This lesson is being piloted (Beta version)

Data Harvesting for Agriculture

Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with agricultural data in R.

This is an introduction to R designed for participants with no programming experience. These lessons can be taught in 2 days. They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files and SSURGO data, the structure of data frames, and how to import and manipulate agricultural shape files.

Getting Started

Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow.

These lessons assume no prior knowledge of the skills or tools.

To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions.

Prerequisites

This lesson requires a working copy of R and RStudio.
To most effectively use these materials, please make sure to install everything before working through this lesson.

For Instructors

If you are teaching this lesson in a workshop, please see the Instructor notes.

Schedule

Setup Download files required for the lesson
00:00 1. Welcome to Data Harvesting for Agriculture!
00:00 2. Introduction to Programming with R and RStudio What is a program?
Why would do we want to use a program?
What is R and RStudio and how can we use them to read in data?
00:00 3. Introduction to QGIS What is QGIS good for?
How do I get started with QGIS?
00:00 4. Geospatial data and boundaries How do I read shape files into R?
What is a coordinate reference system (CRS)?
What is my shape file’s coordinate reference system?
00:00 5. Trial Design What kind of on-farm experiments do we do?
How do we design these experiments efficiently?
00:00 6. Trial Data What are the common file types in agricultural data?
00:00 7. Data Cleaning and Aggregation What does it mean to “clean” data and why is it important to do so before analysis?
How can I quickly and efficiently identify problems with my data?
How can identify and remove incorrect values from my dataset?
00:00 8. SSURGO & Weather Data What are the common file types in agricultural data?
What publicly available datasets exist for my field?
00:00 9. SSURGO & Weather Data What are the common file types in agricultural data?
What publicly available datasets exist for my field?
00:00 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.