Skip to content
This repository has been archived by the owner on Jul 10, 2024. It is now read-only.

Latest commit

 

History

History
20 lines (11 loc) · 1.45 KB

index.md

File metadata and controls

20 lines (11 loc) · 1.45 KB
site
sandpaper::sandpaper_site

:::::::::::::::::::::::::: callout

This lesson changed a lot recently!

You may notice some big changes in the content of this lesson since you last visited.

The Data Carpentry Ecology Curriculum Advisory Committee recently approved this redesigned version of the lesson for adoption into the curriculum! It replaced the previous version in the curriculum in July 2024.

::::::::::::::::::::::::::::::::::

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 ecology data in R.

This is an introduction to R designed for participants with no programming experience. This lesson can be taught in a day (~ 6 hours). It starts with information about the R programming language and the RStudio interface. It then moves to loading in data and exploring how to visualise it with ggplot2. The next episode takes learners through an exploration of data frames and some common data cleaning operations, before discussing vectors and factors. The final episode introduces the flow of data in R, and how to combine operations to select, filter, and mutate a data frame.

This lesson assumes no prior knowledge of R or RStudio and no programming experience.