forked from datacarpentry/R-ecology-lesson
-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.Rmd
155 lines (115 loc) · 6.54 KB
/
index.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
---
title: "Data Carpentry: R for data analysis and visualization of Ecological Data"
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.
## Chapters
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. [Aggregating and analyzing data with dplyr](03-dplyr.html)
5. [Data visualization with **`ggplot2`**](04-visualization-ggplot2.html)
6. [R and Databases](05-r-and-databases.html)
## Requirements
Data Carpentry's teaching is hands-on, so participants are encouraged to use
their own computers to ensure 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 download
the data and install everything *before* working through this lesson.
### Data
Data files for the lesson are available and can be downloaded manually here: <https://doi.org/10.6084/m9.figshare.1314459>
However, we will download them directly from R during the lessons when we need
them.
### Setup instructions
**R** and **RStudio** are separate downloads and installations. R is the
underlying statistical computing environment, but using R alone is no
fun. RStudio is a graphical integrated development environment (IDE) that makes
using R much easier and more interactive. You need to install R before you
install RStudio. After installing both programs, you will need to install the
**`tidyverse`** package from within RStudio. Follow the instructions below for
your operating system, and then follow the instructions to install
**`tidyverse`** and **`RSQLite`**.
#### Windows
##### If you already have R and RStudio installed
* Open RStudio, and click on "Help" > "Check for updates". If a new version is
available, quit RStudio, and download the latest version for RStudio.
* To check which version of R you are using, start RStudio and the first thing
that appears in the console indicates the version of R you are
running. Alternatively, you can type `sessionInfo()`, which will also display
which version of R you are running. Go on
the [CRAN website](https://cran.r-project.org/bin/windows/base/) and check
whether a more recent version is available. If so, please download and install
it. You can [check here](https://cran.r-project.org/bin/windows/base/rw-FAQ.html#How-do-I-UNinstall-R_003f) for
more information on how to remove old versions from your system if you wish to do so.
##### If you don't have R and RStudio installed
* Download R from
the [CRAN website](http://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
XP/Vista/7/8** (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
##### If you already have R and RStudio installed
* Open RStudio, and click on "Help" > "Check for updates". If a new version is
available, quit RStudio, and download the latest version for RStudio.
* To check the version of R you are using, start RStudio and the first thing
that appears on the terminal indicates the version of R you are running. Alternatively, you can type `sessionInfo()`, which will also display which version of R you are running. Go on
the [CRAN website](https://cran.r-project.org/bin/macosx/) and check
whether a more recent version is available. If so, please download and install
it.
##### If you don't have R and RStudio installed
* Download R from
the [CRAN website](http://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.
#### For everyone
**After installing R and RStudio, you need to install the `tidyverse` and
`RSQLite` packages.**
* After starting RStudio, at the console type:
`install.packages(c("tidyverse", "RSQLite"))`
## Contributors
The list of contributors to this lesson is available [here](http://datacarpentry.org/R-ecology-lesson/CITATION),
```{r, child="_page_built_on.Rmd"}
```