Carson Farmer

Carson Farmer has contributed to the materials listed below.

Course Lessons

Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Carson Farmer has contributed to the following lessons:

Interactive Maps in Python

Folium is a Python package that can be used to create interactive maps in Jupyter Notebook. Learn how to create interactive maps with raster overlays in Python using Folium.

Get and Work With Twitter Data in Python Using Tweepy

You can use the Twitter RESTful API to access tweet data from Twitter. Learn how to use tweepy to download and work with twitter social media data in Python.

Programmatically Accessing Geospatial Data Using APIs

This lesson walks through the process of retrieving and manipulating surface water data housed in the Colorado Information Warehouse. These data are stored in JSON format with spatial x, y information that support mapping.

Introduction to APIs

In this module, you learn various ways to access, download and work with data programmatically. These methods include downloading text files directly from a website onto your computer and into Python, reading in data stored in text format from a website into a DataFrame in Python, and finally, accessing subsets of particular data using REST API calls in Python.

Sentiment Analysis of Colorado Flood Tweets in R

Learn how to perform a basic sentiment analysis using the tidytext package in R.

Create Maps of Social Media Twitter Tweet Locations Over Time in R

This lesson provides an example of modularizing code in R.

Use Tidytext to Text Mine Social Media - Twitter Data Using the Twitter API from Rtweet in R

This lesson provides an example of modularizing code in R.

Text Mining Twitter Data With TidyText in R

Text mining is used to extract useful information from text - such as Tweets. Learn how to use the Tidytext package in R to analyze twitter data.

Twitter Data in R Using Rtweet: Analyze and Download Twitter Data

You can use the Twitter RESTful API to access data about Twitter users and tweets. Learn how to use rtweet to download and analyze twitter social media data in R.

Creating Interactive Spatial Maps in R Using Leaflet

This lesson covers the basics of creating an interactive map using the leaflet API in R. We will import data from the Colorado Information warehouse using the SODA RESTful API and then create an interactive map that can be published to an HTML formatted file using knitr and rmarkdown.

Programmatically Accessing Geospatial Data Using API's - Working with and Mapping JSON Data from the Colorado Information Warehouse in R

This lesson walks through the process of retrieving and manipulating surface water data housed in the Colorado Information Warehouse. These data are stored in JSON format with spatial x, y information that support mapping.

Programmatically Access Data Using an API in R - The Colorado Information Warehouse

This lesson covers accessing data via the Colorado Information Warehouse SODA API in R.

Access Secure Data Connections Using the RCurl R Package.

This lesson reviews how to use functions within the RCurl package to access data on a secure (https) server in R.

An Example of Creating Modular Code in R - Efficient Scientific Programming

This lesson provides an example of modularizing code in R.

Introduction to APIs

In this module, you learn various ways to access, download and work with data programmatically. These methods include downloading text files directly from a website onto your computer and into R, reading in data stored in text format from a website, into a data.frame in R and finally, accessing subsets of particular data using REST API calls in R.

Compare Lidar to Measured Tree Height

To explore uncertainty in remote sensing data, it is helpful to compare ground-based measurements and data that are collected via airborne instruments or satellites. Learn how to create scatter plots that compare values across two datasets.

Extract Raster Values at Point Locations in Python

For many scientific analyses, it is helpful to be able to select raster pixels based on their relationship to a vector dataset (e.g. locations, boundaries). Learn how to extract data from a raster dataset using a vector dataset.

Data tutorials

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