An R course for data preparation, aimed at ecologists

View the Project on GitHub cbrown5/RLS-data-prep-course

R course: Cleaning ecological survey data for conservation scientists

Chris Brown, Christina Buelow

Welcome to our “Cleaning ecological survey data for conservation scientists” R course site. Below are instructions for getting setup or jump straight to the notes and data.

Check out the conservation hackers site for upcoming online courses

Course notes

Data for course


So that the course runs efficiently, and to save plenty of time for trying fun things in R, we’d ask that you come to the course prepared. This is an intermediate level course, so we’ll assume you know how to install R and R packages. As a general guide to what we expect in terms of prior knowledge, we’ll assume you can run R, load data and write basic calculations that you save in a script. We’ll assume you know nothing about how to structure data, create plots or make data summaries. Even if you know about these topics, you may still find the course helpful if you are self-taught, because we’ll cover the conceptual foundation of these topics.

Please have R (install from here) and Rstudio (we use the free desktop version) installed on your computer before starting. You’ll want to save plenty of time for doing this, it can be tricky on some computers, especially if you do not have ‘admin’ permission on a work computer. You may need to call IT to get help. We obviously only offer limited help with such installation issues. We are using R version >4.0.2 currently for writing this course, so there may be some minor differences if you have a different version. We definitely recommend making sure you have version 4 or greater.

You’ll also need to install a few R packages. We’re using dplyr, readr, lubridate, ggplot2 and tidyr in this course. You can install them with this R code in R’s console: install.packages(c("dplyr", "readr", "lubridate", "ggplot2")) If that doesn’t work email us with the error and we’ll try to help. Otherwise, see your IT department for help. We’re using dplyr version 1.0, which was released earlier this year. If you have an older version of dplyr the course should still work fine, but there may be some minor differences in the code.