Lecture 3

R Fundamentals

Byeong-Hak Choe

bchoe@geneseo.edu

SUNY Geneseo

September 2, 2026

☁️ Posit Cloud & R Packages

☁️ Posit Cloud

Posit Cloud (formerly RStudio Cloud) is a web service that delivers a browser-based experience similar to RStudio, the standard IDE for the R language.
For our course, we use Posit Cloud for the R programming component.
- If you want to install R and RStudio on your laptop, you use my office hours.

🚀 Getting Started with Posit Cloud

Go to Posit Cloud and click Log In in the top-right corner.
Choose the Sign Up tab from the menu bar.
Create your account using one of the following:
- Google account, or
- Your geneseo.edu or personal email
After logging in, go to New Project → New RStudio Project.
Create a new R script:
- Click the + icon (top-left), or
- Click to File → New File → R script from the menu bar

📝 Posit Cloud Environment

Script Pane is where you write and save R commands in a script file.
- An R script is simply a plain text file containing R code.
- Posit Cloud (RStudio Cloud) automatically color-codes your code to make it easier to read.

Try typing a <- 1 in the Script Pane.
- With the cursor ( ┃ ) on the same line, run the line of code using:
  - Ctrl + Enter (Windows)
  - command + return (Mac)

⌨️ Posit Cloud Environment

Console Pane allows you to interact directly with the R interpreter and type commands where R will immediately execute them.

🧰 Posit Cloud Environment

Environment Pane shows everything you have created in R so far.
- For example, if you make a variable or a data frame, it will appear here.
- Think of it like a workspace where R keeps track of all the things you’re working on.

📈 Posit Cloud Environment

Plots Pane contains any graphics that you generate from your R code.

📦 R Packages

R packages are collections of ready-made tools for R.
- A package usually includes functions (pre-written commands), data sets, and sometimes extra code to make your work easier.
Many packages are already built into R, and thousands more can be installed from the internet (like downloading apps on your phone).
Why use packages?
- They save time—you don’t need to write everything from scratch.
- They give you access to powerful tools for data analysis, visualization, and more.
Examples:
- readr → for reading data files quickly
- dplyr → for cleaning and transforming data
- ggplot2 → for making beautiful graphs and charts

🌐 `tidyverse`

The tidyverse is a collection of R packages built for data analytics.
- They share a common design philosophy, grammar, and data structures.
Popular packages in the tidyverse include:
- readr → data reading
- dplyr → data transformation
- ggplot2 → data visualization

📦 Installing R Packages with `install.packages("packageName")`

install.packages("tidyverse")

Use the base R function install.packages("packageName") to install new packages.
Example: to install the tidyverse, type and run the command above in the R Console.

Note

If a pop-up appears: While running the installation code, R may ask whether to create a personal library. If you are unsure, choose No. You may not see this message at all; that is normal.

📂 Loading R Packages with `library(packageName)`

library(tidyverse)
mpg

After installation, use library(packageName) to load a package into your R session.
Example: running library(tidyverse) loads all the R packages in the tidyverse, including readr, dplyr, and ggplot2.
Once loaded, you can use their functions and datasets.
For instance, mpg is a built-in dataset from ggplot2, which is part of the tidyverse.

🔄 Workflow for R packages: Install → Load → Use

Install (once)

install.packages("tidyverse")

Load (At the top of every new R script, load the package:)

library(tidyverse)

Use (functions & datasets)

df <- read_csv("https://bcdanl.github.io/data/spotify_all.csv")

read_csv() function comes from the readr package, which is part of the tidyverse.

Note

🔑 Tip: In Posit Cloud, you need to install a package once per project.
After that, just load it whenever you start a new R script.

🗂️ Workflow: Naming and File Management

Always save your R script for each class session.
- Go to File → Save (or Save As…), or
- Click the 💾 save icon.
✅ Recommended file naming style (no spaces):
- Example: danl-101-2026-0902.R
Tips for naming files:
- ❌ Avoid spaces in file names.
- ✅ Use lowercase letters and hyphens (-) / underscores (_)

✍️ Workflow: Code and comment style

The two main principles for coding and managing data are:
- Make things easier for your future self.
- Don’t trust your future self.
The # mark is R’s comment character.
- In R scripts (*.R files), # indicates that the rest of the line is to be ignored.
- Write comments before the line that you want the comment to apply to.

⌨️ Workflow: Shortcuts in Posit Cloud

Windows
- Alt + - adds an assignment operator (<-)
- Ctrl + Enter runs a current line of code
- Ctrl + Shift + C makes a comment (#)
- Ctrl + Shift + R makes a section (# Section - - - -)

Mac
- option + - adds an assignment operator (<-)
- command + return runs a current line of code
- command + shift + C makes a comment (#)
- command + shift + R makes a section (# Section - - - -)

↩︎️ Workflow: Shortcuts in Posit Cloud

Ctrl (command for Mac Users) + Z undoes the previous action.
Ctrl (command for Mac Users) + Shift + Z redoes when undo is executed.

🔍 Workflow: Shortcuts in Posit Cloud

Ctrl (command for Mac Users) + F is useful when finding a phrase (and replace the phrase) in the RScript.

⚡ Workflow: Auto-completion

libr

Auto-completion of command is useful.
- Type libr in the RScript in RStudio and wait for a second.

🛑 Workflow: STOP icon

When the code is running, RStudio shows the STOP icon ( 🛑 ) at the top right corner in the Console Pane.
- Do not click it unless if you want to stop running the code.

⚙️ Posit Cloud Options Setting

This option menu is found by menus as follows:
- Tools \(>\) Global Options
Check the boxes as in the left.
Choose the option Never for Save workspace to .RData on exit:

💻 R Fundamentals: Values, Objects, and Data Types

🧱 Values have data types

A value is one piece of data, such as a number, text, or TRUE/FALSE.
A value’s data type tells R how it can work with that value.

7.5 numeric

"Hello!" character

TRUE logical

🏷️ A variable is a name for a value

a <- 7.5
a

a variable name

<- assigns

7.5 numeric value

a is an object name that refers to a value; in this course, we may also call it a variable.
<- assigns the value on the right to the name on the left.
In R, a value stored under a name is an object.

↔︎️ Keep the assignment arrow together

Assignment x <- 2

Store 2 under the name x.

Comparison x < -3

Ask whether x is less than negative 3.

A space between < and - separates them into two operators, changing the meaning.

Note

Keyboard shortcut for <-:
- Windows: Alt + -
- Mac: Option + -

⬅️ R calculates before it assigns

x <- 2
y <- x + 12
y                 # 14

1Find the value stored under x: 2

2Calculate the right side: 2 + 12 = 14

3Store 14 under the name y

Tip

Read x <- 2 as “assign 2 to x” or simply “x gets 2.”

🧩 Common R classes describe kinds of values

Class	Example	What it represents
logical	`TRUE`	A yes-or-no value
numeric	`7.5` or `2`	R’s usual number class
integer	`2L`	A whole number stored explicitly as an integer
character	`"Geneseo"`	Text inside quotation marks
factor	`factor("Green")`	A category with a defined set of levels

Note

Use class(object_name) to ask R how an object is classified.

🔤 Character values store text inside quotes

campus <- "Geneseo"
class(campus)       # "character"

In R, text is stored as character values.
Put text inside matching double quotes (" ") or single quotes (' ').
Without quotes, R treats Geneseo as an object name and looks for its value.

🔢 R usually stores numbers as numeric

class(2)       # "numeric"
class(8.8)     # "numeric"
class(2L)      # "integer"

numeric is R’s default number class—even when the value looks like a whole number, such as 2.
integer is a separate class for explicitly stored whole numbers.

Note

Integer shortcut: add L to a whole number; for example, 2L.

✅ Logical values answer yes-or-no questions

score <- 8.8

score == 8.8       # TRUE
score == 9.9       # FALSE
class(score == 8.8) # "logical"

Logical values are TRUE or FALSE—written in capital letters without quotes.
== compares two values for equality and returns a logical result.

Tip

<- stores a value; == compares two values.

➡️ A vector stores values of one type

scores <- c(82, 59, 76)
regions <- c("North", "South", "West")
mixed <- c("3", 4, 5)

length(scores)     # 3
class(mixed)       # "character"

c(...) combines values in order to create a vector.
The vectors have one common type.
- If types are mixed, R coerces them to a common type; in mixed, every value becomes text.

🗃️ A data frame combines equal-length vectors

vector

score · numeric

82 59 76

data frame

studentcharacter	scorenumeric	passedlogical
Ana	82	TRUE
Bo	59	FALSE
Chen	76	TRUE

Read across a row for one student; read down a column for one variable. Each column is a vector with one type.

🏷️ Factors store categories with defined levels

status <- factor(c("New", "Returning", "New"))
levels(status)     # "New" "Returning"
nlevels(status)    # 2

Values can repeat

NewReturningNew

Levels are distinct

NewReturning

A factor represents categorical data using a defined set of labels called levels.

🔄 Conversion changes how R treats a value

price_text <- "4.39"
price <- as.numeric(price_text)

class(price_text)  # "character"
class(price)       # "numeric"

"4.39"character

→

as.numeric()conversion

→

4.39numeric

Conversion is useful when imported data arrive in the wrong type.

Warning

Conversion can lose information: as.integer(4.39) returns 4. It truncates the decimal; it does not round.

⚠️ A `+` prompt means R is waiting for more input

> greeting <- "hello

A missing quote or parenthesis leaves the command unfinished, so the Console shows +.
Do not type the +: finish the command, or press Esc to cancel it.
Here, + is a continuation prompt—not the addition operator and not an error message.

⚙️ Functions turn inputs into outputs

c(60, 70, 80)input

→

mean()function

→

70output

A function is reusable code that performs a task.
Values supplied to a function are called arguments; the function returns an output.
We will use existing functions from base R and packages. We will not write our own functions yet.

🧩 A function call has a name and arguments

round(3.14159)               # 3
round(3.14159, digits = 2)   # 3.14

roundfunction name

3.14159argument: value to round

digits = 2named argument

A parameter is an input name in a function’s documentation; an argument is the value supplied in a call.
If an argument is omitted, a function may use its documented default value.

🔗 `package::name` identifies an object’s source

ggplot2::mpg

stringr::str_c("Data", "Analytics", sep = " ")
# "Data Analytics"

package::name accesses an exported object—such as a data set or function—without first running library(package).
It makes the source explicit and prevents ambiguity when packages use the same name.

Note

For a name conflict, write package1::FN() or package2::FN() to choose the function you intend.

➗ R follows familiar arithmetic rules

5 + 3        # 8
5 / 2        # 2.5
2^3          # 8

(3 + 4)^2    # 49
3 + 4^2      # 19

+, -, *, and / perform familiar arithmetic; ^ means “raise to a power.”
R follows the standard order of operations: parentheses, exponents, multiplication/division, then addition/subtraction.

Tip

Parentheses make your intended order clear to both R and your reader.

🧮 Built-in functions handle common calculations

Function call	Question answered	Result
`abs(-3)`	How far is \(-3\) from zero?	`3`
`sqrt(16)`	What number squared equals 16?	`4`
`round(3.14159, 2)`	What is this value to two decimal places?	`3.14`
`log(exp(3))`	What exponent of \(e\) produces \(e^3\)?	`3`

log(x) is the natural logarithm, \(\ln(x)\); exp(x) calculates \(e^x\).

⚡ R can calculate across an entire vector at once

prices <- c(10, 12, 9)

prices * 1.05
# 10.50  12.60  9.45

prices >= 10
# TRUE  TRUE  FALSE

Many R operators and functions accept a whole vector and return one result for each element.
One expression performs the same calculation or comparison across every value.

Note

For now, combine vectors of the same length when calculating element by element.

🧪 Apply the R fundamentals

Classwork 2: R Fundamentals →

Create a vector Inspect its class Use functions on it

Lecture 3

☁️ Posit Cloud & R Packages

☁️ Posit Cloud

🚀 Getting Started with Posit Cloud

📝 Posit Cloud Environment

⌨️ Posit Cloud Environment

🧰 Posit Cloud Environment

📈 Posit Cloud Environment

📦 R Packages

🌐 tidyverse

📦 Installing R Packages with install.packages("packageName")

📂 Loading R Packages with library(packageName)

🔄 Workflow for R packages: Install → Load → Use

🗂️ Workflow: Naming and File Management

✍️ Workflow: Code and comment style

⌨️ Workflow: Shortcuts in Posit Cloud

↩︎️ Workflow: Shortcuts in Posit Cloud

🔍 Workflow: Shortcuts in Posit Cloud

⚡ Workflow: Auto-completion

🛑 Workflow: STOP icon

⚙️ Posit Cloud Options Setting

💻 R Fundamentals: Values, Objects, and Data Types

🧱 Values have data types

🏷️ A variable is a name for a value

↔︎️ Keep the assignment arrow together

⬅️ R calculates before it assigns

🧩 Common R classes describe kinds of values

🔤 Character values store text inside quotes

🔢 R usually stores numbers as numeric

✅ Logical values answer yes-or-no questions

➡️ A vector stores values of one type

🗃️ A data frame combines equal-length vectors

🏷️ Factors store categories with defined levels

🔄 Conversion changes how R treats a value

⚠️ A + prompt means R is waiting for more input

⚙️ Functions turn inputs into outputs

🧩 A function call has a name and arguments

🔗 package::name identifies an object’s source

➗ R follows familiar arithmetic rules

🧮 Built-in functions handle common calculations

⚡ R can calculate across an entire vector at once

🧪 Apply the R fundamentals

🌐 `tidyverse`

📦 Installing R Packages with `install.packages("packageName")`

📂 Loading R Packages with `library(packageName)`

⚠️ A `+` prompt means R is waiting for more input

🔗 `package::name` identifies an object’s source