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 (wickham_r_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

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)

Global RStudio options

• 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

Spot the differences

Spot the differences: RProject vs. None

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 on this week’s Moodle section
  • 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

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::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"))