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index.Rmd
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---
title: "Data Analysis and Visualization in R for Ecologists"
author: François Michonneau & Auriel Fournier (Lesson Maintainers)
---
<p></p>
<div style="text-align: center; margin-top: 30px; margin-bottom: 30px;">
</div>
<p></p>
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
1. [Before we start](00-before-we-start.html)
2. [Introduction to R](01-intro-to-r.html)
3. [Starting with data](02-starting-with-data.html)
4. [Manipulating, analyzing and exporting data with **`tidyverse`**](03-dplyr.html)
5. [Data visualization with **`ggplot2`**](04-visualization-ggplot2.html)
6. [SQL databases and R](05-r-and-databases.html)
## 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.
#### Windows
* Download R from the
[CRAN website](https://cran.r-project.org/bin/windows/base/release.htm).
* Run the `.exe` file that was just downloaded
* Go to the [RStudio download page](https://www.rstudio.com/products/rstudio/download/#download)
* 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
* Once it's installed, open RStudio to make sure it works and you don't get any
error messages.
##### MacOS
* Download R from
the [CRAN website](https://cran.r-project.org/bin/macosx/).
* 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](https://www.xquartz.org/) (needed
by some packages)
* Go to the [RStudio download page](https://www.rstudio.com/products/rstudio/download/#download)
* 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
* Once it's installed, open RStudio to make sure it works and you don't get any
error messages.
##### Linux
* Follow the instructions for your distribution
from [CRAN](https://cloud.r-project.org/bin/linux), 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](https://www.rstudio.com/products/rstudio/download/#download)
* 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).
* Once it's installed, open RStudio to make sure it works and you don't get any
error messages.
### 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](https://cran.r-project.org/bin/windows/base/),
[for MacOS](https://cran.r-project.org/bin/macosx/),
or [for Linux](https://cran.r-project.org/bin/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.
Note: It is not necessary to remove old versions of R from your system,
but if you wish to do so you can
[check here.](https://cran.r-project.org/bin/windows/base/rw-FAQ.html#How-do-I-UNinstall-R_003f)
### 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 <kbd>Enter</kbd> (Windows and Linux) or <kbd>Return</kbd> (MacOS)
to execute the command.
```{r eval=FALSE}
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:
```{r eval=FALSE}
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>
## Contributors
The list of contributors to this lesson is available [here](https://datacarpentry.org/R-ecology-lesson/CITATION).
```{r, child="_page_built_on.Rmd"}
```