Introduction to R, RStudio, and Quarto

CS&SS 508 • Lecture 1

26 March 2024

Victoria Sass

Introductions & Syllabus

Welcome!

• Introductions
• Syllabus
• Lecture 1: Introduction to R, RStudio, and Quarto

Who Am I?

• Victoria (Vic) Sass

• PhD Candidate in Sociology

• I’ve been using R and RStudio for over 10 years 😱

• I love teaching this class 🤓

Introductions

Let’s start by getting to know each other a bit better. On your index card write the following:

• Name and pronouns

• Program and year

• Experience with programming (in R or more generally)

• One word that best describes your feelings about taking this class

• Would you rather be able to converse with (non-human) animals, or have lifelong fluency in every (human) language?

Pair up with someone nearby and introduce yourself to one another. Let’s take about 5-10 minutes to do this.

Syllabus

The syllabus (as well as lots of other information) can be found on our course website:

https://vsass.github.io/CSSS508

Feel free to follow along online as I run through the syllabus!

Course goals

This course is intended to give students a foundational understanding of programming in the statistical language R. This knowledge is intended to be broadly useful wherever you encounter data in your education and career. General topics we will focus on include:

• Developing intermediate data management and visualization skills
• Organizing projects and creating reproducible research
• Cleaning data
• Linking multiple data sets together
• Learning basic programming skills

By the end of this course you should feel confident approaching any data you encounter in the future. We will cover almost no statistics, however it is the intention that this course will leave you prepared to progress in CS&SS or STAT courses with the ability to focus on statistics instead of coding. Additionally, the basic concepts you learn will be applicable to other programming languages and research in general, such as logic and algorithmic thinking.

Logistics: General

Lecture: On Tuesdays we will meet in the CSSCR lab for an interactive session where we’ll cover a specific topic to help you learn fundamental skills, concepts, and principles for learning R. Additionally, these sessions will provide you with the opportunity to work with each other to learn and practice key skills in R. I will be available to answer questions and help troubleshoot code as well.

Office Hours: Drop-in to ask questions, get advice, or continue discussions from lab/lecture. We can talk in a breakout room or with the group!

• Wednesdays, 11am-1pm (on Zoom; link on Canvas)

How to Contact Me

Please message me in our Slack Workspace rather than sending me an email. I get far too many emails a day and I don’t want to miss your message!

Logistics: Three Tools for Class

Communication

Learning is collaborative! In addition to being the place to communicate with me, our Slack is also where you can ask one another questions, share resources, and just generally check in with each other about how your adventures with R are going. You can find the link to join our workplace on our course Canvas.

Homework & Peer-Reviews

We will be using Canvas solely for homework & peer review submissions/deadlines and for any links I only want to distribute to those registered for this class (i.e. Slack and Office Hours Zoom).

Course Content

All course content will be accessible on our course website: https://vsass.github.io/CSSS508.

Slack

If you’ve never used Slack before you’ll need to download the desktop app.

A useful quick-start guide can be found here.

Go to our Canvas site for the invite link to join our private workspace.

Schedule

March 26

April 2

April 9

April 16

April 23

April 30

May 7

May 14

May 21

May 28

Week 1: Introduction to R, RStudio, and Quarto

Week 2: Visualizing Data

Week 3: Workflow and Reproducibility

Week 4: Importing, Exporting, and Cleaning Data

Week 5: Manipulating and Summarizing Data

Week 6: Data Structures & Types

Week 7: Working with Text Data

Week 8: Writing Functions

Week 9: Iteration

Week 10: Next Steps

Prerequisites

None 😎

Course Materials

Materials: All course materials will be provided on the course website. This includes:

• Lecture slides and the code used to generate them.
• Homework instructions and/or templates.
• Recommended reading/cheatsheet(s).
• Useful links to other resources.

Laptops: You’re welcome to bring a laptop to class if you’d prefer to use your own machine.

 Keep In Mind

The versions of R, RStudio, and Quarto (as well as any packages you have installed) will not necessarily be the same/up to date if you do your work on different computers. My advice is to consistently use the same device for homework assignments or to make sure to download the latest versions of R, RStudio, and Quarto when using a new machine.

Readings

Textbooks: This course has no textbook. However, I will be suggesting selections from R for Data Science to pair with each week’s topic. While not required, I strongly suggest reading those selections before doing the homework for that week.

Course Assessment

Final grade

Credit/No Credit (C/NC); You need at least 60% to get Credit

Homework (75%; assessed by peers)

9 total homeworks; assessed on a 0-3 point rubric. Assigned at the end of lecture sessions and due a week later.

Evaluation	Points
Didn't turn anything in.	0
Turned in but low effort, ignoring many directions.	1
Decent effort, followed directions with some minor issues.	2
Nailed it!	3

Peer Grading (25%; assessed by me)

One per homework, assessed on a binary satisfactory/unsatisfactory scale. Due 5 days after homework due date.

Evaluation	Points
Didn’t follow all peer-review instructions.	0
Peer review is at least several sentences long, mentions any and all key issues from the assignment, and points out at least one positive thing in your peer’s work (and hopefully more!).	1

Due Dates and Late Policy

Homework/peer grading instructions and deadlines can be found on the Homework page of the course website. All homework will be turned in on Canvas by 4:30pm the day it is due.

Late Homework Will Automatically Lose Peer-Review Credit

Peer reviews are randomly assigned when the due date/time is reached. Therefore, if you don’t submit your homework on time, you will not be given a peer’s homework to review and vice versa. That said, life is messy and complicated and we all miss deadlines for a variety of reasons. Therefore, you can request that I review and provide feedback on a late assignment (message me on Slack) but you won’t be able to earn peer-review credit for that particular homework.

Ugh, peer grading?

Yes, because:

• You will write your reports better knowing others will see them

• You learn alternate approaches to the same problem

• You will have more opportunities to practice and have the material sink in

How to peer review:

• Leave constructive comments: You’ll only get the point if you write at least several sentences that includes
  • Any key issues from the assignment and,
  • Points out something positive in your peer’s work.
• Send me a message on Slack if you would like your assignment to be regraded or for me to provide feedback if no peer review was given.

Academic Integrity

Academic integrity is essential to this course and to your learning. Violations of the academic integrity policy include but are not limited to:

• Copying from a peer
• Copying from an online resource
• Using resources from a previous iteration of the course.

I hope you will collaborate with peers on assignments and use Internet resources when questions arise to help solve issues. The key is that you ultimately submit your own work.

Anything found in violation of this policy will be automatically given a score of 0 with no exceptions. If the situation merits, it will also be reported to the UW Student Conduct Office, at which point it is out of my hands. If you have any questions about this policy, please do not hesitate to reach out and ask.

Classroom Environment

I’m committed to fostering a friendly and inclusive classroom environment in which all students have an equal opportunity to learn and succeed. This course is an attempt to make an often difficult and frustrating experience (learning R for the first time) less obfuscating, daunting, and stressful. That said, learning happens in different ways at at a different pace for everyone. Learning is also a collaborative and creative process and my aim is to create an environment in which you all feel comfortable asking questions of me and each other. Treat your peers and yourself with empathy and respect as you all approach this topic from a range of backgrounds and experiences (in programming and in life).

Classroom Environment…

Names & Pronouns: Everyone deserves to be addressed respectfully and correctly. Fill out your profile on Slack with your picture, preferred name (as your Display Name), and correct gender pronouns so we can all be on the same page!

Covid Considerations: I will follow all University rules and procedures regarding Covid, which may or may not change during the quarter. I also recognize that Covid creates unique circumstances and concerns for each of us, which may limit your ability to fully attend or participate in this course. You never need to apologize to me for anything pandemic-related. If there is something I can do to make you feel more comfortable during class, please let me know!

Diversity: Diverse backgrounds, embodiments, and experiences are essential to the critical thinking endeavor at the heart of university education. Therefore, I expect you to follow the UW Student Conduct Code in your interactions with your colleagues and me in this course by respecting the many social and cultural differences among us, which may include, but are not limited to: age, cultural background, disability, ethnicity, family status, gender identity and presentation, body size/shape, citizenship and immigration status, national origin, race, religious and political beliefs, sex, sexual orientation, socioeconomic status, and veteran status.

Accommodations

Accessibility & Accommodations: Your experience in this class is important to me. If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course. If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924, uwdrs@uw.edu, or through their website.

Religious Accommodations: Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW's policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Help and Feedback

Getting Help: If at any point during the quarter you find yourself struggling to keep up, please let me know! I am here to help. A great place to start this process is by chatting before¹ class, coming to office hours, or message meon Slack.

Also, help one another as you navigate this course! Slack allows you to chat directly with one another, send messages to the whole class about specific topics (see the already-created # r-code-questions and # quarto-questions channels), send snippets of code or entire files to one another, and much more.

Feedback

If you have feedback on any part of this course or the classroom environment I want to hear it! You can message me directly on Slack or send me an anonymous message here. Additionally, I will send out a mid-quarter feedback survey on Slack around Week 5.

1. Unfortunately I have to leave right after class to catch the ferry so if you want to chat in person, come early to class. I’ll be hanging out starting at 4pm each Tuesday.

Asking Questions

Don’t ask like this:

tried lm(y~x) but it iddn’t work wat do

Instead, ask like this:

y <- seq(1:10) + rnorm(10)
x <- seq(0:10)
model <- lm(y ~ x)

Running the block above gives me the following error, anyone know why?

Error in model.frame.default(formula = y ~ x, 
drop.unused.levels = TRUE) : variable lengths differ 
(found for 'x')

FYI

If you ask me a question directly over Slack I may send out your question (anonymously) along with my answer to the whole course.

Questions?

Introduction to R, RStudio, and Quarto

A Note on Slide Formatting

Bold usually indicates an important vocabulary term. Remember these!

Italics indicate emphasis but also are used to point out things you must click with a mouse.

• For example: “Please click File > Print”

Code represents R code you could use to perform actions.

• For example: “Press Ctrl-P to open the print dialogue.”

Code chunks that span the page represent actual R code embedded in the slides.

7 * 49

A Note on Slide Formatting

Bold usually indicates an important vocabulary term. Remember these!

Italics indicate emphasis but also are used to point out things you must click with a mouse.

• For example: “Please click File > Print”

Code represents R code you could use to perform actions.

• For example: “Press Ctrl-P to open the print dialogue.”

Code chunks that span the page represent actual R code embedded in the slides.

7 * 49 
# Sometimes important stuff is highlighted!

A Note on How to Use These Slides

Since the lectures for this class were created using Quarto, there are numerous built-in features meant to facilitate your learning, particularly of R.

1. The in the bottom left-hand corner will show you a table of contents for the entire slideshow, allowing you to find what you’re looking for more easily.
2. Anything followed by is a link to an external site. You will be shown a preview (if available) within the presentation first and from there you can open the link in a new tab to explore it more.
3. If you hover over any chunk of R code embedded in the slides you will see a which you can click to copy the code. You can then paste it in your own Quarto document or R script to run it in your session of RStudio.
4. To get a PDF version of these slides click File > Print from your internet browser, select Save as PDF as the Destination or Printer, and make sure the Layout is set to Landscape. (Note: the PDF Export Mode in Tools actually cuts off content which is why I’m not recommending it)
5. Clicking on the in the bottom left-hand corner allows you to draw directly on the slides, in a very Microsoft Paint kind of way. See if it’s useful but I make no promises!
6. Type ? at any time to see all the available key-board shortcuts.
7. Some content may be scrollable (like this page!). If this is the case I will put the icon in the title to let you know.

Why R?

R is a programming language built for statistical computing.

If one already knows Stata or similar software, why use R?

• R is free.

• R has a very large community.

• R can handle virtually any data format.

• R makes replication easy.

• R is a language so it can do everything.

• R skills transfer to other languages like Python and Julia.

R Studio

R Studio is a “front-end” or integrated development environment (IDE) for R that can make your life easier.

We’ll show RStudio can…

• Organize your code, output, and plots
• Auto-complete code and highlight syntax
• Help view data and objects
• Enable easy integration of R code into documents with Quarto

It can also…

• Manage git repositories

• Run interactive tutorials

• Handle other languages like C++, Python, SQL, HTML, and shell scripting

Selling You on Quarto

Built upon many of the developments of the R Markdown ecosystem, Quarto distills them into one coherent system and additionally expands its functionality by supporting other programming languages besides R, including Python and Julia.

Selling You on Quarto

The ability to create Quarto files in R is a powerful advantage. It allows us to:

• Document analyses by combining text, code, and output
  • No copying and pasting into Word
  • Easy for collaborators to understand
  • Show as little or as much code as you want
• Produce many different document types as output
  • PDF documents
  • HTML webpages and reports
  • Word and PowerPoint documents
    
  • Presentations (like these slides)
  • Books
  • Theses/Dissertations 😉🎓
  • Websites (like the one for this course!)
• Works with LaTeX and HTML for math and more formatting control

Downloading R and RStudio

If you don’t already have R and RStudio on your machine, now is the time to do so!

1. Go to the course homepage, https://vsass.github.io/CSSS508
2. Click the Download R link and download R to your machine.
3. Afterwards, click the Download RStudio link and download RStudio to your machine.
4. Lastly, click the Download Quarto link and download Quarto to your machine.

Getting Started

Open up RStudio now and choose File > New File > R Script.

Then, let’s get oriented with the interface:

• Top Left: Code editor pane, data viewer (browse with tabs)

• Bottom Left: Console for running code (> prompt)

• Top Right: List of objects in environment, code history tab.

• Bottom Right: Tabs for browsing files, viewing plots, managing packages, and viewing help files.

Editing and Running Code

There are several ways to run R code in RStudio:

• Highlight lines in the editor window and click     Run at the top right corner of said window or hit Ctrl+Enter or ⌘+Enter to run them all.

Editing and Running Code

There are several ways to run R code in RStudio:

• Highlight lines in the editor window and click     Run at the top right corner of said window or hit Ctrl+Enter or ⌘+Enter to run them all.

• With your caret¹ on a line you want to run, hit Ctrl+Enter or ⌘+Enter. Note your caret moves to the next line, so you can run code sequentially with repeated presses.

• Type individual lines in the console and press Enter.

• In quarto documents, click within a code chunk and click the green arrow    to run the chunk. The button beside that (   ) runs all prior chunks.

The console will show the lines you ran followed by any printed output.

1. This thing is the caret: |

Incomplete Code

If you mess up (e.g. leave off a parenthesis), R might show a + sign prompting you to finish the command:

> (11-2
+

Finish the command or hit Esc to get out of this.

R as a Calculator

In the console, type 123 + 456 + 789 and hit Enter.

123 + 456 + 789

[1] 1368

The [1] in the output indicates the numeric index of the first element on that line.

Now in your blank R document in the editor, try typing the line sqrt(400) and either clicking Run or hitting Ctrl+Enter or ⌘+Enter.

sqrt(400)

[1] 20

Functions

sqrt() is an example of a function in R.

Arguments are the inputs to a function. In this case, the only argument to sqrt() is x which can be a number or a vector of numbers.

The basic template of a function is

function_name(argument1, argument2 = value2, argument3 = value3...)

 Something to Note

Functions can have a wide range of arguments and some are required for the function to run, while others remain optional. You can see from each functions’ help page which are not required because they will have an = with some default value pre-selected. If there is no = it is up to the user to define that value and it’s therefore a required specification.

Help

If we didn’t have a good guess as to what sqrt() will do, we can type ?sqrt in the console and look at the Help panel on the bottom right.

?sqrt

If you’re trying to look up the help page for a function and can’t remember its name you can search by a keyword and you will get a list of help pages containing said keyword.

??exponential

Help

Help files provide documentation on how to use functions and what functions produce. They will generally consist of the following sections:

• Description - What does it do?
• Usage - How do you write it?
• Arguments - What arguments does it take; which are required; what are the defaults?
• Details - A more in-depth description
• Value - What does the function return?
• See Also - Related R functions
• Examples - Example (& reproducible) code

Objects

R stores everything as an object, including data, functions, models, and output.

Creating an object can be done using the assignment operator: <- . . .

new.object <- 144

Operators like <- are functions that look like symbols but typically sit between their arguments (e.g. numbers or objects) instead of having them inside () like in sqrt(x).

We do math with operators, e.g., x + y.

+ is the addition operator!

Calling Objects

You can display or “call” an object simply by using its name.

new.object

[1] 144

Naming Objects

Object names must begin with a letter and can contain letters, numbers, ., and _.

Try to be consistent in naming objects. RStudio auto-complete means long, descriptive names are better than short, vague ones! Good names save confusion later!

• snake_case, where you separate lowercase words with _ is a common and practical naming convention that I strongly recommend.

snake_case_is_easy_to_read
CamelCaseIsAlsoAnOptionButSortOfHardToReadQuickly
some.people.use.periods
And_some.People_ARETRUErebels

Remember that object names are CaSe SeNsItIvE!!

Also, TYPOS MATTER!

Using Objects

An object’s name represents the information stored in that object, so you can treat the object’s name as if it were the values stored inside. . . .

new.object + 10

[1] 154

new.object + new.object

[1] 288

sqrt(new.object)

[1] 12

Comments

Anything writen after #¹ will be ignorned by R.

# create vector of ages of students
ages <- c(45, 21, 27, 34, 23, 24, 24)

# get average age of students
mean(ages)

[1] 28.28571

Comments help collaborators and future-you understand what, and more importantly, why you are doing what you’re doing with that specific line/chunk of code.

Additionally, comments allow you to explain your overall coding plan and record anything important that you’ve discovered along the way.

1. In Quarto documents, comments only work in code chunks. Outside of a chunk, # creates headers like “comments” at the top of this slide.

Vectors

A vector is one of many data types available in R. Specifically, it is a series of elements, such as numbers, strings, or booleans (i.e. TRUE, FALSE).

You can create a vector using the function c() which stands for “combine” or “concatenate”. . . .

new.object <- c(4, 9, 16, 25, 36)
new.object

[1]  4  9 16 25 36

If you name an object the same name as an existing object, it will overwrite it.

You can provide a vector as an argument for many functions. . . .

sqrt(new.object)

[1] 2 3 4 5 6

More Complex Objects

There are other, more complex data types in R which we will discuss later in the quarter! These include matrices, arrays, lists, and dataframes.

Most data sets you will work with will be read into R and stored as a dataframe, so this course will mainly focus on manipulating and visualizing these objects.

Quarto

Creating a Quarto Document

Let’s try making an Quarto file:

1. Choose File > New File > Quarto Document…
2. Make sure HTML Output is selected
3. In the Title box call this test document My First Qmd and click Create
4. Save this document somewhere (you can delete it later) (either with File > Save or clicking    towards the top left of the source pane).
5. Lastly, click    Render at the top of the source pan to “knit” your document into an html file. This will produce a minimal webpage since we only have a title. We need to add more content!

If you want to create PDF output in the future, you’ll need to run the following code in your console.

install.packages("quarto")
install.packages('tinytex')
tinytex::install_tinytex()

Anatomy of a Quarto Document

• Quarto file
• Rendered html document

---
title: "ggplot2 demo"
author: "Norah Jones"
date: "5/22/2021"
format: 
  html:
    fig-width: 8
    fig-height: 4
    code-fold: true
---

## Air Quality

@fig-airquality further explores the impact of temperature on ozone level.

```{r}
#| label: fig-airquality
#| fig-cap: "Temperature and ozone level."
#| warning: false

library(ggplot2)
ggplot(airquality, aes(Temp, Ozone)) + 
  geom_point() + 
  geom_smooth(method = "loess")
```

The rendered output of the qmd file shown on the previous tab.

Elements of a Quarto document include:

1. An (optional) YAML header (surrounded by ---s).

2. Plain text and any associated formatting.

3. Chunks of code (surrounded by ``` s) and/or their output.

Quarto Headers

The header of an .qmd file is a YAML¹code block, and everything else is part of the main document. Try adding some of these other fields to your YAML and re-render it to see what it looks like.

---
title: "Untitled"
author: "Victoria Sass"
date: "March 26, 2024"
output: html_document
---

To mess with global formatting, you can modify the header².

output:
  html_document:
    theme: readable

1. You can read a bit more about YAML headers in Quarto here and this reference page lists all options possible for html output.

2. Be careful though, YAML is space-sensitive; indents matter!

Quarto Syntax

Output

bold/strong emphasis
italic/normal emphasis

Header

Subheader

Subsubheader

Block quote from famous person

Quarto Syntax

**bold/strong emphasis**
*italic/normal emphasis*
  
  
  
# Header
  
  
  
## Subheader
  


### Subsubheader
  



> Block quote from
> famous person

Quarto Syntax¹ Continued

Output

1. Ordered lists
2. Are real easy
   1. Even with sublists
   2. Or with lazy numbering

• Unordered lists
• Are also real easy
  • Also even with sublists
    • And subsublists

Syntax

1. Ordered lists
1. Are real easy
    1. Even with sublists
    1. Or with lazy numbering
  
    
    
    
    
* Unordered lists
* Are also real easy
  + Also even with sublists
    - And subsublists

1. This is all basic markdown syntax which you can learn about here.

Formulae and Syntax

Output

Include math \(y= \left( \frac{2}{3} \right)^2\) inline.

Or centered on your page like so:

\[\frac{1}{n} \sum_{i=1}^{n} x_i = \bar{x}_n\]

Or write code-looking font.

Or a block of code:

y <- 1:5
z <- y^2

Syntax

Include math $y= \left(\frac{2}{3} \right)^2$ inline. 

Or centered on your page like so: 
  
$$\frac{1}{n} \sum_{i=1}^{n}x_i = \bar{x}_n$$


  
  
Or write`code-looking font`.

Or a block of code:

```{r}
y <- 1:5
z <- y^2
```

Try copying any of the code chunks from the previous three slides to add some formatted text to your own qmd.

Quarto Tinkering

Quarto docs can be modified in many ways. Visit these links for more information.

• Getting started with Quarto
  • Tutorial: Hello, Quarto
  • Tutorial: Computations
  • Tutorial: Authoring
• Ways to modify the overall document appearance
• Ways to format parts of your document
• Learn about the visual editor here

R Code in Quarto

Inside Quarto, lines of R code are called chunks. Code is sandwiched between sets of three backticks and {r}. This chunk of code…

```{r}
summary(cars)
```

Produces this output in your document:

summary(cars)

     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00  

Add this code chunk to your document!

Chunk Options

Chunks have options that control what happens with their code. They are specified as special comments at the top of a block. For example:

```{r}
#| label: bar-chart
#| eval: false
#| fig-cap: "A line plot on a polar axis"
```

Chunk Options

Some useful and common options include:

• echo: false - Keeps R code from being shown in the document

• eval: false - Shows R code in the document without running it

• include: false - Hides all output but still runs code (good for setup chunks where you load packages!)

• output: false - Doesn’t include the results of that code chunk in the output

• cache: true - Saves results of running that chunk so if it takes a while, you won’t have to re-run it each time you re-render the document

• fig.height: 5, fig.width: 5 - modify the dimensions of any plots that are generated in the chunk (units are in inches)

• fig.cap: "Text" - add a caption to your figure in the chunk

Playing with Chunk Options

Try adding or changing the chunk options for the chunk in my_first_Rmd.qmd and re-render your document to see what happens.

```{r}
#| eval: false
summary(cars)
```

In-Line R code

Sometimes we want to insert a value directly into our text. We do that using code in single backticks starting off with r.

Four score and seven years ago is the same as `r 4*20 + 7` years.

Four score and seven years ago is the same as 87 years.

Maybe we’ve saved a variable in a code chunk that we want to reference in the text:

x <- sqrt(77)

The value of `x` rounded to the nearest two decimals is `r round(x, 2)`.

The value of x rounded to the nearest two decimals is 8.77.

This is Amazing!

Having R dump values directly into your document protects you from silly mistakes:

• Never wonder “how did I come up with this quantity?” ever again: Just look at your formula in your .qmd file!

• Consistency! No “find/replace” mishaps; reference a variable in-line throughout your document without manually updating if the calculation changes (e.g. reporting sample sizes).

• You are more likely to make a typo in a “hard-coded” number than you are to write R code that somehow runs but gives you the wrong thing.

Example: Keeping Dates

In your YAML header, make the date come from R’s Sys.time() function by changing:

date: "March 26, 2024"

to

date: "`r Sys.time()`"

Base R and Packages

Base R

Simply by downloading R you have access to what is referred to as Base R. That is, the built-in functions and datasets that R comes equipped with, right out of the box.

Examples that we’ve already seen include <-, sqrt(), +, Sys.time(), and summary() but there are obviously many many more.

You can see a whole list of what Base R contains by running library(help = "base") in the console.

A Base R Dataset: cars

In the sample Quarto document you are working on, we can load the built-in data cars, which loads as a dataframe, a type of object mentioned earlier. Then, we can look at it in a couple different ways.

data(cars) loads this dataframe into the Global Environment.

View(cars) pops up a Viewer tab in the source pane (“interactive” use only, don’t put in Quarto document!).

head(cars, 5) # prints first 5 rows, can use tail() too

  speed dist
1     4    2
2     4   10
3     7    4
4     7   22
5     8   16

A Base R Dataset: cars

str() displays the structure of an object:

str(cars) # str[ucture]

'data.frame':   50 obs. of  2 variables:
 $ speed: num  4 4 7 7 8 9 10 10 10 11 ...
 $ dist : num  2 10 4 22 16 10 18 26 34 17 ...

A Base R Dataset: cars

str() displays the structure of an object:

str(cars) # str[ucture]

'data.frame':   50 obs. of  2 variables:
 $ speed: num  4 4 7 7 8 9 10 10 10 11 ...
 $ dist : num  2 10 4 22 16 10 18 26 34 17 ...

summary() displays summary information ¹:

summary(cars)

     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00  

1. Note R is object-oriented which means summary() provides different information for different types of objects!

Base R is pretty…Basic

hist() generates a histogram of a vector. Note that you can access a vector that is a column of a dataframe using $, the extract operator.

hist(cars$speed) # Histogram

hist(cars$dist)

Base R is pretty…Basic

We can try and make this histogram a bit more appealing by adding more arguments and their specifications.

• Code
• Plot

hist(cars$dist,
     xlab = "Distance (ft)", # X axis label
     main = "Observed stopping distances of cars") # Title

Base R is pretty…Basic

We can also make scatterplots to show the relationship between two variables.

• Code
• Plot

plot(dist ~ speed, data = cars,
     xlab = "Speed (mph)",
     ylab = "Stopping distance (ft)",
     main = "Speeds and stopping distances of cars",
     pch = 16) # Point shape
abline(h = mean(cars$dist), col = "firebrick") # add horizontal line (y-value)
abline(v = mean(cars$speed), col = "cornflowerblue") # add vertical line (x-value)

Note

dist ~ speed is a formula of the type y ~ x. The first element (dist) gets plotted on the y-axis and the second (speed) goes on the x-axis. Regression formulae follow this convention as well!

Base R is pretty…Basic

We can also make scatterplots to show the relationship between two variables.

• Code
• Plot

plot(dist ~ speed, data = cars,
     xlab = "Speed (mph)",
     ylab = "Stopping distance (ft)", # add y-axis label
     main = "Speeds and stopping distances of cars",
     pch = 16) # Point shape
abline(h = mean(cars$dist), col = "firebrick") # add horizontal line
abline(v = mean(cars$speed), col = "cornflowerblue") # add vertical line

Another Base R Dataset: swiss

Let’s look at another built-in dataset.

First, run ?swiss in the console to see what things mean.

Then, load it using data(swiss)

Add chunks to your Quarto document inspecting swiss, defining variables, doing some exploratory plots using hist or plot.

You might experiment with colors and shapes.

Looking at swiss

• Code
• Plot

pairs(swiss, 
      pch = 8, 
      col = "violet",
      main = "Pairwise comparisons of Swiss variables")

pairs() is a pairwise scatterplot function. Good for a quick look at small datasets with numerical/continuous data, but mostly useless for larger data.

Packages

What makes R so powerful though is it’s extensive library of packages. Due to it’s open-source nature, anyone (even you!) can write a package that others can use.

Packages contain pre-made functions and/or data that can be used to extend Base R’s capabilities.

 Base R/Package Analogy

Base R is like creating a recipe from scratch: going to the store and buying all the ingredients and cooking it by yourself. Using a package is more akin to using a meal-kit service: you still have to cook but you’re provided with the ingredients and step-by-step instructions for making the recipe.

As of this writing there are 20,613¹ available packages!

1. To put this in perspective, there were 19,940 available packages the last time I taught this course (Fall 2023).

Installing Packages

To use a package outside of Base R you need to do two things:

Installing Packages

To use a package outside of Base R you need to do two things:

1. Download the package from CRAN (The Comprehensive R Archive Network) by running the following in your console:¹

install.packages("package_name")

1. You never want to include this line of code in a Quarto document or an R Script

Installing Packages

To use a package outside of Base R you need to do two things:

1. Download the package from CRAN (The Comprehensive R Archive Network) by running the following in your console:¹

install.packages("package_name")

This downloads the package to your local machine (or the server of whatever remote machine you’re using). Thus, you only every need to do it once for each package²!

1. You never want to include this line of code in a Quarto document or an R Script

2. You’ll only need to re-install a package when you update R or if the package itself comes out with an updated version with new features you want to use.

Installing Packages

To use a package outside of Base R you need to do two things:

1. Download the package from CRAN (The Comprehensive R Archive Network) by running the following in your console¹:

install.packages("package_name")

This downloads the package to your local machine (or the server of whatever remote machine you’re using). Thus, you only every need to do it once for each package²!

2. Once a package is installed, you need to load it into the current session of R so you can use it. You’ll do this by putting the following in an R Script or embedded in a code chunk in a Quarto file:

library(package_name)

1. You never want to include this line of code in a Quarto document or an R Script

2. You’ll only need to re-install a package when you update R or if the package itself comes out with an updated version with new features you want to use.

gt Package

Let’s make a table that’s more polished than the code-y output R automatically gives us. To do this, we’ll want to install our first package called gt. In the console, run: install.packages("gt").

Different Syntax

Notice that unlike the library() command, the name of a package to be installed must be in quotes? This is because the name here is a search term (text, not an object!) while for library() it is an actual R object.

Making cleaner tables

• Code
• Table

library(gt) # loads gt, do once in your session
gt(as.data.frame.matrix(summary(swiss))) 

Nesting Functions

Note that we put the summary(swiss) function call inside the as.data.frame.matrix() call which all went into the gt() function. This is called nesting functions and is very common. I’ll introduce a method next week to avoid confusion from nesting too many functions inside each other.

What’s as.data.frame.matrix() Doing?

gt() takes as its first argument a data.frame-type object, while summary() produces a table-type object. Therefore, as.data.frame.matrix() was additionally needed to turn the table into a data.frame.

Fertility	Agriculture	Examination	Education	Catholic	Infant.Mortality
Min. :35.00	Min. : 1.20	Min. : 3.00	Min. : 1.00	Min. : 2.150	Min. :10.80
1st Qu.:64.70	1st Qu.:35.90	1st Qu.:12.00	1st Qu.: 6.00	1st Qu.: 5.195	1st Qu.:18.15
Median :70.40	Median :54.10	Median :16.00	Median : 8.00	Median : 15.140	Median :20.00
Mean :70.14	Mean :50.66	Mean :16.49	Mean :10.98	Mean : 41.144	Mean :19.94
3rd Qu.:78.45	3rd Qu.:67.65	3rd Qu.:22.00	3rd Qu.:12.00	3rd Qu.: 93.125	3rd Qu.:21.70
Max. :92.50	Max. :89.70	Max. :37.00	Max. :53.00	Max. :100.000	Max. :26.60

gt’s Version of head() and tail()

• head()
• gt_preview() alternative

head(swiss)

             Fertility Agriculture Examination Education Catholic
Courtelary        80.2        17.0          15        12     9.96
Delemont          83.1        45.1           6         9    84.84
Franches-Mnt      92.5        39.7           5         5    93.40
Moutier           85.8        36.5          12         7    33.77
Neuveville        76.9        43.5          17        15     5.16
Porrentruy        76.1        35.3           9         7    90.57
             Infant.Mortality
Courtelary               22.2
Delemont                 22.2
Franches-Mnt             20.2
Moutier                  20.3
Neuveville               20.6
Porrentruy               26.6

gt_preview(swiss, 
           top_n = 3, # default is 5
           bottom_n = 3) # default is 1 

	Fertility	Agriculture	Examination	Education	Catholic	Infant.Mortality
1	80.2	17.0	15	12	9.96	22.2
2	83.1	45.1	6	9	84.84	22.2
3	92.5	39.7	5	5	93.40	20.2
4..44
45	35.0	1.2	37	53	42.34	18.0
46	44.7	46.6	16	29	50.43	18.2
47	42.8	27.7	22	29	58.33	19.3

👋 Bye Bye as.data.frame.matrix()

We no longer need as.data.frame.matrix() since we’re no longer using summary(). Both head() and gt_preview() take a data.frame-type object as their first argument which is the same data type as swiss.

Homework