R Programming Language

r fundamentals, packages, programming

2026-01-27

Welcome to R

R Programming Language

• Built to efficiently store and manipulate data
• Open-source, free, and flexible alternative to other statistical analysis software
• Large, active community of users and contributors
• Packages shared and stored on the open index CRAN (Comprehensive R Archive Network)

R is a programming language (“environment”) built to efficiently store and manipulate data, particularly matrices and arrays, based on another language called S - Widely used among statisticians, researchers, and data miners for developing statistical software and data analysis (vs general programming languages like python) Open-source, free, and flexible alternative to other statistical analysis software like Stata and SPSS Has a large and active community contributing packages that facilitate a wide variety of analysis types, graphical options, and more. Many contributors store their packages on CRAN (Comprehensive R Archive Network)

R: OOP

• R is an object-oriented programming language (OOP)
  • Objects: contain a value and have an identifier
  • Classes: templates for objects and their contained data type
  • Structures: classes containing multiple objects
• R manipulates data (objects)
  • Environments: collections of manipulable objects
  • Functions: procedures for creating or changing code
  • Arguments: inputs to functions that specify procedure

• OBJECTS contain a value and have a name/identifier
• CLASSES are templates for objects and the “type” of data contained within
• STRUCTURES are classes for objects that comprise multiple objects
• ENVIRONMENTS are collections of objects that can be manipulated
• FUNCTIONS are procedures for creating or changing data
• ARGUMENTS are passed to functions to specify the procedure in context

NOTE: you might hear objects and classes referred to by other terms. this is because all OOP langs have slightly different differentiation (if they have them at all!) between things like classes and “structures” for example. for our purposes the precise language isn’t critical, just the concepts. but it’s worth keeping in mind if you’re going to go deeper into heavy programming with R or other OOPs like python

R Scripts

• R scripts include:
  • Series of functions (code) to carry out in order
  • Plain text comments (non-code) that are ignored by R
• R code is written in:
  • R script files (.R)
  • “Code chunks” within other file types (e.g., .Rmd & .qmd notebooks)
• Sourcing scripts allows code in one file to “talk to” code in other files

R scripts: Dictate a series of functions (code) to carry out in order Include plain text comments (non-code) that are ignored by R R code is usually written in R script files with the .R extension R code can also be included in “code chunks” within other file types (e.g., .Rmd & .qmd notebooks) Some functions allow R scripts to “talk to” R code in other files So an R project typically has multiple .R files in addition to the .Rmd

.R scripts are just that: scripts. .Rmd files are “R Notebooks”, combining .R : magical files that process many kinds of code and non-code to produce a coherent, human-friendly presentation of data.

R Syntax

Comments

• Comments are plain text notes within code that are ignored by R
• Created using the # symbol
  • Everything that follows on that line will be ignored
• Used to:
  • Explain code functionality
  • Provide context or instructions
  • Temporarily disable code during testing/debugging

# This comment spans multiple lines. The `#` symbol must be used at the start 
# of each line. Alternatively, start the comment mid-line like below:

x <- 42  # The rest of this line is comment
# y <- x + 10  Treat this line as comment, not code

More on comments in the introduction and best practices lecture (Week 1).

Variables

• Variables store data values with unique identifiers
• Created using the assignment operator <-
  • Example: class_name <- "D2M-R"
• Variable names:
  • Are case-sensitive
    • e.g., Class_Name, class_name, and CLASS_NAME are different variables
  • MUST start with a letter or dot, not a number
    • e.g., student1, .temp_value are valid; 1student, _value are not
  • MUST contain (only) letters, numbers, dots, or underscores
    • e.g., total.score, data_frame_1 are valid; total-score, data frame are not
  • SHOULD be descriptive, meaningful, and pronounceable
    • e.g., student_age, is_enrolled, total_score
  • SHOULD follow consistent naming conventions
    • e.g., camelCase, snake_case, or dot.notation
    • Use a style guide like the Tidyverse style guide

Variables are assigned values with an assignment operator. The standard assignment operator in R is <-, where the variable name is on the left and the value to be assigned is on the right. Alternatively you can use = for assignment in the same way, but <- is preferred for variable assignment. = is used for assigning values to function arguments. You can also use -> to assign values in reverse, but this is very rarely used.

Main Data Types

Data you can work with in R takes one of 6¹ forms, most commonly:

Data type	Description	Example
Numeric	Decimal numbers, including whole numbers	3.14, 42.0, -1.5
Integer	Whole numbers (exclusively), represented with an L suffix	42L, -1L, 1000L
Logical	Boolean values, either TRUE or FALSE	TRUE, FALSE or T, F
Character	Text strings, enclosed in quotes	"hello", '123', "R is great!"

The main data types you’ll encounter in R are Numeric, Integer, Logical, and Character. Numeric data type represents decimal numbers, including whole numbers (e.g., 3.14, 42.0, -1.5) Integer data type represents only whole numbers, indicated by an L suffix (e.g., 42L, -1L, 1000L) Logical data type represents boolean values, either TRUE or FALSE (e.g., TRUE, FALSE) Character data type represents text strings, enclosed in quotes (e.g., “hello”, ‘123’, “R is great!”)

The other 2 are Complex numbers (e.g., 1 + 2i) and Raw vectors (used to store raw bytes of data, e.g., images or binary files). You’re won’t run into these in this class.

1. Also complex numbers and raw vectors, rarely used in practice.

Data Types(+)

There are also a few “honorary” data types:

Data type	Description	Example
Factor	Leveled categorical data, stored as integers with labels	factor(c("low", "medium", "high"))
Date	Dates, stored as a special class of object	as.Date("2025-01-31")
POSIXct	Date-time objects, which include both date and time	as.POSIXct("1776-07-04 12:01:59")
empty	Not a data type, but the absence of data	NA

Factors are used to represent categorical data with a fixed number of levels. They are stored as integers with associated labels (e.g., factor(c(“low”, “medium”, “high”))) Dates are stored as a special class of object in R, allowing for date-specific operations (e.g., as.Date(“2025-01-31”)) POSIXct represents date-time objects, which include both date and time information (e.g., as.POSIXct(“1776-07-04 12:01:59”)) NA represents missing or undefined data in R. It is not a data type itself, but rather the absence of data. There are other ways to represent missing data (e.g., NULL, NaN), but NA is the most common. - NA is not the same as the numeric/integer 0 or the empty string "". Those are both values!!

Data Structures

R organizes data into structures to for manipulation and analysis. The main data structures are:

Data structure	Description	Example
Scalar	Single data value of any data type	42, "hello", TRUE
Vector	One-dimensional array of elements of the same data type	c(1, 2, 3, 4, 5)
List	Ordered collection of elements that can be of different data types	list(name = "Alice", age = 30, scores = c(90, 85, 88))
Matrix	Two-dimensional array of elements of the same data type	matrix(1:6, nrow = 2, ncol = 3)
Data Frame	Two-dimensional, tabular data structure with columns of potentially different data types	data.frame(name = c("Alice", "Bob"), age = c(30, 25))
Tibbles	Alternative data frames with enhanced features	tibble::tibble(name = c("Alice", "Bob"), age = c(30, 25))

Data Structures: Data Frames and Tibbles

• Data frames are 2-dimensional, tabular data structures
  • dfs are the heart <3 of R
  • Special type of list
    • Each vector in the list is a column
    • All of equal length (e.g. same number of rows)
    • Can be different data types across vectors
    • Must be the same data type within a vector
• Tibbles are alternatives to data frames
  • Special type of data frame
  • Used in the tidyverse ecosystem
  • Have some enhanced features (e.g., better printing, stricter subsetting rules)
  • Functionally equivalent to data frames (for our purposes)

Data frames are the heart of R. They are lists of vectors, where each vector is a column in the data frame. All vectors must be of equal length (i.e., same number of rows), but they can be different data types across vectors (e.g., one column can be numeric, another character, etc.). Within a single vector (column), all elements must be of the same data type.

Tibbles are the tidyverse’s alternative to data frames. Tibbles are also data frames. It’s just another class applied to the same object. There are several important technical advantages to tibbles, but for our applied purposes all that you need to know is that tidyverse functions generally expect tibbles as inputs and return tibbles as outputs. So when working in the tidyverse, you’ll mostly be using tibbles. Conceptually, they’re going to be interchangeable with data frames.

Operators

Operators are symbols that perform operations on variables and values. The main types of operators in R are:

Operator type	Description	Example
Arithmetic	Perform mathematical calculations	+, -, *, /, ^
Relational	Compare values and return logical results	==, !=, <, >, <=, >=
Logical	Combine or negate logical values	&, |, !
Assignment	Assign values to variables	<-, =, ->

Relational aka comparison

ASSIGNMENT: By convention, use the left arrow to assign values to variables. Use the equals sign to assign values to function arguments. Don’t use the right arrow.

Operators: Arithmetic

Arithmetic do math.

• Input: numeric data
• Output: numeric data

Operator	Description	Example	Output
+	Addition	3 + 5	8
-	Subtraction	10 - 4
*	Multiplication	6 * 7	42
/	Division	20 / 4	5
^	Exponentiation (power)	2 ^ 3	8
%%	Modulo (remainder of division)	10 %% 3	1

Operators: Relational

Relational or comparison operators compare objects and return a logical value (TRUE or FALSE). They are a special kind of logical operator.

• Input: numeric, character, or logical data
• Output: logical data

Operator	Description	Example	Output
==	Equal to	5 == 5	TRUE
!=	Not equal to (“≠”)	5 != 3	TRUE
>	Greater than	7 > 4	TRUE
<	Less than	3 < 8	TRUE
>=	Greater than or equal to	6 >= 6	TRUE
<=	Less than or equal to	2 <= 5	TRUE

Warning: '==' != '='

The == operator checks for equality, while = is an (argument) assignment operator.

Operators: Logical

Logical operators combine and modify boolean values (TRUE or FALSE).

• Input: logical data
• Output: logical data

Operator	Description	Example	Output
&	“and”: both sides of the operators evaluate to TRUE	TRUE & FALSE	FALSE
|	“or”: at least one side of the operator evaluates to TRUE	TRUE | FALSE	TRUE
!	“not”: the opposite of something’s logical evaluation	!TRUE	FALSE

It helps to think of the ! as the word “not”: != is “not equal to”. !TRUE is FALSE. is.na(x) means “is x missing?” (true or false), and !is.na(x) means “is x not missing?” (true or false, the opposite of is.na(x)).

The & and | operators come in two flavors: single or (& and |) and double (&& and ||). Dig into this difference if you want or need to, but for most purposes, you can just use the single operators (& and |).

Operators: Other

Operator	Description	Example	Output
<-	Variable assignment operator	x <- 5	Assigns the value 5 to the variable x
=	Argument assignment operator	round(3.14159, digits = 2)	Assigns the value 2 to the digits argument of the round() function
:	Create a sequence of integers	1:5	c(1, 2, 3, 4, 5)
[ ]	Subset elements of a vector, list, or data frame by position or name	list(first = 1, second = 2)[2]	> $second > [1] 2
[[ ]]	Extract a single element from a list by position or name	list(first = 1, second = 2)[[2]]	2
$	Extract a single element from a list or data frame by name	list(first = 1, second = 2)$second	2
|> or %>%	Pipe operator to pass the output of one function as the input to another	data |> filter(condition)	Passes data as the first argument to filter()

The assignment operators <- and = can both be used to assign values to variables, but <- is preferred for variable assignment. = is primarily used for assigning values to function arguments or within functions.

Functions

• Basic syntax: function_name(argument1, argument2, ...)
  • Example: paste("Hello", class_name)
• Functions: pre-defined procedures that perform specific tasks
  • Take 0 or more arguments as inputs
  • Return 1 output
• Arguments: inputs to functions that specify procedure
  • Can be required or optional
  • Assigned with = operator or by position following default order
    • round(3.14159) is the same as round(x = 3.14159)
    • round(2, 3.14159) is not the same as round(digits = 2, x = 3.14159).
• View all possible arguments in a function’s documentation with ?functionname or ??functionname.

Functions are pre-defined procedures that perform specific tasks. They take arguments as inputs and return outputs. Basic syntax for calling a function is the function name followed by parentheses, containing required and optional arguments: function_name(argument1, argument2, …)

Function Definition

All functions have:

1. Name
2. Zero or more arguments (inputs)
3. Procedure body (code)
4. Return value (output)

# See the elements of a function by running the name of 
# the function without parentheses in the console

example.function

<srcref: file "" chars 2:21 to 9:1>

Defining Functions

Use the function() function to define your own functions:

function_name <- function(arguments) {
    # Informative comments
    the code for your function
    return(value)
}

helloworld <- function(name, punctuation = "!") {
    # Prints greeting with name and punctuation
    greeting <- paste0("Hello ", name, punctuation)
    return(greeting)
}

# Specify both arguments in order 
# No argument names
helloworld("D2M-R", "!?")

[1] "Hello D2M-R!?"

# Specify arguments out of order 
# Use argument names
helloworld(
    punctuation = "...", 
    name = "class"
)

[1] "Hello class..."

# Specify required argument only
# Use default for optional
helloworld("everyone")

[1] "Hello everyone!"

Notes on Function Definition: Names, Arguments

Names

• Descriptive, pronounceable, consistently styled
• Unique within your R environment

Arguments

• Typically limited to specific data types (e.g., numeric, character)
  • Data constraints can be explicitly checked with code or implicitly based on function body requirements
• Can have default values assigned, making them optional
• Specify arguments without naming them by following documented order: function_name(value1, value2)
• Specify arguments out of order by naming them: function_name(arg2 = value2, arg1 = value1)

Notes on Function Definition: Body, Return

Procedure Body

• Can include multiple lines of code and comments
• Scoped environment: variables created inside are not accessible outside

Return Value

• If specified with return(), that value is printed to console
  • return(arg1 + arg2) returns sum of arg1 and arg2
• If no return() is used, the last evaluated expression is returned by default
  • arg1 + arg2 returns sum of arg1 and arg2
• Functions have to evaluate without object assignment to return a value to the console
  • result <- arg1 + arg2 does not return anything

Reference: Useful Base R Functions (1)

Function	Description	Example	Output
c()	Combine values into a vector	c(1, 2, 3)	c(1, 2, 3)
paste()	Concatenate strings together	paste("Hello", "world!")
data.frame()	Create a data frame from vectors	data.frame(x = 1:3, y = c("a", "b", "c"))	A data frame with 3 rows and 2 columns named x and y
class()	Check the data type of an object	class(3.14)	"numeric"
str()	Display the structure of an object	str(mtcars)	A summary of the mtcars data frame
length()	Get the length of a vector	length(c(1, 2, 3, 4, 5))	5
head()	View the first few rows of a data frame or vector	head(mtcars)	The first 6 (default) rows of the mtcars data frame
summary()	Get a summary of a data frame or vector	summary(mtcars)	Summary statistics for each column in the mtcars data frame

Reference: Useful Base R Functions (2)

number_list <- c(11, 37, 42, 101, 202, 1000, 2025, -3)

Function	Description	Example	Output
round()	Round a numeric value to a specified number of decimal places	round(67.1988, 2)	67.2
sum()	Calculate the sum of a numeric vector	sum(number_list)	3415
min()	Find the minimum value in a numeric vector	min(number_list)	-3
max()	Find the maximum value in a numeric vector	max(number_list)	2025

Reference: Useful Base R Functions (3)

number_list <- c(11, 37, 42, 101, 202, 1000, 2025, -3)

Function	Description	Example	Output
mean()	Calculate the mean of a numeric vector	mean(number_list)	426.875
median()	Calculate the median of a numeric vector	median(number_list)	71.5
sd()	Calculate the standard deviation of a numeric vector	sd(number_list)	726.7456693
cor()	Calculate the correlation between two numeric vectors	cor(number_list[1:4], number_list[5:8])	-0.2855236

Packages

Consider two very useful functions:

# function add.two accepts numeric value `x` 
# and returns numeric x+2

add.two <- function(x) {
    return(x + 2)
    }

# function add.two2 accepts any scalar value `x`, 
# coerces to x to a string, then prints the string 
# followed by the string " two"

add.two2 <- function(x) {
    return(paste(as.character(x), "two"))
    }

Someone out there really needs to add two to things in multiple ways. Tragically, base R just doesn’t have the essential tools needed for all the two-adding tasks. This two-adding user would benefit from accessing both these functions together for:

• Reusability: use in multiple projects without redefining
• Organization: group related functions together
• Sharing: bundle and share functions with others with similar needs
• Documentation: add explanation for consistent use and development

Packages are collections of many user-created functions, usually designed to work toward a specific use-case or goal.

Why combine these two functions into a package? Consider the user. Whoever is doing this is a person who needs to add two a lot and in multiple ways. If we assume they have good reason to do so, then packaging these functions together provides several benefits:

• Reusability: The user can easily reuse these functions across multiple projects without having to rewrite them each time.
• Organization: Grouping related functions together in a package helps keep code organized and easier to maintain.
• Sharing: The user can share the package with others who may also need to add two in their work.
• Documentation: Packages often include documentation that explains how to use the functions, making it easier for others (and the user themselves) to understand and utilize the functions effectively.

Packages

• Packages or libraries are collections of functions
  • Centered on a specific use case, goal, or domain
  • User-developed and shared
• You need to know:
  • Why packages are useful
  • How to find them
  • How to access and understand documentation
  • How to install and load them
  • The basics of common problem points

run_chatter_pipeline <- function(
  tbl, tbltype, target.ptcp, addressee.tags, cliptier, nearonly,
  lxonly = default.lxonly,
  allowed.gap = default.max.gap, allowed.overlap = default.max.overlap,
  min.utt.dur = default.min.utt.dur, interactants = default.interactants,
  mode = default.mode, output = default.output, n.runs = default.n.runs) {
  # step 1. read in the file
  spchtbl <- read_spchtbl(filepath = tbl, tbltype = tbltype,
                          cliptier = cliptier,
                          lxonly = lxonly, nearonly = nearonly)
  # step 2. run the speech annotations through the tt behavior detection pipeline
  ttinfotbls <- fetch_chattr_tttbl(
    spchtbl = spchtbl, target.ptcp = target.ptcp,
    cliptier = cliptier, lxonly = lxonly,
    allowed.gap = allowed.gap, allowed.overlap = allowed.overlap,
    min.utt.dur = min.utt.dur, interactants = interactants,
    addressee.tags = addressee.tags,
    mode = mode, output = output, n.runs = n.runs)
  # step 3. create a summary of the tt behavior by clip and overall, 
  # incl. the random baseline
  ttinfotbls$tt.summary <- summarize_chattr(ttinfotbls)
  return(ttinfotbls)
}

View the chattr package on GitHub.

This is the code for 1 function in the chattr package created by Marisa Casillas. Take a look at the github repo’s documentation and files for a great example of how the need for a package arises & what it looks like to create that package and meet that need.

https://github.com/marisacasillas/chattr-basic

Packages: Install & Load

To use functions from a package, you must first:

1. Install the package on your local machine
   • install.packages("packagename")
   • Install a package once (on each machine)
2. Load (aka attach) it into your R session
   • library(packagename) or require(packagename)
   • Load a package each time you start a new R session

Loading vs attaching

Calling library() on a package makes its functions available for use as though they were built into R itself. We usually call this loading the package, but technically it’s attaching it.

Once a package is installed on your machine, you can load functions directly without attaching the whole package with library(). Do so by prefixing the function with the name of the package and two colors: packagename::functionname().

Also: 1. use library() in scripts; require() is more for using in functions 2. in addition to loading packages with each new session, good practice to also load packages at the start of each script that uses them 3. package loading order matters! loading a package with a function that has the same name as a function in an already loaded package will override the definition of that function (but you can still call the old one with packagename::functionname())

install.package("tidyverse") – install a package on your local machine library(tidyverse) – load an existing package require(tidyverse) and Require("tidyverse") - require a package Try those requires out and compare… Not working? try typing ??Require into the console (note the 2 question marks) and see if you can figure out what’s going on and how to fix it. Installing/loading some packages will also install/load dependencies e.g., library(tidyverse) will load the included readr package readr will also load dependencies DBI, haven, readxl, and others

Having trouble? Review the troubleshooting strategies from first week! Most packages have full documentation on CRAN Alternatively on the package’s GitHub repo or developer site See documentation for functions within RStudio by typing ?thefunctioname into the console

Packages: Dependencies

Functions in packages are often defined using functions from other packages. The former package depends on the latter, its dependency.

A package’s CRAN documentation will list its dependencies in three categories:

1. Depends: Packages that must be installed and attached for the package to work
2. Imports: Packages that must be installed (but not attached) for the package to work
3. Suggests: Packages that are not required for the package to work, but are recommended for full functionality

In practice…

When you install or load a package, R will automatically install or load its Depends and Imports dependencies for you. Usually this will just happen without you needing to even notice, but occasionally you may be prompted by the console to approve the installation/loading of dependencies. Rarely, you may need to manually install or load a dependency.

The same way that package functions are built using base R functions, packages also frequently (usually) build on functions from other packages. When a package uses functions from another package, that other package is its dependency.

There are two big categories of dependencies, hard and soft dependencies. Hard dependencies are required. Functions in the package simply won’t work without them. Soft dependencies are recommended to get the most out of the package, but not strictly necessary. We’ve got three flavors of dependencies in R:

1. Depends: Packages that must be installed and attached for the package to work
2. Imports: Packages that must be installed (but not attached) for the package to work
3. Suggests: Packages that are not required for the package to work, but are recommended for full functionality

Reference: D2M-R Required Packages

Package Name	Description
tidyverse	Ecosystem of packages for data manipulation, visualization, and analysis; includes core tidyverse packages1
bibtex	BibTeX tools for R (bibliography management)
citr	RStudio add-in to insert citations
DescTools	Tools for descriptive statistics
gt	Easily create presentation-ready tables
knitr	Dynamic report generation in R
lme4	Linear and generalized linear mixed-effects models
psych	Procedures for psychological, psychometric, and personality research
quarto	Tools for working with the Quarto markdown publishing system
rmarkdown	Authoring dynamic documents with R Markdown
usethis	Automate package and project setup tasks

1. Core tidyverse packages include: ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, lubridate, and forcats.

Reference: D2M-R Suggested Packages

Package Name	Description
broom	Convert statistical analysis objects into tidy tibbles
data.table	Fast data manipulation and aggregation
flextable	Functions for reporting tabular results in R Markdown and Word
haven	Import and export of SPSS, Stata, and SAS files
janitor	Simple tools for examining and cleaning dirty data
kableExtra	Construct complex tables in R Markdown
papaja	APA style manuscript preparation with R Markdown
pwr	Power analysis for general linear models
RColorBrewer	Color palettes for maps and figures
patchwork	Combine separate ggplot2 plots into the same graphic
vcd	Visualizing categorical data
ggsci	Scientific journal and sci-fi movie color palettes for ggplot2

R Programming

Indexing & Subsetting

Indexing and subsetting are ways to select specific elements from data structures like vectors, lists, data frames, and matrices.

Indexing: Identifying the position of an element within a data structure (or positions within sub-structures) using numeric position or name.

Subsetting: Extracting a portion of a data structure based on specific criteria (e.g., selecting certain rows or columns from a data frame), including indexing.

Reference: Indexing & Subsetting

Create a vector of integers beginning with one number and ending with another using ::

3:7

[1] 3 4 5 6 7

1:5 == c(1, 2, 3, 4, 5)

[1] TRUE TRUE TRUE TRUE TRUE

Reference: Indexing & Subsetting

Select elements of a vector by position or name with [ ]:

my_vector <- c(1, 10, 3, fourth = 1000, fifth = 2)
my_vector[3] # select 4th element of a vector, return the named or unnamed element

  
3 

my_vector["fourth"] # select element named 'fourth' from a vector, return the named element

fourth 
  1000 

Reference: Indexing & Subsetting

Select elements of a list by position or name with [ ] and [[ ]]:

my_list <- list(first = 1, second = 2)
my_list[2] # select 2nd element of a list, returned as a list

$second
[1] 2

my_list[[2]] # select 2nd element of a list, returned as the element itself

[1] 2

my_list[["second"]] # select element named 'second' from a list, returned as the element itself

[1] 2

Reference: Indexing & Subsetting

Select elements of a data frame or matrix by position or name with [ ] and [[ ]], using , to separate row and column indices:

mtcars[1, ]  # first row

          mpg cyl disp  hp drat   wt  qsec vs am gear carb
Mazda RX4  21   6  160 110  3.9 2.62 16.46  0  1    4    4

mtcars[, 1]  # first column

 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

mtcars[1, "mpg"] # value in the first row and first column (named "mpg")

[1] 21

Reference: Indexing & Subsetting

Select a list or data frame element by name with $:

my_list <- list(first = 1, second = 2)
my_list$second # returns `2`

[1] 2

mtcars$mpg # 'mpg' column of mtcars 

 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

Environments

Environments: data structures that store variable bindings (associations between variable names and their values) in R.

• Global environment: the main workspace where user-defined variables and functions are stored during an R session. Default view in the Environment pane.
• Local environments: created when functions are called; variables defined within a function are stored in its local environment and are not accessible outside the function unless explicitly returned.

Scope

Environments are scoped, meaning that variables defined within an environment are only accessible within that environment and its child environments.

Global environment variables are accessible from its local environment children, but local environment variables are not accessible from the parent global environment.

Control Flow

Control flow: the order in which individual statements, instructions, or function calls are executed or evaluated within a program.

In R, control flow is managed using conditional statements and loops.

Conditional Statements

Conditional statements allow you to execute different blocks of code based on whether certain conditions are met.

Statement	Description
if	Execute a block of code if a specified condition is TRUE
if...else	Execute one block of code if a condition is TRUE,
if...else if...else	Execute different blocks of code based on multiple conditions

# if statement

x <- 3

if (x > 0) {
    print("x is positive")
}

[1] "x is positive"

# if...else statement
y <- 10

if (y > 5) {
    print("y is greater than 5")
} else {
    print("y is not greater than 5")
}

[1] "y is greater than 5"

Conditional Statements: Example

# Example of if...else if...else statement

z <- 0 # change this value to test different outcomes

# Check if z is a number
# if it is, do the checks inside the {}
# if it isn't, go to the else statement
if (is.numeric(z)) {
    if (z > 0) {      # is it positive?
        print("z is positive")  # if it is, say so and then STOP
    # if not, is it negative?
    } else if (z < 0) {     # if not, is it negative?
        # if it is, say so and then STOP
        print("z is negative")  # if it is, say so and then STOP
    # if not positive and not negative, it must be zero
    } else {
        print("z is zero") # say so and then STOP
    }
# if it isn't a number, say so and then STOP
} else {
    print("z is not a number")
}

[1] "z is zero"

Conditional Functions

R’s conditional functions provide a way to perform conditional operations in a more functional, condensed style.

Function	Package
ifelse()	base
if_else()	dplyr
case_when()	dplyr

The examples from the previous slide can be written more concisely:

y <- 10

ifelse(y > 5, "y is greater than 5", "y is not greater than 5")

[1] "y is greater than 5"

z <- 0 # change this value to test different outcomes

print(case_when(
    !is.numeric(z) ~ "z is not a number",
    z > 0 ~ "z is positive",
    z < 0 ~ "z is negative",
    TRUE ~ "z is zero"
))

[1] "z is zero"

What’s the difference between ifelse() and if_else()?

• The ifelse() function is a base R function that is vectorized, meaning it can handle vectors of conditions and return corresponding values for each element. It is generally faster for large datasets but can be less strict about data types.
• The if_else() function is part of the dplyr package in the tidyverse. It is also vectorized but is stricter about data types, ensuring that the output type matches the input type. This can help prevent unexpected results when working with different data types.

Iteration

Iteration, or looping, allows you to repeat a block of code multiple times, either a fixed number of times or while a certain condition is met.

Loop Type	Description
for	Repeats a block of code for each item in a sequence or vector
while	Repeats a block of code as long as a specified condition is TRUE

Loops Conceptually

When I say go

Do this thing

And keep doing it

Until I say stop

for Loops

When I say go

i = my_list[1]

Do this thing

print(i)

And keep doing it

i = my_list[2]
i = my_list[3]...

Until I say stop

my_list[6] > length(my_list)

# Create a finite sequence of numbers
my_list <- c(1, 3, 5, 7, 9)

# for each value i in the sequence num_list
for (i in my_list) {
    # print the value of i to the console
    print(i)
# iterate to the next value in the sequence 
    # until there are no values left
}

[1] 1
[1] 3
[1] 5
[1] 7
[1] 9

while Loops

When I say go

i = 1

Do this thing

print(i)
i <- i + 1

And keep doing it

print(i + 1)
print(i + 2)...

Until I say stop

i >= 6

# Initialize counter variable i to 1
i <- 1 

# while (if) i=1, then i=2...til i=6
while (i < 6) {
    # print the value of i to the console
    print(i)
    i <- i + 1 # Increment i by 1
# check whether "while" condition still holds
    # repeat if TRUE
    # stop if FALSE
}

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5

for vs while

for

• Known, finite sequence
• Repeats for each item in sequence
• Does not require an explicit counter variable
• Iteration happens automatically

while

• Unknown, potentially infinite sequence
• Repeats while condition is TRUE
• Requires an explicit counter variable
• Iteration controlled by defined condition

Warning: Infinite loops

Use while for potentially but not actually infinite sequences. Your code must eventually lead to a FALSE evaluation:

1. Define the iteration mechanism:
   • i <- i+1
2. Ensure the while condition will eventually be FALSE when iterated using that mechanism
   • i < 6

• Use a for loop when you know in advance how many times you want to iterate (i.e., finite sequence).
• Use a while loop when you want to continue iterating until a certain condition is met, and you may not know in advance how many iterations that will take (i.e., potentially infinite sequence).
  • But not actually infinite! Always ensure that the condition will eventually become FALSE to avoid infinite loops.

Regular Expressions

What are Regular Expressions?

Regular expressions (regex or regexp): sequences of characters that form a search pattern, primarily used for string matching and manipulation.

Examples of regex syntax:

• ^Hello: matches any string that starts with “Hello”
• [aeiou]: matches any single vowel character (a, e, i, o, u)
• \d{3}-\d{2}-\d{4}: matches a pattern like a US Social Security number (e.g., “123-45-6789”)
  • or with R’s escaping: \\d{3}-\\d{2}-\\d{4}

Tip

Use tools like regex101 or RegExr to test and visualize regex patterns interactively. Just be careful about escaping rules when using R! (More info below.)

Regular expressions (regex or regexp) are sequences of characters that form a search pattern. They are primarily used for string matching and manipulation, allowing you to search, match, and manipulate text based on specific patterns.

Basic Regex Syntax: Essentials

Symbol	Description	Example
.	Matches any single character except newline	a.b matches “acb”, “a1b”, “a b”
^	Matches the start of a string	^Hello matches “Hello world”
$	Matches the end of a string	world$ matches “Hello world”
*	Matches 0 or more occurrences of the preceding element	ab* matches “a”, “ab”, “abb”, “abbb”
+	Matches 1 or more occurrences of the preceding element	ab+ matches “ab”, “abb”, “abbb” but not “a”
?	Matches 0 or 1 occurrence of the preceding element	ab? matches “a” and “ab”
[]	Matches any one character within the brackets	[aeiou] matches “a”, “e”, “i”, “o”, or “u”
|	Logical OR operator	cat|dog matches “cat” or “dog”
()	Groups expressions	(ab)+ matches “ab”, “abab”, “ababab”

What about a logical AND operator? There isn’t one! The whole regex pattern is treated as a sequence of elements that must all be matched in order. “and” is built-in.

Basic Regex Syntax: Other Useful Matches

Symbol	Description	Example
\d	Matches any digit (0-9)	\d matches “0”, “1”, …, “9”
\w	Matches any word character (alphanumeric + underscore)	\w matches “a”, “b”, …, “z”, “A”, “B”, …, “Z”, “0”, “1”, …, “9”, “_”
\s	Matches any whitespace character (space, tab, newline)	\s matches ” “,”, “”
{n}	Matches exactly n occurrences of the preceding element	a{3} matches “aaa”
{n,}	Matches n or more occurrences of the preceding element	a{2,} matches “aa”, “aaa”, “aaaa”, …
{n,m}	Matches between n and m occurrences of the preceding element	a{2,4} matches “aa”, “aaa”, or “aaaa”

Escaping

Characters that have assigned functions will perform that function unless they are escaped.

Escape a special character to refer to its literal meaning.

• R uses a backslash (\) as the escape character.
• Regex also uses a backslash (\) as the escape character.
• When using regex in R, you need to use double backslashes (\\) to escape special characters because the backslash itself is an escape character in R strings.

TELL ME WHY.

Why? Imagine R is humming along, reading your string. It sees a backslash. Aha! A special character is coming up! So R uses the backslash to escape the special character, and the special character is interpreted literally. But wait! The special character is supposed to be part of a regex pattern, and regex also uses backslashes to escape special characters. Whatever follows the backslash is going to be interpreted literally. When the string is passed to the regex engine, it sees a backslash followed by a special character. It does not see the backslash as part of the regex pattern, because R has already used it to escape the special character,

So now, to get the regex engine to see the backslash as part of the regex pattern, you need to escape the backslash itself in R. Hence, you use double backslashes (\\) in R strings to represent a single backslash in the regex pattern. The first backslash escapes the second backslash in R, and the regex engine sees a single backslash followed by the special character, allowing it to interpret the special character according to regex rules.

Using Regex in R (with base)

Base R uses “grep” functions for regex operations.

• grep = global regular expression print.
• Semi-consistent argument order: function_name(pattern_to_find, string_to_search, other_args)

Common grep functions:

• grep(): Search for patterns in strings and return indices of matches
• grepl(): Search for patterns and return logical vector indicating matches
• gsub(): Replace occurrences of a pattern with a specified replacement

Using Regex in R (with base): Examples

# Example strings
text <- "The rain in Spain stays mainly in the plain."
title <- "My Fair Lady"

# Find which list elements contain "ain"
grep("ain", c(title, text))

[1] 2

# Replace "ain" with "XXX"
gsub("ain", "XXX", text)

[1] "The rXXX in SpXXX stays mXXXly in the plXXX."

# Check if the string starts with "The"
grepl("^The", text)

[1] TRUE

Using Regex in R (with stringr)

The stringr package (part of the tidyverse) provides an alternative set of string manipulation functions, using consistent syntax and behavior across nearly all stringr functions

• Naming: str_ + descriptive function name
• Argument order: str_func(string_to_search, pattern_to_find, other_args)
  • Note: this flips the order of the first two arguments compared to base R’s grep functions
• Accepts regex patterns plus some user-friendlier, tidyvers-ier enhancements

Common stringr functions:

• str_which(): Search for patterns in strings and return indices of matches
• str_detect(): Search for patterns and return logical vector indicating matches
• str_replace_all(): Replace occurrences of a pattern with a specified replacement

stringr has a good reason to flip the order. When using pipes (%>%), the data being operated on is passed as the first argument to the function. More when we talk tidyverse

stringr cheat sheet

Using Regex in R (with stringr): Examples

# Load stringr package for string manipulation
library(stringr)

# Example strings
text <- "The rain in Spain stays mainly in the plain."
title <- "My Fair Lady"

# Find which list elements contain "ain"
str_which(c(title, text), "ain")

[1] 2

# Replace "ain" with "XXX"
str_replace_all(text, "ain", "XXX")

[1] "The rXXX in SpXXX stays mXXXly in the plXXX."

# Check if the string starts with "The"
str_detect(text, "^The")

[1] TRUE

Summary

R Language

• R is an object-oriented programming language for statistical computing and graphics
• R syntax includes:
  • Variables: store data values
  • Data Types: numeric, character, logical, etc.
  • Data Structures: vectors, lists, data frames, matrices
  • Functions: reusable blocks of code that perform specific tasks
  • Comments: annotate code with #, ignored by R
• Case-sensitive but whitespace insensitive
• Functions are called using parentheses, with arguments passed inside
• Install and load packages to extend R’s functionality with additional functions and tools

R Programming

• Indexing and Subsetting: selecting specific elements from data structures using position or name
• Environments: data structures that store scoped variable bindings, including global and local environments
• Control Flow: managing the order of code execution using
  • Conditional statements (if, if...else)
  • Conditional functions (ifelse(), case_when())
  • for loops: iterate over a known, finite sequence
  • while loops: iterate while a condition is TRUE
• Regular Expressions (regex): sequences of characters forming search patterns for string matching and manipulation
  • Basic syntax includes symbols like ., ^, $, *, +, ?, [], |, ()
  • Escaping special characters with backslashes (\), using double backslashes (\\) in R strings
  • Use regex in R with base grep-style functions or stringr str_-style functions