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# Code Clinic: R

## Overview

Practice coding with R. Explore common R programming challenges, and then compare the results with other programming languages in the Code Clinic series.

Successful programmers know more than just how to code. They also know how to think about solving problems. Code Clinic is a series of courses where our instructors solve the same problems using different programming languages. Here, Mark Niemann-Ross works with R. Throughout the course, Mark introduces challenges and then provides an overview of his solutions in R. Challenges include topics such as statistical analysis and accessing peripheral devices. Visit other courses in the series to see how to solve the same challenges in languages like C++, C#, JavaScript, PHP, Python, Ruby, Go, and Swift.

## Syllabus

Introduction
• Welcome
• Getting the most from Code Clinic
1. Problem One: Weather Statistics
• Introduction: The weather at Lake Pend Oreille
• Overview of linear models solution
• Importing data from GitHub
• Calculating the coefficient of barometric pressure
• Plotting the pressure and fit line
2. Problem Two: Where Am I?
• Introduction: Where am I?
• Overview of geolocation solution
• Retrieving my latitude and longitude
• Mapping my location
3. Problem Three: Eight Queens
• Introduction: Eight queens
• Overview of eight queens solution
• Modeling the problem to exclude columns
• Setting up to exclude rows
• Filtering for diagonal conflicts
• Efficiently storing the results
• Plotting the result
4. Problem Four: Musical Instrument
• Introduction: Accessing peripherals
• Overview of accessing peripherals solution
• Use Tcl/Tk and sonify( ) for a theremin
5. Problem Five: Facial Recognition
• Introduction: Facial recognition
• Overview of facial recognition solution
• Sending an image to Microsoft Cognitive Services Face API
• Rendering the image with face boxes
• Saving the results in JSON
6. Problem Six: Real-Time Information Dashboard
• Introduction: Real-time information dashboard
• Overview of dashboard solution
• Using Shiny Server to serve data values
• Using an R package for data and functions
• Using the Shiny client to render data

### Taught by

Mark Niemann-Ross

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