📘 Day 1: Foundations of Collaborative Stata Programming

📘 Day 1: Foundations of Collaborative Stata Programming#

🎯 Objectives#

  • Understand the importance of directory structure for collaboration and reproducibility

  • Learn key system-level and Stata-specific commands for working with files

  • Write OS-agnostic, path-sensitive Stata code

  • Think like a collaborator: your code should run for someone else without edits

📂 Why Directories Matter#

Modern data work — especially in collaborative settings — depends on clean, predictable organization. Your folder structure is not just a container, it’s a map others will rely on.

A good structure:

  • Saves time (and money, if using compute credits)

  • Reduces errors in file paths

  • Builds trust with collaborators and co-authors

🔧 System-Level vs. Stata-Level#

Task

Type

Example

Change folder

System-level

cd, mkdir

Read/write data

Stata

use, save, export

Automate folder creation

Both

mkdir before graph export

🏗️ Example Project Structure#

project/
├── data/           # .dta files
├── output/         # Graphs, tables, logs
├── code/           # .do files
├── notes/          # README, protocols
└── master.do       # Runs everything

// Set root and create folder
cd "~/Documents/project/"
mkdir output

// Load a dataset
use "data/sample.dta", clear

// Save a graph
graph twoway scatter y x
graph export "output/scatterplot.png", replace

🖥️ Writing OS-Agnostic Code#

Different collaborators may use Windows, Mac, or Linux. Paths differ (\ vs /). You can detect the OS using Stata’s system variable c(os):

di c(os)

if c(os) == "Windows" {
    use data\transplants, clear
    list age in 1/10
}
else {
    use data/transplants, clear
    list age in 1/10
}

🤔 Reflection Questions#

  1. Could a non-Stata user run your code without opening it?

  2. Can you describe your folder layout to a collaborator in one sentence?

  3. Are your outputs stored in predictable, clean locations?

🧑🏾‍💻 Assignment#

Create your own project folder with this layout:

  • data/ – include a sample .dta

  • output/ – save a log and graph

  • code/ – write your master.do that automates loading, analyzing, exporting

Contents