RProjects

Creating a project-oriented workflow in R

Author
Affiliation

Daniela Palleschi

Humboldt-Universität zu Berlin

Workshop Day 2

Thu Aug 22, 2024

Last Modified

Wed Aug 21, 2024

Learning Objectives

Today we will…

  • learn about project-oriented workflows
  • create an RProject
  • use project-relative filepaths with the here package

Installation requirements

Project-oriented workflow

  1. Folder structure:
    • keeping everything related to a project in one place
    • i.e., contained in a single folder, with subfolders as needed
  2. Project-relative working directory
    • the project folder should act as your working directory
    • all file paths should be relative to this folder

Folder structure

  • a core computer literacy skill
    • keep your Desktop as empty as possible
    • have a sensible folder structure
    • avoid mixing subfolders and files
      • i.e., if a folder contains subfolders, ideally it should not contain files

RProjects

  • in data analysis, using an IDE is beneficial
    • e.g., RStudio
  • most IDEs have their own implementation of a Project
  • in RStudio, this is the RProject
    • creates a .Rproj file in a project folder
    • stores project settings
  • you can have several RProjects open simultaneously
    • and run several scripts across projects simultaneously
  • most importantly, RProjects (can) centralise a specific project’s workflow and file path
  • to read more about R Projects, check out Section 6.2: Projects from Wickham et al. (2023; or Ch. 8 - Workflow: Projects in Wickham & Grolemund, 2016)

Creating a new Project

  • when?
    • whenever you’re starting a new course or project which will use R
  • why?
    • to keep all the relavent materials in one place
  • where?
    • somewhere that makes sense, e.g., a folder called SoSe2024 or Mastersarbeit
  • how?
    • File > New Project > New Directory > New Project > [Directory name] > Create Project

New RProject

Create a new RProject for this workshop

  • File > New Project > New Directory > New Project > [Directory name] > Create Project
  • make sure you choose a sensible location

Opening a Project

  • to open a project, locate its .Rproj file and double-click
  • or if you’re already in RStudio, you can use the Project (None) drop-down (top right)
Figure 1: Double-click .Rproj
Figure 2: Open from RStudio

Adding a README file

  • File > New File > Markdown File (not R Markdown!)
    • add some text describing the purpose of this project
    • include your name, the date
    • use Markdown formatting (e.g., # for headings, *italics*, **bold**)
  • save as README.md in your project directory

Global RStudio options

Figure 3: RStudio settings for reproducibility
  • Tools > Global Options
    • Workspace: Restore .RData into workspace at startup: NO
    • Save workspace to .RData on exit: Never
  • this will ensure that you are always starting with a clean slate
    • and that your code is not dependent on some pacakge or object you created in another session
  • this is also how RMarkdown and Quarto scripts run
    • they start with an empty environment and run the script linearly

Global settings

Change your Global Options so that

  • Workspace: Restore .RData into workspace at startup: NO
  • Save workspace to .RData on exit: Never

Identifying your RProject

  • there are a ways to check which (if any) RProject you’re in
    • there are 6 differences between xyzfig-noproject and xyzfig-project
    • which is in an RProject session?
Figure 4: RStudio Session A
Figure 5: RStudio Session B

Folder structure

  • some folders you’ll typically want to have:
    • data: containing your dataset(s)
    • scripts (or analyses, etc.): containing any analysis scripts
    • manuscript: containing any write-ups of your results
    • materials: containing relevant experiment materials (e.g., stimuli)
  • let’s just create the first 2 (data and scripts)

data/

  • do you have “raw”, i.e., pre-processed data?
    • if so, you might want to create a raw sub-folder
    • and any other relevant sub-folders (e.g., processed or tidy)
  • download the dataset from the workshop repo (from Chromý et al., 2023)
    • or, move a dataset of your own to this folder

scripts/

  • try to create a single script for each “product”
    • e.g., anonymised data, ‘cleaned’ data, data exploration, visualisation, analyses, etc.
  • you can create sub-folders as the project develops and move scripts around
    • for now, let’s create a new script to take a look at our data

New script

Create a new Quarto script:

  1. File > New File > Quarto Document
  2. Add a title
  3. Uncheck the Use Visual Editor box
  4. Click Create
  5. Save it in your scripts/ folder: File > Save as...

Load in the data

  • load in the data however you normally would
    • e.g., readr::read_csv()

here-package

  • here package (Müller, 2020) enables file referencing
    • avoids the use of setwd()
Figure 6: Illustration by Allison Horst

The problem with setwd()

If the first line of your R script is

setwd("C:\Users\jenny\path\that\only\I\have")

I will come into your office and SET YOUR COMPUTER ON FIRE🔥.

— Jenny Bryan

  • setwd() depends on your entire machine’s folder structure
  • setwd() breaks when you
    • send your project folder to a collaborator
    • make your analyses open
    • change the location of your project folder
  • using slashes is also dependent on your operating system

The benefit of here()

  • uses the top-level directory of your project as the working directory
  • can separate folder names with a comma

here

Load the dataset using here

  1. Install here (e.g., install.packages("here"))
  2. Load here at the beginning of your package
    • or use here:: before calling a function
  3. Use the here() function to load in your data
  4. Inspect the dataset however you usually would (e.g., summary(), names(), etc.)
  5. Save your script

here::here()

  • install package
In the Console
install.packages("here")
  • load package and call the here function
# load package
library(here)

# read in data
df_data <- read.csv(here("data", "data_lifetime_pilot.csv"))
  • or directly call the here function without loading the package
# read in data without loading here
df_data <- read.csv(here::here("data", "data_lifetime_pilot.csv"))
  • note that I stored the data with the prefix df_
    • df stands for dataframe
  • I recommend using object-type defining prefixes for all objects in your Environment
    • e.g., fit_ for models, fig_ for figures, sum_ for summaries, tbl_ for tables, etc.

Reproduce your analysis
  1. Perform some data exploration (e.g., with names(), summary(), dplyr::glimpse(), whatever you typically do)
  2. Save your script, then close RStudio/your Rproject.
  3. Re-open the project. Can you re-run the script?

Learning objectives 🏁

Today we learned…

  • learn about project-oriented workflows ✅
  • create an RProject ✅
  • establish a self-contained project environment with here ✅

References

Bryan, J., & TAs, T. S. 545. (n.d.). R Basics and workflows. In STAT 545 Course materials. Retrieved May 6, 2024, from https://stat545.com/
Chromý, J., Brand, J., Laurinavichyute, A., & Lacina, R. (2023). Number agreement attraction in Czech and English comprehension: A direct experimental comparison. Glossa Psycholinguistics, 2(1), 1–20. https://doi.org/10.5070/G6011235
Müller, K. (2020). Here: A Simpler Way to Find Your Files (Version 1.0.1). https://CRAN.R-project.org/package=here
Using RStudio Projects. (2024, April 16). Posit Support. https://support.posit.co/hc/en-us/articles/200526207-Using-RStudio-Projects
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science (2nd ed.). https://r4ds.hadley.nz/
Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. " O’Reilly Media, Inc.".