read in the raw species data and clean it

path_to_species <- "data/species.csv"

species_raw <- read_csv(path_to_species)

# further code for cleaning the data

Coding style - Object names

  • Variables should only have lowercase letters, numbers, and *_*
  • Use snake_case for longer variable names
  • Try to use concise but meaningful names
# Good
day_one
day_1

# Bad
DayOne
dayone
first_day_of_the_month
dm1

Coding style - Spacing

  • Always put spaces after a comma
# Good
x[, 1]

# Bad
x[ , 1]
x[,1]
x[ ,1]

Coding style - Spacing

  • Always put spaces after a comma
  • No spaces around parentheses for normal function calls
# Good
mean(x, na.rm = TRUE)

# Bad
mean (x, na.rm = TRUE)
mean ( x, na.rm = TRUE )

Coding style - Spacing

  • Always put spaces after a comma
  • No spaces around parentheses for normal function calls
  • Spaces around most operators (<-, ==, +, etc.)
# Good
height <- (feet * 12) + inches
mean(x, na.rm = TRUE)

# Bad
height<-feet*12+inches
mean(x, na.rm=TRUE)

Coding style - Spacing

  • Always put spaces after a comma
  • No spaces around parentheses for normal function calls
  • Spaces around most operators (<-, ==, +, etc.)
  • Spaces before pipe (|>) followed by new line
# Good
iris |> 
  summarize_if(is.numeric, mean, .by = Species)  |> 
  arrange(desc(Sepal.Length))

# Bad
iris|>summarize_if(is.numeric, mean, .by = Species)|>arrange(desc(Sepal.Length))

Coding style - Spacing

  • Always put spaces after a comma
  • No spaces around parentheses for normal function calls
  • Spaces around most operators (<-, ==, +, etc.)
  • Spaces before pipe (|>) followed by new line
  • Spaces before + in ggplot followed by new line
# Good
ggplot(aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point()

# Bad
ggplot(aes(x = Sepal.Width, y = Sepal.Length, color = Species))+geom_point()

Coding style - Line width

Try to limit your line width to 80 characters.

  • You don’t want to scroll to the right to read all code
  • 80 characters can be displayed on most displays and programs
  • Split your code into multiple lines if it is too long
    • See this grey vertical line in R Studio?
# Bad
iris |> summarise(Sepal.Length = mean(Sepal.Length), Sepal.Width = mean(Sepal.Width), Species = n_distinct(Species), .by = Species)

# Good
iris |>
  summarise(
    Sepal.Length = mean(Sepal.Length),
    Sepal.Width = mean(Sepal.Width),
    Species = n_distinct(Species),
    .by = Species
  )

Coding style

Do I really have to remember all of this?

Luckily, no! R and R Studio provide some nice helpers

Coding style helpers - RStudio

RStudio has style diagnostics that tell you where something is wrong

Tools -> Gloabl Options -> Code -> Diagnostics

Coding style helpers - Auto-formatting

RStudio can automatically format your code!

  • Open the command palette with Ctrl/Cmd + Shift + P
  • Search for “format” to see all formatting options
    • “Reformat Current Document” to format the open script
    • You can toggle “Reformat documents on save” to format automatically on save
    • By default the “Code formatter” is the tidyverse style guide (Styler)

Coding style helpers - Linting

  • Linters are tools that analyze your code for potential errors
  • The R package lintr is a linter for R code
    • Install it with install.packages("lintr")
  • Open the command palette with Ctrl/Cmd + Shift + P and search for “lint”
    • “Lint current file” to lint the open script
    • This will print a list of potential improvements in the console area

Let’s share

Publish or share your analysis

Make your code sharable

  • Use relative paths!
  • Make sure you include all necessary files
  • Include a readme with instructions on how to run the code and an explanation of the project structure
  • Comment your code to make it understandable

Send your code to a colleague

If you are unsure if your code is understandable and can run on another machine, send it to a colleague and ask them to run it and give you feedback

Publishing your code

  • Include a licence
    • Choose a licence (common for open source projects: MIT, GPL-3.0)
    • Use the usethis package to add a licence to your project
      • install.packages("usethis")
      • usethis::use_mit_license("Your Name") adds a licence file to your project
  • Show which packages and which versions you used
    • this is important for reproducibility as packages change over time
    • devtools::session_info() prints a list of all packages and their versions (add this to your readme file)
    • renv is a package that helps you manage package versions
  • Consider learning Git and publish on GitHub/Gitlab etc.

Clean projects and workflows …

… allow you and others to work productively.

But don’t get overwhelmed by all the advice. Just start with one thing.

An organized kitchen with sections labeled "tools", "report" and "files", while a monster in a chef's hat stirs in a bowl labeled "code."

Artwork by Allison Horst, CC BY 4.0