Introduction to R Exercises

Open R Sessions 2024

Authors

Etka Yapar

Iain Moodie

Simon Jacobsen Ellerstrand

Violeta Caballero-Lopez

Ximena Alva Caballero

The exercises in this session are intended for you to start to become familiar with R and Rstudio. Do not worry if everything has not “clicked” by the end. You will gain more confidence the more time you spend using these tools.

1 Installing R and RStudio

1.1 R

To install R, follow these steps:

1.1.1 Windows

Go to the CRAN R Project website.
Click on the “Download R for Windows” link.
Click on the “base” link to download the base system.
Click on the “Download R x.x.x for Windows” link (where x.x.x is the latest version).
Once the download is complete, open the installer and follow the on-screen instructions to complete the installation.

1.1.2 macOS

Go to the CRAN R Project website.
Click on the “Download R for macOS” link.
Click on the .pkg file link to download the installer for the latest version of R.
Once the download is complete, open the installer and follow the on-screen instructions to complete the installation.

1.1.3 Linux

For most Linux distributions, you can install R from the command line.

1.2 RStudio

To install RStudio, follow these steps:

Go to the RStudio website.

1.2.1 Windows

Click on the “Download RStudio Desktop for Windows” link.
Once the download is complete, open the installer and follow the on-screen instructions to complete the installation.

1.2.2 macOS

Click on the “Download RStudio Desktop for macOS” link.
Once the download is complete, open the installer and follow the on-screen instructions to complete the installation.

1.2.3 Linux

For most Linux distributions, you can install RStudio from the command line.

2 Getting familiar with RStudio

Once you have downloaded both R and RStudio, open RStudio. Your installation of R should be automatically detected, and you will be greated with an interface that looks like this:

Rstudio is designed around a four panel layout. Currently you can see three of them. To reveal the fourth, go to File -> New file -> R script. Now your window should look like this:

Script: a script is simply a text file, where you write and save commands. It is designed to be read like English, left to right and top to bottom. The order of commands matters! The most common way to work with R is to write your commands in a script, and then send those commands to the console. Saving your script allows you to come back to a project later, or send it to someone else.
Console: the console is where you write or send commands to be executed by the computer. Usually, you have written your commands in the script, and send each line of commands to the console using either the Run button, or by using Ctrl+Enter (Windows) or Cmd+Enter (macOS). You can also send the whole script to the console, using the Source button, or by using Ctrl+Shift+S (Windows) or Cmd+Shift+S (macOS).
Environment: this panel shows you objects loaded into R. We will come back to this later.
Viewer: this panel has many functions, but is commonly used to show the output of your commands (e.g. plots), or to read the R help documentation.

3 Before you start coding

There are some good habits to get into early when you start programming. Most of these apply to any programming language. They help you keep your work organised, and make sure it is reproducible.

3.1 Working directory

We recommend you create a folder where you save all the work you do as part of the Open R Sessions. Within that folder, you should create a subfolder for each session. An example might be a folder called open_r that within it contains folders open_r/01_intro, open_r/02_datatypes, etc. Create a folder and subfolder for this session.

We now want to set our working directory to this folder. A working directory is the directory (folder) in a file system where a user is currently working. It is the default location where commands, scripts, and programs are stored and executed and where files are read from or written to unless specified otherwise. To set the working directory using RStudio, go to Session -> Set working directory -> Choose directory, then navigate to the folder you just made for this session.

You should notice that doing this, the command setwd(your_directory) was sent to the console. Most buttons in RStudio will simply run an R command, that you could also write.

3.2 Creating a script

To create a new script (if you have closed the one you made before), go to File -> New file -> R script. Now save this script into your folder for this session, by going to File -> Save. Give the file a name that makes sense to you. R script files generally end with the extension .R or .r.

3.3 Some good practises

It is possible to write comments in an R script that are not read by the computer. To do this, you start a line with a #. You can use this feature to write notes to yourself as you go, but you can also use it to give your script a title, so you remember what it does, or what it was for. For example, I might include at the start of my script:

# Open R Sessions 01
# 2024-09-19

Add something similar to your script.

Another common practise is to include the command rm(list=ls()) at the start of an R scripts. This command will clear the R environment of all variables. It makes sure that your script will always run the same no matter who runs it. Add this line to the top of your script now.

# Open R Sessions 01
# 2024-09-19

rm(list=ls())

Another good practise is to declare your working directory at the start of a script. You can do this using the setwd() we encountered before.

# Open R Sessions 01
# 2024-09-19

rm(list=ls())

setwd(write your directory here)

Remember to save your script!

You are now setup to do some coding!

4 Exercises

4.1 Importing data to R

You will often want to use R to analyse some of your data. The first step is naturally to import it into R. On the canvas page, you can download a dataset called “Simple_data.txt”. Download it to the folder you set as a working folder. Most of you are used to working with data in Excel files and not text files. Don’t worry! We will talk more about file types in the coming sessions. For now, you want to import the file with read.delim(). First, write the command list.files() into your script, and run it. This lists all the files in your working directory. Does the .txt files appear? If so, then write and run data <- read.delim("Simple_data.txt", head=T, sep="\t").

The data file has now been imported to R and stored in the object data. An easy way of getting an overview of the data is by looking at the top 5 rows with the command head(data). To see the whole file, you can also open it by clicking on the object which has now appeared in the Global Environment. As you can see, it contains four columns with information on some of Simon’s samples of lark species. Return to the script by clicking on the correct tab. To get some basic summary statistics of the datafile you can try a few other commands.

Try str(data), table(data$Species), table(data$Sex), and table(data$Species, data$Sex).

Now, try on your own to make a table containing the number of individuals of each population and species.

Hopefully, this will give you a very fast idea of some of the content of this data. More on importing files and the str() command in the coming sessions!

4.2 R as a very powerful calculator

One of the many ways of using R is simply to use it as a calculator. You just need to write the formula you desire to calculate and R will do it immediately. Note that R will always follow the mathematical calculation priorities. These priorities can be overruled with the use of parenthesis. Some mathematical operators used in R are the following: +, -, *, /, ^. Here are some examples:

23+13

[1] 36

4*5+7

[1] 27

4*(5+7)

[1] 48

4*(5+7)^2

[1] 576

Do you get the same answers?

4.3 Creating variables in R

In R, a variable is a name assigned to a value or a data object, which can be used to store data for later use. Variables in R can hold various types of data, including numbers, strings, vectors, lists, and more. We cover this in the next session. Variables are created using the assignment operator <-.

# Assigning a numeric value to a variable
x <- 10

# Assigning a string value to a variable
course <- "Open R Sessions"

# Assigning a vector to a variable
numbers <- c(1, 2, 3, 4, 5)

# Assigning a list to a variable
person <- list(name = "Alice", age = 30, occupation = "Engineer")

Notice that once you declare a variable, it appears in the environment tab.

Let’s focus on the variable we just made called x. We can now use x in other commands. For example, we could find the square root of x:

sqrt(x)

[1] 3.162278

Notice that if we change what x is, we get a different answer:

x <- 42

sqrt(x)

[1] 6.480741

The order your commands come in matters.

5 Games

Previous members of the Open R Team have coded some games in R! To try them out, go to the canvas page, and follow the instructions in the games_in_r.pdf file.

--- title: "Introduction to R Exercises" subtitle: "Open R Sessions 2024" author: - Etka Yapar - Iain Moodie - Simon Jacobsen Ellerstrand - Violeta Caballero-Lopez - Ximena Alva Caballero format: html: code-fold: true code-tools: true code-overflow: wrap toc: true toc-depth: 4 self-contained: true anchor-sections: true smooth-scroll: true theme: light: flatly dark: darkly number-sections: true number-depth: 3 execute: echo: true warning: false eval: false --- The exercises in this session are intended for you to start to become familiar with R and Rstudio. Do not worry if everything has not "clicked" by the end. You will gain more confidence the more time you spend using these tools. # Installing R and RStudio ## R To install R, follow these steps: ### Windows 1. Go to the [CRAN R Project website](https://cran.r-project.org/). 2. Click on the "Download R for Windows" link. 3. Click on the "base" link to download the base system. 4. Click on the "Download R x.x.x for Windows" link (where x.x.x is the latest version). 5. Once the download is complete, open the installer and follow the on-screen instructions to complete the installation. ### macOS 1. Go to the [CRAN R Project website](https://cran.r-project.org/). 2. Click on the "Download R for macOS" link. 3. Click on the `.pkg` file link to download the installer for the latest version of R. 4. Once the download is complete, open the installer and follow the on-screen instructions to complete the installation. ### Linux For most Linux distributions, you can install R from the command line. ## RStudio To install RStudio, follow these steps: 1. Go to the [RStudio website](https://posit.co/download/rstudio-desktop/). ### Windows 1. Click on the "Download RStudio Desktop for Windows" link. 2. Once the download is complete, open the installer and follow the on-screen instructions to complete the installation. ### macOS 1. Click on the "Download RStudio Desktop for macOS" link. 2. Once the download is complete, open the installer and follow the on-screen instructions to complete the installation. ### Linux For most Linux distributions, you can install RStudio from the command line. # Getting familiar with RStudio Once you have downloaded both R and RStudio, open RStudio. Your installation of R should be automatically detected, and you will be greated with an interface that looks like this: ![](rstudio_fresh_screenshot.png) Rstudio is designed around a four panel layout. Currently you can see three of them. To reveal the fourth, go to *File* -> *New file* -> *R script*. Now your window should look like this: ![](rstudio_panels_screenshot.png) 1. **Script**: a script is simply a text file, where you write and save commands. It is designed to be read like English, left to right and top to bottom. The order of commands matters! The most common way to work with R is to write your commands in a script, and then send those commands to the console. Saving your script allows you to come back to a project later, or send it to someone else. 2. **Console**: the console is where you write or send commands to be executed by the computer. Usually, you have written your commands in the script, and send each line of commands to the console using either the *Run* button, or by using `Ctrl+Enter` (Windows) or Cmd+Enter (macOS). You can also send the whole script to the console, using the *Source* button, or by using `Ctrl+Shift+S` (Windows) or `Cmd+Shift+S` (macOS). 3. **Environment**: this panel shows you objects loaded into R. We will come back to this later. 4. **Viewer**: this panel has many functions, but is commonly used to show the output of your commands (e.g. plots), or to read the R help documentation. # Before you start coding There are some good habits to get into early when you start programming. Most of these apply to any programming language. They help you keep your work organised, and make sure it is reproducible. ## Working directory We recommend you create a folder where you save all the work you do as part of the Open R Sessions. Within that folder, you should create a subfolder for each session. An example might be a folder called `open_r` that within it contains folders `open_r/01_intro`, `open_r/02_datatypes`, etc. Create a folder and subfolder for this session. We now want to set our working directory to this folder. A working directory is the directory (folder) in a file system where a user is currently working. It is the default location where commands, scripts, and programs are stored and executed and where files are read from or written to unless specified otherwise. To set the working directory using RStudio, go to *Session* -> *Set working directory* -> *Choose directory*, then navigate to the folder you just made for this session. You should notice that doing this, the command `setwd(your_directory)` was sent to the console. Most buttons in RStudio will simply run an R command, that you could also write. ## Creating a script To create a new script (if you have closed the one you made before), go to *File* -> *New file* -> *R script*. Now save this script into your folder for this session, by going to *File* -> *Save*. Give the file a name that makes sense to you. R script files generally end with the extension `.R` or `.r`. ## Some good practises It is possible to write comments in an R script that are not read by the computer. To do this, you start a line with a `#`. You can use this feature to write notes to yourself as you go, but you can also use it to give your script a title, so you remember what it does, or what it was for. For example, I might include at the start of my script: ```r # Open R Sessions 01 # 2024-09-19 ``` Add something similar to your script. Another common practise is to include the command `rm(list=ls())` at the start of an R scripts. This command will clear the R environment of all variables. It makes sure that your script will always run the same no matter who runs it. Add this line to the top of your script now. ```r # Open R Sessions 01 # 2024-09-19 rm(list=ls()) ``` Another good practise is to declare your working directory at the start of a script. You can do this using the `setwd()` we encountered before. ```r # Open R Sessions 01 # 2024-09-19 rm(list=ls()) setwd(write your directory here) ``` Remember to save your script! You are now setup to do some coding! # Exercises ## Importing data to R You will often want to use R to analyse some of your data. The first step is naturally to import it into R. On the canvas page, you can download a dataset called “Simple_data.txt”. Download it to the folder you set as a working folder. Most of you are used to working with data in Excel files and not text files. Don’t worry! We will talk more about file types in the coming sessions. For now, you want to import the file with `read.delim()`. First, write the command `list.files()` into your script, and run it. This lists all the files in your working directory. Does the `.txt` files appear? If so, then write and run `data <- read.delim("Simple_data.txt", head=T, sep="\t")`. The data file has now been imported to R and stored in the object `data`. An easy way of getting an overview of the data is by looking at the top 5 rows with the command `head(data)`. To see the whole file, you can also open it by clicking on the object which has now appeared in the Global Environment. As you can see, it contains four columns with information on some of Simon's samples of lark species. Return to the script by clicking on the correct tab. To get some basic summary statistics of the datafile you can try a few other commands. Try `str(data)`, `table(data$Species)`, `table(data$Sex)`, and `table(data$Species, data$Sex)`. Now, try on your own to make a table containing the number of individuals of each population and species. Hopefully, this will give you a very fast idea of some of the content of this data. More on importing files and the `str()` command in the coming sessions! ## R as a very powerful calculator One of the many ways of using R is simply to use it as a calculator. You just need to write the formula you desire to calculate and R will do it immediately. Note that R will always follow the mathematical calculation priorities. These priorities can be overruled with the use of parenthesis. Some mathematical operators used in R are the following: `+`, `-`, `*`, `/`, `^`. Here are some examples: ```{r} #| eval: true #| code-fold: false 23+13 4*5+7 4*(5+7) 4*(5+7)^2 ``` Do you get the same answers? ## Creating variables in R In R, a variable is a name assigned to a value or a data object, which can be used to store data for later use. Variables in R can hold various types of data, including numbers, strings, vectors, lists, and more. We cover this in the next session. Variables are created using the assignment operator `<-`. ```{r} #| code-fold: false #| eval: true # Assigning a numeric value to a variable x <- 10 # Assigning a string value to a variable course <- "Open R Sessions" # Assigning a vector to a variable numbers <- c(1, 2, 3, 4, 5) # Assigning a list to a variable person <- list(name = "Alice", age = 30, occupation = "Engineer") ``` Notice that once you declare a variable, it appears in the environment tab. Let's focus on the variable we just made called `x`. We can now use `x` in other commands. For example, we could find the square root of `x`: ```{r} #| code-fold: false #| eval: true sqrt(x) ``` Notice that if we change what `x` is, we get a different answer: ```{r} #| code-fold: false #| eval: true x <- 42 sqrt(x) ``` The order your commands come in matters. # Games Previous members of the Open R Team have coded some games in R! To try them out, go to the canvas page, and follow the instructions in the `games_in_r.pdf` file.