W-01: Introduction to Map Making in R Pre-registration and additional fees are required for participation. Sign up will be available on a first-come, first-serve basis via the online registration form.
FULL DAY - SUNDAY; 9AM - 5PM (lunch on own)
Fee: Student -$30; Professional - $60
Contact: Shaley Valentine, Post Doctoral Researcher, Ohio State University, shaleyvalentine@gmail.com
Overview: This workshop will introduce participants to fundamentals of creating static and interactive maps using R statistical software. We will focus on the process of wrangling spatial data, integrating spatial data into maps, and mapping aesthetics. We will use the dplyr package to learn the process of wrangling spatial data as well as Tmap and Leaflet to create both static and dynamic maps. These packages use formatting that is consistent across the Tidyverse and the functions are relatively user friendly because they read as explanatory text. The benefit of using R or other coding languages for creating maps is that the workflow is reproduceable and easily altered for future project iterations. We will use fisheries and wildlife spatial datasets to maximize applicability of material to participants. Writing code in any language takes practice, so this workshop is meant to learn the coding process while thinking about the data wrangling needs to produce maps. Using this process, the goal of this workshop is for participants to increase their proficiency in coding and be able to apply the material to their own projects in the future. To achieve this goal, the workshop is set-up as modules that include walkthrough explanations and guided examples followed by independent or team exercises applying the walk-through code to new data. Each module and exercise will build off previous modules to reinforce material. Participants must bring a laptop with R and RStudio (free software) already downloaded. Participants should have some experience with R or another coding language such as Python and understand basic applications of spatial data.
Intended Audience: This workshop is intended for students and professionals who have some experience using R or other coding language such as Python and some familiarity with spatial data applications.
