Gallery of Enterprise Examples

Featured

The following are a set of multiple types of artifacts published to Connect. They share or build on the same base analysis. Their purpose is to demonstrate how each type of asset can showcase an aspect of the analysis. The idea is to enable you to reach your audiences more effectively and efficiently.

Bike Prediction

This app provides real-time predictions of the number of bikes that will be available at the stations of Washington DC’s…

Gallery

A Daily ETL process with R, Python, feather, Shiny, and secured credentials

Often applications require data that updates at a regular interval as the result of an ETL process. This example shows how to setup an R Markdown document that runs every…

Access to Care

jupyter notebook

parameterized RMD

The following are a set of multiple types of artifacts published to Connect. They share or build on the same base analysis.…

Bike Prediction

internal package

This app provides real-time predictions of the number of bikes that will be available at the stations of Washington DC’s…

Bitcoin Demo

These files demonstrate how to use R in a production environment.

Connect & `targets`

Example GitHub repository: https://github.com/sol-eng/targets-deployment-rsc

Customized Conditional Emails with Connect

This article uses the example of an Etsy shop to demonstrate an R Markdown-based conditional custom email workflow using Connect.

Example: Customer Churn

Using Keras to predict customer churn based on the IBM Watson Telco Customer Churn dataset. We also demonstrate using the lime package to help explain which features drive…

Income Scoring

In this example, we’ll walk through an example of using the Posit toolchain for a data science project using US census data from the UCI Machine Learning Repository. The notebooks…

Jupyter Notebook Slideshows on Connect

jupyter notebook

If you’re familiar with Jupyter Notebooks, you’ve likely seen or encountered the slideshow cell toolbar extension before:

Machine Learning Operations (MLOps) with vetiver and Posit

jupyter notebook

Machine learning operations, or MLOps, are a set of practices to deploy and maintain machine learning models in production reliably and efficiently. The vetiver framework is built for R and Python model workflows, giving you tools for model deployment, monitoring, and…

Model Deployment and Plumber

This resource showcases how to expose a machine learning model built in R via a Plumber API. A model is first trained and saved as an .rds file. The Plumber file loads this saved model from disk, interprets data submitted via POST request, and returns a JSON object containing…

Plumber Logging

See Plumber Logging on R Views for a discussion of this code.

Plumber and the Slack API

See Slack and Plumber on R Views for a discussion of this code.

Repeatable Data Science: A Demo

Data science is repeatable if results can be reproduced on demand. This is a core tenet of good…

Replacing Excel: Customer Tracker

parameterized RMD

Use R Markdown instead of complex Excel calculators. The code-based examples here are easier to maintain and less prone to error than…

Reticulate example: Image classification with PyTorch and Shiny

This application demonstrates using reticulate and shiny to provide a user interface to a Pytorch image classification model.

Reticulate example: Sentiment analysis with Plumber and spaCy

You can call Python from R within REST APIs using Plumber.

Reticulate example: Visualizing pandas dataframes with ggplot2

You can use Python and R together within R Markdown by using code chunks that call either language.

Shiny Feature Toggles

At Posit, we recommend staying (or at least starting) within the boundaries of a set of best practices when it comes to the use of our professional products. I write how-to…

Kelly O’Briant

Stock Portfolio

parameterized RMD