WAGIS2018

Intended Audience:
This course is for students who are familiar with geospatial analysis (this can be in working with ArcMap or QGIS, or even Python) and possess beginner to intermediate programming skills for at least one language. We would like attendees to know basic data structures (lists, arrays, and matrices), know basic conditional and looping constructs (if/else, for, while), and have a grasp on the purpose of functions when writing code. We also expect attendees to be familiar with common geospatial vector and raster operations.

Overview:
R is at the forefront of reproducible research. With R Markdown you can share your work with clients and colleagues for quality control or transparency. It’s all open source, which means anyone can check your work or collaborate with you on a given analysis without having to pay for licensing. It has multiple tools and packages for data visualization, making it easy to test out new ideas and analyses. It also has one of the biggest package repositories for data analysis and statistics.

In this workshop we will introduce students to R and its capabilities with both tabular and spatial data.

Our goal is to cover the basics of the R language using data manipulation examples, show how to document work, then dive into geospatial analysis for both raster and vector data structures. We will work in R Markdown, a way of documenting your methods and code in a file that is interpreted by R and outputs a document that can be reviewed by others. These markdown files will act both as teaching materials and as a reference that students can use after the workshop. For the geospatial component, we will teach students about the capabilities of the sp, sf, and raster libraries. We will also cover some of the basics of functional programming and parallel processing along the way.

Learning Objectives:
• Working with R Studio
• R Markdown
• Using R’s base data structures: vectors, lists, matrices, data.frames
• Basic plotting
• Functional programming concepts
• The *apply (apply, lapply, sapply, mapply) family for functional programming
• magrittr pipes
• sp vs sf libraries (we will focus on sf)
• The raster library
• The parallel library
. . . and if we have time. . .
• tidyverse
• data.table

We will implement these tools to perform a geospatial analysis using R Markdown.