This tutorial demonstrates how to access and visualize crime data for Denver, Colorado.
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 lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Max Joseph has contributed to the following lessons:
This lesson contains a series of challenges that require using tidyverse functions in R to process data.
When you are programming, it can be easy to copy and paste code that works. However this approach is not efficient. Learn how to create for-loops to process multiple files in R.
Learn how to write pseudocode to plan our your approach to working with data. Then use tidyverse functions including group_by and summarise to implement your plan.
Learn how to create and submit a pull request to another repo.
Learn how to fork a repository using the GitHub website.
Learn how to undo changes in git after they have been added or committed.
Learn basic git commands, including clone, add, commit, and push.
Learn what version control is, and how Git and GitHub are used in a typical version control workflow.
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.
This lesson covers accessing data via the Colorado Information Warehouse SODA API in R.
This lesson covers the JSON data structure. JSON is a powerful text based format that supports hierarchical data structures. It is the core structure used to create geoJSON which is a spatial version of json that can be used to create maps. JSON is preferred for use over .csv files for data structures as it has been proven to be more efficient - particulary as data size becomes large.
This lesson reviews how to use functions within the RCurl package to access data on a secure (https) server in R.
This lesson provides an example of modularizing code in R.
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.
Learn how to take code in a for loop and convert it to be used in an apply function. Make your R code more efficient and expressive programming.
Learn how to combine if statements, functions and for loops to process sets of text files.
Learn how to write a for loop to process a set of .csv format text files in R.
Learn how to work with function arguments in the R programming language..
This lesson introduces the function environment and documenting functions in R. When you run a function intermediate variables are not stored in the global environment. This not only saves memory on your computer but also keeps our environment clean, reducing the risk of conflicting variables.
Learn how to write a function in the R programming language.
This lesson will cover the basic principles of using functions and why they are important.
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).