Use R Studio projects

Always make your project an R Studio Project (if possible)!

Create an R Studio Project

Navigate an R Studio Project

You can use the Files pane in R Studio to interact with your project folder:

Set up your project

Set up your project

R Studio offers a lot of settings and options.

Name your files properly

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

1. Machine readable file names

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

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

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

Write beautiful code

Artwork by Allison Horst, CC BY 4.0

• Try to write code that others (i.e. future you) can understand
• Follow standards for readable and maintainable code
  • For R: tidyverse style guide defines code organization, syntax standards, …

Standard code structure

# This code replicates figure 2 from the
# Baldauf et al. 2022 Journal of Ecology paper.
# Authors: Selina Baldauf, Jane Doe, Jon Doe

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()

Modularize your Code

• Don’t put all your code into one long file (hard to maintain)
  • Write multiple files that can be called sequentially
    • E.g. 01_prepare-data.R, 02_lm-temperature-trend.R, 03_plot-temperture-trends.R
  • Write functions that can be called in other scripts
    • Use the source() function to source these files
    • Have one main workflow script that calls these functions sequentially

Use save paths

To read and write files, you need to tell R where to find them.

Avoid setwd()

# my unique path from hell with white space and special characters
setwd("C:/Users/Selina's PC/My Projects/Göttingen Temperatures/temperatures")

read_csv("data/2023-04-20_temperature_goettingen.csv")

read_csv("data/2023-04-20_temperature_goettingen.csv")

Coding style - Object names

# Good
day_one
day_1

# Bad
DayOne
dayone
first_day_of_the_month
dm1

Coding style - Spacing

# Good
x[, 1]

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

Coding style - Spacing

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

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

Coding style - Spacing

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

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

Coding style - Spacing

# Good
iris %>%
    group_by(Species) %>%
    summarize_if(is.numeric, mean) %>%
    ungroup()

# Bad
iris %>%
    group_by(Species) %>%
    summarize_all(mean) %>%
    ungroup()

Coding style - Spacing

# 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 %>%
    group_by(Species) %>%
    summarise(Sepal.Length = mean(Sepal.Length), Sepal.Width = mean(Sepal.Width), Species = n_distinct(Species))

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

Coding style

Do I really have to remember all of this?

Coding style helpers - R Studio

R Studio has style diagnostics that tell you where something is wrong

• Tools -> Gloabl Options -> Code -> Diagnostics

Coding style helpers - {lintr}

The lintr package analyses your code files or entire project and tells you what to fix.

# install the package before you can use it
install.packages("lintr")
# lint specific file
lintr::lint(filename = "analysis/01_prepare_data.R")
# lint a directory (by default the whole project)
lintr::lint_dir()

Coding style helpers - {lintr}

Coding style helpers - {styler}

The styler package package automatically styles your files and projects according to the tidyverse style guide.

# install from CRAN
install.packages("styler")

Coding style helpers - {styler}

• Pro-Tip: Add a custom keyboard short cut to style your files
  • Tools -> Modify Keyboard Shortcuts

Manage dependencies with {renv}

Manage dependencies with {renv}

# Get started
install.packages("renv")

# Step 1: initialize a project level R library
renv::init()
# Step 2: save the current status of your library to a lock file
renv::snapshot()
# Step 3: restore state of your project from renv.lock
renv::restore()

Clean projects and workflows …

… allow you and others to work productively.

Artwork by Allison Horst, CC BY 4.0

Take aways

There are a lot of things that require minimal effort and that you can start to implement into your workflow NOW

1. Use R Studio projects -> Avoid setwd()!
2. Keep your projects clean

• Sort your files into folders
• Give your files meaningful names

3. Use styler to style your code automatically
4. Use lintr and let R analyse your project
5. Consider renv for project local environments

Outlook

Of course there is much more:

• Version control with Git
• Using R packages to build a research compendium
• Docker containers for full reproducibility
• …

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