Folder and file organization

Nicky Wakim

Why does file organization matter?

Good organization means you can:

Find files without hunting
Re-run your analysis months later
Share your work without thorough explanations
Avoid broken paths and missing files
Collaborate without stepping on each other

Folder organization

Every analysis project should live in one folder that contains everything needed to reproduce it.

For an analysis project, I typically have the following folders

project_name/ 
├── admin/
├── background/
├── code/
├── data/
│   ├── data_info/
│   ├── data_raw/          
│   └── data_processed/    
├── dissemination/
├── meetings/
├── output/
└── README.txt

For our class, I suggest making one folder for the course with the following folders in it:

PUBH_523_26Su/ 
├── admin/
├── data_lessons/
├── data_practice/
├── data_project/
│   ├── data_raw/          
│   └── data_processed/    
├── lessons/
├── practice/
├── project/
└── README.txt

Our class folders

PUBH_523_26Su/ 
├── admin/
├── data_lessons/
├── data_practice/
├── data_project/
│   ├── data_raw/  
│   └── data_processed/    
├── lessons/
├── practice/
├── project/
└── README.txt

admin: Contains course materials, syllabus, and other administrative documents
data_lessons: Contains datasets used in lessons
data_practice: Contains datasets used in practice assignments
data_project: Contains datasets used in the final project
- data_raw: Original data — do not modify this!!
- data_processed: Cleaned/transformed data from labs
lessons: Contains lesson materials and code
practice: Contains practice assignments and code
project: Contains labs and project code and assignments

Folder and file naming

There are a few good practices for naming files and folders for easy tracking:

Keep the name short and relevant
Use lowercase letters for file names
Use “_” to separate sections of the name
- I also use this to separate words, but some people say you should use “-” to separate words
- Do not use spaces!!
Use leading numbers to help organize sequential items
- I can show you my lessons folders as an example
Use dates in the format “YYYY-MM-DD” so that files are in chronological order
You can label different versions if you would like to

Rule 1 & 2: Short, relevant, and lowercase

Keep names short and relevant — and use lowercase letters.

Too vague
- script3.R
- data.csv
Too long
- third_script_for_linear_model_v2.R
- all_the_survey_data_collected_2024.csv
Just right
- 03_wbns_modeling.R
- wbns_data_2024.csv

Using uppercase is okay! This is something I do in our class documents. It’s just a little more annoying to type.

Rule 3: Use `_` to separate words — no spaces!

Use underscores _ to separate parts of a name. Never use spaces.

Good
- clean_data.R
- clean-data.R
- 01_cleaning-data.qmd
- survey_responses_2024.csv
Avoid
- clean data.R
- survey responses 2024.csv

Some people use hyphens - to separate words and underscores _ to separate larger sections — both are fine, just be consistent!

Rule 4: Leading numbers for sequential items

Use leading numbers to keep files in the right order in any file browser.

01_import_data.R
02_clean_data.qmd
03_exploratory_analysis.qmd
04_model.qmd
05_figures.qmd

This works for folders too — great for organizing lessons, project phases, or any sequential workflow!
I also use something like z_misc to put a folder or file at the end of the order

Rule 5: Dates in `YYYY-MM-DD` format

Including dates keeps files in chronological order automatically.

Sorts correctly
- report_2026-01-15.pdf
- report_2026-03-02.pdf
- report_2026-11-20.pdf
Sorts incorrectly
- report_15-01-24.pdf
- report_02-03-24.pdf
- report_20-11-24.pdf

Starting with the year means alphabetical order = chronological order.

Rule 6: Versioning (optional)

You can label different versions of a file when you need to track changes.

analysis_v1.R
analysis_v2.R
report_draft_v1.qmd
report_draft_v2.qmd

Combine with dates for even more clarity:

report_2024-03-15_v1.qmd
report_2024-03-22_v2.qmd

Version labels are perfectly reasonable for coursework. For larger projects, clouds like OneDrive will save versions

Organizing your scripts/quarto docs

Split your analysis into numbered, single-purpose documents:

Script	Does
`01_import_data.qmd`	Loads raw data, minimal changes
`02_clean_data.qmd`	Filtering, recoding, joining
`03_explore.qmd`	Summary stats, exploratory plots
`04_model.qmd`	Statistical modeling
`05_figures.qmd`	Final publication-ready plots

Each script should be runnable on its own (given the right inputs exist), and should save its outputs to a known location
Files and output should only be dependent on files from earlier scripts in the sequence

The README File

Every project folder should have a README.txt at the top level.

# Survey Analysis — Spring 2024

## What this project does
Analyzes student survey responses collected in March 2024.

## Data
- `data/raw/survey_2024.csv` — Original export from Qualtrics
- `data/processed/survey_clean.csv` — Cleaned version (run 01_clean.R)

## How to run
1. Open `my_project.Rproj` in RStudio
2. Run scripts in order: 01 → 02 → 03 → 04

## Dependencies
R 4.3+, tidyverse, ggplot2

Wrap-up

The key habits:

One project = one folder — keep everything together
Protect raw data — never overwrite originals
Names: short, lowercase, no spaces — use _ to separate
Leading numbers for sequential files and folders
Dates as YYYY-MM-DD — for chronological order

Good organization is a gift to your future self.
The five minutes you spend now saves hours of confusion later.

Note on AI usage

I used AI (Claude) to help me organize my previous content and make some naming techniques more clear
AI (Claude) also wrote the wrap-up slide