Data Carpentry R for data analysis for Ecology

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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 for data analysis.

This is an introduction to R designed for participants with no programming experience. These lessons can be taught in 3/4 of a day. They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data.frame, how to deal with factors, how to add/remove rows and columns, and finish with how to calculate summary statistics for each level and a very brief introduction to plotting.

Content Contributors: Sarah Supp, John Blischak, Gavin Simpson, Tracy Teal, Greg Wilson, Diego Barneche, Stephen Turner, Francois Michonneau

*Lesson Maintainers: *

Lesson status: Teaching

Lessons:

1. Lesson 00 Before we start
2. Lesson 01 Introduction to R
3. Lesson 02 Starting with data
4. Lesson 03 Introducing data.frame
5. Lesson 04 Aggregating and analyzing data with dplyr
6. Lesson 05 Data visualisation with ggplot2
7. Lesson 06 R and SQL

Data

Data files for the lesson are available here: (https://github.com/iDigBio/2015-09-23-TDWG-R-ecology/tree/gh-pages/data)[https://github.com/iDigBio/2015-09-23-TDWG-R-ecology/tree/gh-pages/data]

Requirements

Data Carpentry's teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools, but working through this lesson requires working copies of the software described below. To most effectively use these materials, please make sure to install everything before working through this lesson.

Twitter: @datacarpentry