Write Effective Code

What is “effective” code? The notion of effectiveness evolves as a data science project moves through the develop, deploy, and scale stages.

In Development, effectiveness is measured by the data scientist’s ability to use code to solve a problem. Code that is burdensome to maintain or not optimized to perform under demand is not effective.

The articles below offer guidance through these stages.

Learning to code in R and/or Python

When just beginning, these resources can help


Training
Posit Academy
The most effective way to learn data skills with your team

Getting comfortable with your tools
Minimum Viable Python
Using Python with Posit Products — an Overview
Connect User Guide
Workbench User Guide

Examples
Moving from Excel to R
See insights from an Excel workbook extended with code-based outputs such as Shiny, Flexdashboard, and R Markdown
Using `reticulate` to combine R and Python in one project
No matching items

Learn new frameworks

Whether R or Python, there are new tools to add to your toolbelt


Analyses and Reports
Add interactivity or parameterization to your reports for greater flexibility
Quarto for R, Python, Julia, and Observable
R Markdown
Jupyter

Web Applications and Dashboards
Without knowing html, javascript, or CSS, you can create powerful interactive applications and dashboards
Shiny for R and Python
Dash
Streamlit

APIs
Use APIs as a means to expose your analytics to other systems or create pipelines
Plumber
Flask
FastAPI
Example: integrate a plumber API with Slack
Example: expose a ML model as a plumber API

ModelOps
Vetiver for R and Python
Example: Bikeshare project model deployment using vetiver
No matching items

Code Smart

Be efficient with your code and your time.

Back to top