Data Analysis and Visualization in R for Ecologists

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. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R.

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

Episodes

Preparations

Data Carpentry’s teaching is hands-on, and to follow this lesson learners must have R and RStudio installed on their computers. They also need to be able to install a number of R packages, create directories, and download files. To avoid troubleshooting during the lesson, learners should follow the instruction below to download and install everything beforehand. If they are using their own computers this should be no problem, but if the computer is managed by their organization’s IT department they might need help from an IT administrator.

Install R and RStudio

R and RStudio are two separate pieces of software:

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis
RStudio is an integrated development environment (IDE) that makes using R easier. In this course we use RStudio to interact with R.

If you don’t already have R and RStudio installed, follow the instructions for your operating system below. You have to install R before you install RStudio. Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

Windows

Download R from the CRAN website.
Run the .exe file that was just downloaded
Go to the RStudio download page
Under Installers select RStudio x.yy.zzz - Windows Vista/7/8/10 (where x, y, and z represent version numbers)
Double click the file to install it

MacOS

Download R from the CRAN website.
Select the .pkg file for the latest R version
Double click on the downloaded file to install R
It is also a good idea to install XQuartz (needed by some packages)
Go to the RStudio download page
Under Installers select RStudio x.yy.zzz - Mac OS X 10.6+ (64-bit) (where x, y, and z represent version numbers)
Double click the file to install RStudio

Linux

Follow the instructions for your distribution from CRAN, they provide information to get the most recent version of R for common distributions. For most distributions, you could use your package manager (e.g., for Debian/Ubuntu run sudo apt-get install r-base, and for Fedora sudo yum install R), but we don’t recommend this approach as the versions provided by this are usually out of date. In any case, make sure you have at least R 3.3.1.
Go to the RStudio download page
Under Installers select the version that matches your distribution, and install it with your preferred method (e.g., with Debian/Ubuntu sudo dpkg -i rstudio-x.yy.zzz-amd64.deb at the terminal).

Update R and RStudio

If you already have R and RStudio installed, check if your R and RStudio are up to date:

When you open RStudio your R version will be printed in the console on the bottom left. Alternatively, you can type sessionInfo() into the console. If your R version is 4.0.0 or later, you don’t need to update R for this lesson. If your version of R is older than that, download and install the latest version of R from the R project website for Windows, for MacOS, or for Linux
To update RStudio to the latest version, open RStudio and click on Help -> Check for updates. If a new version is available, quit RStudio, follow the instruction on screen.

Install required R packages

During the course we will need a number of R packages. Packages contain useful R code written by other people. We will use the packages tidyverse, hexbin, patchwork, and RSQLite.

To try to install these packages, open RStudio and copy and paste the following command into the console window (look for a blinking cursor on the bottom left), then press the Enter (Windows and Linux) or Return (MacOS) to execute the command.

install.packages(c("tidyverse", "hexbin", "patchwork", "RSQLite"))

Alternatively, you can install the packages using RStudio’s graphical user interface by going to Tools -> Install Packages and typing the names of the packages separated by a comma.

R tries to download and install the packages on your machine. When the installation has finished, you can try to load the packages by pasting the following code into the console:

library(tidyverse)
library(hexbin)
library(patchwork)
library(RSQLite)

If you do not see an error like there is no package called ‘...’ you are good to go!

Download the data

We will download the data directly from R during the lessons. However, if you are expecting problems with the network, it may be better to download the data beforehand and store it on your machine. The data files for the lesson can be downloaded manually here: https://doi.org/10.6084/m9.figshare.1314459