In this module, we introduce 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.
Max JosephMax Joseph has contributed to the materials listed below. Max is a data scientist with the Analytics Hub at Earth Lab and maintains this website.
Course material modules are sets of materials developed to teach specific learning objectives in a course setting.
This module will overview the basic principles of DRY - don't repeat yourself. It will then walk you through incorporating functions into your scientific programming to increase efficiency, clarity, and readability.
This tutorial demonstrates how to access and visualize crime data for Denver, Colorado.
This tutorial explains how members of Earth Lab can gain access to the PetaLibrary at the University of Colorado Boulder. It also outlines the process for setting up Globus to transfer files between endpoints (e.g., your local machine and the PetaLibrary).
This tutorial shows how to compute and plot contour lines for elevation from a raster DEM (digital elevation model).
This tutorial shows how to compute the slope and aspect from a digital elevation model in Python.
This tutorial shows how to compute raster statistics like the mean and variance around buffered spatial points in Python.
This tutorial demonstrates how to use climata to acquire streamflow data in and around Boulder, Colorado.
This tutorial demonstrates how to compute 2d spatial density and visualize the result using storm event data from NOAA.
This tutorial shows how to color lidar point clouds with RGB imagery, using freely available data from the National Ecological Observatory Network (NEON).