Data Wrangling with R

Target Audience:

Anyone who wants to learn R to manipulate data and communicate analysis.

Summary and Objectives:

Participants with no prior experience in R will learn how to transform messy field data into purposeful information for analysis and decision making. Computer programming is a critical skill for anyone working with data because it promotes reproducible research and communication of science. R is an open-source programming language that was developed by scientists as a tool to analyze and share data. Participants will find R much easier to learn than other programming languages because R has a growing user community that provides online support, books, and tutorials. Program users can do almost anything in R, the only limitation is their creativity. The course format combines lectures, hands-on computer exercises, and open lab time where participants can work on their own data that they bring to class. The RStudio integrated development environment and the tidyverse family of packages will be highlighted in the course as they make R programming a fun and intuitive experience. This course is highly recommended as a precursor to all of the National Conservation Training Center's data analysis courses.

Upon completion of this course, participants will be able to:

• Understand the basics of writing R code and working in the RStudio environment.
• Import and export multiple file types, such as text, spreadsheet, database, and spatial file types.
• Use the tidyverse family of packages to wrangle messy data into a clean data structure structure
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  Learn more about structure and perform summaries.
• Create remarkable graphs to explore their data and communicate results.
• Write functions to automate common data manipulation tasks.
• Share their data in GitHub and promote reproducible research by creating reports in RMarkdown and RShiny.

Competency Addressed:

Data Management - Basic, Ecoinformatics - Basic, Geospatial Information Systems - Awareness, Research and Statistics - Basic

Special Requirements:

Participants should bring some of their own field data to use during course exercises.