Shiny Package in R Programming
Last Updated :
24 Nov, 2023
The package is an appropriate way to organize the work and share it with others. Packages in the R Programming language are a collection of R functions, compiled code, and sample data. They are stored under a directory called “library” in the R environment. By default, R installs a set of packages during installation. One of the most important packages in R is the Shiny package. Shiny is an R package that makes it easy to build interactive web apps straight from R.
Shiny package Install in R language
To use a package in R programming one must have to install the package first. This task can be done using the command install.packages(“packagename”). To install the whole Shiny package type this.
R
install.packages("shiny")
To install the latest development builds directly from GitHub, run this instead.
R
if (!require("remotes"))
install.packages("remotes")
remotes::install_github("rstudio/shiny")
Important Verb Functions in Shiny Package R Language
fluidPage(): It creates a page with a fluid layout. A fluid page layout consists of rows which in turn include columns. Rows exist for the purpose of making sure their elements appear on the same line and columns exist for the purpose of defining how much horizontal space within a 12-unit wide grid. Fluid pages scale their components in real-time to fill all available browser width.
Syntax:
fluidPage(..., title = NULL, theme = NULL)
|
| ... | Elements to include within the page. |
| title | The browser window title. |
| theme | Alternative Bootstrap stylesheet. |
Example:
This is a basic shiny app template.
R
# import shiny package
library(shiny)
# define a page with fluid layout
ui <- fluidPage(h1("GeeksforGeeks article on shiny package in R"),
p(style = "font-family:Impact", "My first shiny app")
)
server <- function(input, output) {}
shinyApp(ui = ui, server = server)
Output:
shinyApp(): It creates Shiny app objects from either an explicit UI/server pair or bypassing the path of a directory that contains a Shiny app.
Syntax:
shinyApp(ui, server, onStart = NULL, options = list(), uiPattern = "/", enableBookmarking = NULL)
shinyAppDir(appDir, options = list())
shinyAppFile(appFile, options = list())</p
|
| ui | The UI definition of the app. |
| server | It has three parameters: input, output, and session.
It is called once for each session to ensure that each app is independent.
|
| onStart | A function that will be called before the app is actually run. |
| options | Options that should be passed to the runApp. |
| uipattern | A regular expression that will be applied to each
GET request to determine whether the ui should be used to handle the request.
|
| enableBookmarking | Can be "url", "server", or "disable".The default value is NULL. |
| appDir | Path to directory that contains a Shiny app. |
| appFile | Path to a .R file containing a Shiny application. |
R
# import shiny package
library(shiny)
# define fluid page layout
ui <- fluidPage(
sliderInput(inputId = "num",
label = "Choose a number",
value = 10, min = 1, max = 1000),
plotOutput("hist")
)
server <- function(input, output)
{
output$hist <- renderPlot({
hist(rnorm(input$num))
})
}
# create shiny app object
# using shinyApp
shinyApp(ui = ui, server = server)
Output:
- Library Import: The code begins by importing the
shiny package using the library(shiny) function. Shiny is an R package for creating interactive web applications. - UI Definition: The
ui variable is defined to create a Shiny app's user interface (UI). It includes a slider input (sliderInput) allowing the user to choose a number between 1 and 1000 and a placeholder for a plot (plotOutput("hist")). - Server Logic: The
server function is defined to specify the server-side logic. It includes a reactive expression (renderPlot) that generates a histogram using hist() based on a random normal distribution with a sample size determined by the user's slider input (input$num). - App Initialization: The Shiny app object is created using
shinyApp(ui = ui, server = server), combining the UI and server components. - App Execution: The Shiny app is executed, and the interactive web application is launched. Users can interact with the slider to choose a number, and the corresponding histogram is dynamically updated based on the selected sample size.
reactive(): It creates a reactive expression. A reactive expression is an expression whose result will change over time.reactive() wraps a normal expression to create a reactive expression.
Syntax: reactive(x, env = parent.frame(), quoted = FALSE, label = NULL)
|
| x | An expression. |
| env | The parent environment for reactive expression. |
| quoted | Is the expression quoted? By default, this is FALSE.
This is useful when you want to use an expression that is stored in a variable
|
| label | A label for reactive expression. |
R
# import shiny package
library(shiny)
# define fluid page layout
ui <- fluidPage(
textInput(inputId = "num", label = "Choose a number",
value = "", width = 100, placeholder = NULL),
plotOutput("hist"),
verbatimTextOutput("stats")
)
server <- function(input, output)
{
# use reactive to create
# a reactive expression
data <- reactive({rnorm(input$num)})
output$hist <- renderPlot({hist(data())})
output$stats <- renderPrint({summary(data())})
}
# create shiny app object
# using shinyApp
shinyApp(ui = ui, server = server)
Output:
Now, change the input value from 50 to 100 to see what happens.
The output values(histogram and summary) also change with the alternation in the input values, i.e the app has reactivity.
observeEvent(): It triggers code to run on servers. Respond to "event-like" reactive inputs, values, and expressions.
Syntax:
observeEvent(eventExpr, handlerExpr, event.env = parent.frame(), event.quoted = FALSE, handler.env = parent.frame(), handler.quoted = FALSE, label = NULL, suspended = FALSE, priority = 0, domain = getDefaultReactiveDomain(), autoDestroy = TRUE, ignoreNULL = TRUE, ignoreInit = FALSE, once = FALSE)
|
| eventExpr | A expression that represents the event.It can be
a simple or a complex reactive expression.
|
| handler.Expr | The expression to call whenever eventExpr is invalidated. |
| event.env | The parent environment for eventExpr.
By default, this is the calling environment.
|
| event.quoted | Returns whether the eventExpr expression is quoted or not.
By default, this is FALSE.
|
| handler.env | The parent environment for handlerExpr. By default, this is the calling environment. |
| handler.quoted | Returns whether the handlerExpr expression is quoted or not.
By default, this is FALSE.
|
| label | A label for the observer or reactive |
| suspended | If TRUE, start the observer in a suspended state. |
| priority | An integer or numeric that controls the priority
with which this observer should be executed.Positive,Negative and Zero values are allowed.
|
| autodestroy | If TRUE (the default), the observer will be automatically
destroyed when its domain (if any) ends.
|
| ignoreNULL | Whether the action should be triggered when the input is NULL. |
| ignoreInit | If TRUE, then, when this observeEvent is first created/initialized,
ignore the handlerExpr (the second argument), whether
it is otherwise supposed to run or not. The default is FALSE.
|
| once | Whether this observeEvent should be immediately destroyed after
the first time that the code in handlerExpr is run.
|
R
# import shiny package
library(shiny)
# define fluid page layout
ui = fluidPage(
textInput(inputId = "num",
label = "Enter a numeric value", value = "10"),
actionButton("button", "Calculate"),
column(8, tableOutput("table"))
)
server = function(input, output)
{
# Take an action every time button is pressed
observeEvent(input$button, {
# Convert input$num to numeric
num <- as.numeric(input$num)
# Check if the conversion was successful
if (is.na(num)) {
cat("Invalid numeric value entered.\n")
return(NULL)
}
cat("Showing", num, "rows\n")
})
# Take a reactive dependency
# on input$num, but not on any
# of the stuff inside the function
df <- eventReactive(input$button, {
# Convert input$num to numeric
num <- as.numeric(input$num)
# Check if the conversion was successful
if (is.na(num)) {
cat("Invalid numeric value entered.\n")
return(NULL)
}
head(cars, num)
})
output$table <- renderTable({
df()
})
}
# create shiny app object
# using shinyApp
shinyApp(ui, server)
Output:
eventReactive(): A reactive expression that only responds to specific values. Respond to "event-like" reactive inputs, values, and expressions.
Syntax:
eventReactive(eventExpr,
valueExpr,
event.env = parent.frame(),
event.quoted = FALSE,
value.env = parent.frame(),
value.quoted = FALSE,
label = NULL,
domain = getDefaultReactiveDomain(),
ignoreNULL = TRUE,
ignoreInit = FALSE)
|
| eventExpr | An expression that represents the event.It can be a simple or a complex reactive expression. |
| valueExpr | It produces the return value of the eventReactive. It will be executed within an isolate() scope. |
| event.env | The parent environment for eventExpr. By default, this is the calling environment. |
| event.quoted | Returns whether the eventExpr expression is quoted or not. By default, this is FALSE. |
| value.env | The parent environment for valueExpr. By default, this is the calling environment. |
| value.quoted | Returns whether the valueExpr expression is quoted or not. By default, this is FALSE. |
| ignoreNULL | Whether the action should be triggered when the input is NULL. |
| ignoreInit | If TRUE, then, when this observeEvent is first created/initialized,
ignore the handlerExpr (the second argument), whether
it is otherwise supposed to run or not. The default is FALSE.
|
R
# import shiny package
library(shiny)
# define fluid page layout
ui <- fluidPage(
sliderInput(inputId = "num",
label = "Choose a number",
value = 25, min = 1, max = 100),
actionButton(inputId = "go",
label = "Update"),
plotOutput("hist")
)
server <- function(input, output)
{
data <- eventReactive(input$go, {
rnorm(input$num)
})
output$hist <- renderPlot({
hist(data())
})
}
# create shiny app object
# using shinyApp
shinyApp(ui = ui, server = server)
Output:
Here, the output does not change with the input values until and unless the update button is triggered
actionButton(): It creates an action button or a link. Their initial value is zero, and increments by one each time it is pressed.
Syntax:
actionButton(inputId, label, icon = NULL, width = NULL, ...)
actionLink(inputId, label, icon = NULL, ...)
|
| inputId | The input slot that will be used to access the value. |
| label | The contents of the button or link. |
| icon | An optional icon() to appear on the button. |
| width | The width of the input(ex-'200px',or'100%'). |
| .... | Named attributes to be applied to the button or link. |
R
# import shiny package
library(shiny)
# define fluid page layout
ui <- fluidPage(
sliderInput("obs", "Number of observations", 0, 1000, 500),
actionButton("goButton", "Go!", class = "btn-success"),
plotOutput("distPlot")
)
server <- function(input, output)
{
output$distPlot <- renderPlot({
input$goButton
dist <- isolate(rnorm(input$obs))
hist(dist)
})
}
# create shiny app object
# using shinyApp
shinyApp(ui, server)
Output:
The output gets updated as soon as the actionButton(Go!) is clicked.
checkboxGroupInput(): It creates a group of checkboxes that can be used to toggle multiple choices independently. The server will receive the input as a character vector of the selected values.
Syntax:
checkboxGroupInput(inputId, label, choices = NULL, selected = NULL, inline = FALSE, width = NULL, choiceNames = NULL, choiceValues = NULL)
|
| inputId | The input slot that will be used to access the value. |
| label | The contents of the button or link. |
| choices | List of values to show checkboxes for. If elements of
the list are named then that name rather than the value is displayed to the user.
|
| selected | The initial selection. |
| inline | If TRUE, render the choices horizontally. |
| width | The width of the input(ex-'200px',or'100%'). |
| choiceValues, choiceNames | List of names and values. |
R
# import shiny package
library(shiny)
# define fluid page layout
ui <- fluidPage(
checkboxGroupInput("icons", "Choose icons:",
choiceNames =
list(icon("dog"), icon("cat"),
icon("fish"), icon("bug")),
choiceValues =
list("dog", "cat", "fish", "bug")),
textOutput("txt")
)
server <- function(input, output, session)
{
output$txt <- renderText({
icons <- paste(input$icons, collapse = ", ")
paste("You chose", icons)
})
}
# create shiny app object
# using shinyApp
shinyApp(ui = ui, server = server)
Output:
textInput(): It creates a text input label.
Syntax:
textInput(inputId, label, value = "", width = NULL, placeholder = NULL)
|
| inputId | The input slot that will be used to access the value. |
| label | The contents of the button or link. |
| value | The initial value. |
| width | The width of the input(ex-'200px',or'100%'). |
| placeholder | A character string giving the user a hint as to what can be entered into the control. |
R
# import shiny package
library(shiny)
# define fluid page layout
ui <- fluidPage(
textInput("txt", "Enter your text here", "Empty"),
verbatimTextOutput("value")
)
server <- function(input, output)
{
output$value <- renderText({ input$text })
}
# create shiny app object
# using shinyApp
shinyApp(ui, server)
Output:
textOutput(): It creates a text output element. Render a reactive output variable as text within an application page.
Syntax:
textOutput(outputId, container = if (inline) span else div, inline = FALSE)
|
| outputId | The output variable to read the value from. |
| container | A function to generate an HTML element to contain the text. |
| inline | Use an inline (span()) or block container (div()) for the output. |
R
# import shiny package
library(shiny)
# define fluid page layout
ui <- fluidPage(
textInput(inputId = "Name", label = "Enter your name"),
textOutput("txt")
)
server <- function(input, output, session)
{
output$txt <- renderText({
Name <- paste(input$Name, collapse = ", ")
paste("Welcome! to geeksforgeeks ", Name)
})
}
# create shiny app object
# using shinyApp
shinyApp(ui = ui, server = server)
Output:
wellPanel(): Creates a panel with a slightly inset border and gray background.
Syntax: wellPanel(...)
|
| ... | UI elements to include inside the panel. |
R
# import shiny package
library(shiny)
# define fluid page layout
ui <- fluidPage(
sliderInput(inputId = "num",
label = "Choose a number",
value = 25, min = 1, max = 100),
# define plot inside
# a wellPanel
wellPanel(plotOutput("hist"))
)
server <- function(input, output)
{
output$hist <- renderPlot({
hist(rnorm(input$num), main = input$title)
})
}
# create shiny app object
# using shinyApp
shinyApp(ui = ui, server = server)
Output:
Observe that the histogram lies inside a gray-colored box(wellPanel).
This all function we can apply on different types of plots also now we apply this on scatter plot.
R
# Import shiny package
library(shiny)
# Define fluid page layout with enhanced styling
ui <- fluidPage(
titlePanel("Interactive Scatter Plot"),
# Input: Numeric input for the number of points
sidebarLayout(
sidebarPanel(
numericInput(inputId = "num_points", label = "Number of Points",
value = 50, min = 1, max = 100),
br(),
actionButton(inputId = "update_plot", label = "Update Plot")
),
# Output: Enhanced scatter plot
mainPanel(
plotOutput(outputId = "scatter_plot", height = "400px")
)
)
)
# Server logic
server <- function(input, output) {
# Generate random data based on user input
generate_data <- reactive({
data.frame(
x = rnorm(input$num_points),
y = rnorm(input$num_points)
)
})
# Render enhanced scatter plot with interactivity
output$scatter_plot <- renderPlot({
# Plot the generated data with enhanced aesthetics
plot(generate_data()$x, generate_data()$y,
main = "Interactive Scatter Plot",
xlab = "X-axis",
ylab = "Y-axis",
pch = 19, col = "skyblue",
xlim = c(-3, 3), ylim = c(-3, 3),
cex = 1.5)
})
# Add interactivity: Update plot when the "Update Plot" button is pressed
observeEvent(input$update_plot, {
invalidateLater(500, session) # Add a slight delay for a smoother update
output$scatter_plot <- renderPlot({
plot(generate_data()$x, generate_data()$y,
main = "Interactive Scatter Plot",
xlab = "X-axis",
ylab = "Y-axis",
pch = 19, col = "skyblue",
xlim = c(-3, 3), ylim = c(-3, 3),
cex = 1.5)
})
})
}
# Create enhanced Shiny app object using shinyApp
shinyApp(ui, server)
Output:
.jpg)
Shiny Package in R Programming
Shiny package helps to host standalone apps on a webpage embed them in R Markdown documents or build dashboards. One can also extend Shiny apps with CSS themes, HTML widgets, and JavaScript actions.
Similar Reads
R Tutorial | Learn R Programming Language R is an interpreted programming language widely used for statistical computing, data analysis and visualization. R language is open-source with large community support. R provides structured approach to data manipulation, along with decent libraries and packages like Dplyr, Ggplot2, shiny, Janitor a
4 min read
Introduction
R Programming Language - IntroductionR is a programming language and software environment that has become the first choice for statistical computing and data analysis. Developed in the early 1990s by Ross Ihaka and Robert Gentleman, R was built to simplify complex data manipulation and create clear, customizable visualizations. Over ti
4 min read
Interesting Facts about R Programming LanguageR is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge to
4 min read
R vs PythonR Programming Language and Python are both used extensively for Data Science. Both are very useful and open-source languages as well. For data analysis, statistical computing, and machine learning Both languages are strong tools with sizable communities and huge libraries for data science jobs. A th
5 min read
Environments in R ProgrammingThe environment is a virtual space that is triggered when an interpreter of a programming language is launched. Simply, the environment is a collection of all the objects, variables, and functions. Or, Environment can be assumed as a top-level object that contains the set of names/variables associat
3 min read
Introduction to R StudioR Studio is an integrated development environment(IDE) for R. IDE is a GUI, where you can write your quotes, see the results and also see the variables that are generated during the course of programming. R Studio is available as both Open source and Commercial software.R Studio is also available a
4 min read
How to Install R and R Studio?Navigating the R language installation process and setting up R Studio is crucial for anyone looking to delve into data analysis, statistical computing, and graphical representation with R. In this article, we provide a step-by-step guide on how to install R and configure R Studio on your system. Wh
6 min read
Creation and Execution of R File in R StudioR Studio is an integrated development environment(IDE) for R. IDE is a GUI, where you can write your quotes, see the results and also see the variables that are generated during the course of programming. R is available as an Open Source software for Client as well as Server Versions. Creating an R
5 min read
Clear the Console and the Environment in R StudioR Studio is an integrated development environment(IDE) for R. IDE is a GUI, where you can write your quotes, see the results and also see the variables that are generated during the course of programming. Clearing the Console We Clear console in R and RStudio, In some cases when you run the codes us
2 min read
Hello World in R ProgrammingWhen we start to learn any programming languages we do follow a tradition to begin HelloWorld as our first basic program. Here we are going to learn that tradition. An interesting thing about R programming is that we can get our things done with very little code. Before we start to learn to code, le
2 min read
Fundamentals of R
Basic Syntax in R ProgrammingR is the most popular language used for Statistical Computing and Data Analysis with the support of over 10, 000+ free packages in CRAN repository. Like any other programming language, R has a specific syntax which is important to understand if you want to make use of its features. This article assu
3 min read
Comments in RIn R Programming Language, Comments are general English statements that are typically written in a program to describe what it does or what a piece of code is designed to perform. More precisely, information that should interest the coder and has nothing to do with the logic of the code. They are co
3 min read
R-OperatorsOperators are the symbols directing the compiler to perform various kinds of operations between the operands. Operators simulate the various mathematical, logical, and decision operations performed on a set of Complex Numbers, Integers, and Numericals as input operands. R supports majorly four kinds
5 min read
R-KeywordsR keywords are reserved words that have special meaning in the language. They help control program flow, define functions, and represent special values. We can check for which words are keywords by using the help(reserved) or ?reserved function.Rhelp(reserved) # or "?reserved"Output:Reserved Key Wor
2 min read
R-Data TypesData types in R define the kind of values that variables can hold. Choosing the right data type helps optimize memory usage and computation. Unlike some languages, R does not require explicit data type declarations while variables can change their type dynamically during execution.R Programming lang
5 min read
Variables
R Variables - Creating, Naming and Using Variables in RA variable is a memory location reserved for storing data, and the name assigned to it is used to access and manipulate the stored data. The variable name is an identifier for the allocated memory block, which can hold values of various data types during the program’s execution.In R, variables are d
5 min read
Scope of Variable in RIn R, variables are the containers for storing data values. They are reference, or pointers, to an object in memory which means that whenever a variable is assigned to an instance, it gets mapped to that instance. A variable in R can store a vector, a group of vectors or a combination of many R obje
5 min read
Dynamic Scoping in R ProgrammingR is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge to
5 min read
Lexical Scoping in R ProgrammingLexical scoping means R decides where to look for a variable based on where the function was written (defined), not where it is called.When a function runs and it sees a variable, R checks:Inside the function, is the variable there?If not, it looks in the environment where the function was created.T
4 min read
Input/Output
Control Flow
Control Statements in R ProgrammingControl statements are expressions used to control the execution and flow of the program based on the conditions provided in the statements. These structures are used to make a decision after assessing the variable. In this article, we'll discuss all the control statements with the examples. In R pr
4 min read
Decision Making in R Programming - if, if-else, if-else-if ladder, nested if-else, and switchDecision making in programming allows us to control the flow of execution based on specific conditions. In R, various decision-making structures help us execute statements conditionally. These include:if statementif-else statementif-else-if laddernested if-else statementswitch statement1. if Stateme
3 min read
Switch case in RSwitch case statements are a substitute for long if statements that compare a variable to several integral values. Switch case in R is a multiway branch statement. It allows a variable to be tested for equality against a list of values. Switch statement follows the approach of mapping and searching
2 min read
For loop in RFor loop in R Programming Language is useful to iterate over the elements of a list, data frame, vector, matrix, or any other object. It means the for loop can be used to execute a group of statements repeatedly depending upon the number of elements in the object. It is an entry-controlled loop, in
5 min read
R - while loopWhile loop in R programming language is used when the exact number of iterations of a loop is not known beforehand. It executes the same code again and again until a stop condition is met. While loop checks for the condition to be true or false n+1 times rather than n times. This is because the whil
5 min read
R - Repeat loopRepeat loop in R is used to iterate over a block of code multiple number of times. And also it executes the same code again and again until a break statement is found. Repeat loop, unlike other loops, doesn't use a condition to exit the loop instead it looks for a break statement that executes if a
2 min read
goto statement in R ProgrammingGoto statement in a general programming sense is a command that takes the code to the specified line or block of code provided to it. This is helpful when the need is to jump from one programming section to the other without the use of functions and without creating an abnormal shift. Unfortunately,
2 min read
Break and Next statements in RIn R Programming Language, we require a control structure to run a block of code multiple times. Loops come in the class of the most fundamental and strong programming concepts. A loop is a control statement that allows multiple executions of a statement or a set of statements. The word ‘looping’ me
3 min read
Functions
Functions in R ProgrammingA function accepts input arguments and produces the output by executing valid R commands that are inside the function. Functions are useful when we want to perform a certain task multiple times.In R Programming Language when we are creating a function the function name and the file in which we are c
5 min read
Function Arguments in R ProgrammingArguments are the parameters provided to a function to perform operations in a programming language. In R programming, we can use as many arguments as we want and are separated by a comma. There is no limit on the number of arguments in a function in R. In this article, we'll discuss different ways
4 min read
Types of Functions in R ProgrammingA function is a set of statements orchestrated together to perform a specific operation. A function is an object so the interpreter is able to pass control to the function, along with arguments that may be necessary for the function to accomplish the actions. The function in turn performs the task a
6 min read
Recursive Functions in R ProgrammingRecursion, in the simplest terms, is a type of looping technique. It exploits the basic working of functions in R. Recursive Function in R: Recursion is when the function calls itself. This forms a loop, where every time the function is called, it calls itself again and again and this technique is
4 min read
Conversion Functions in R ProgrammingSometimes to analyze data using R, we need to convert data into another data type. As we know R has the following data types Numeric, Integer, Logical, Character, etc. similarly R has various conversion functions that are used to convert the data type. In R, Conversion Function are of two types: Con
4 min read
Data Structures
Data Structures in R ProgrammingA data structure is a particular way of organizing data in a computer so that it can be used effectively. The idea is to reduce the space and time complexities of different tasks. Data structures in R programming are tools for holding multiple values. R’s base data structures are often organized by
4 min read
R StringsStrings are a bunch of character variables. It is a one-dimensional array of characters. One or more characters enclosed in a pair of matching single or double quotes can be considered a string in R. It represents textual content and can contain numbers, spaces, and special characters. An empty stri
6 min read
R-VectorsR Vectors are the same as the arrays in R language which are used to hold multiple data values of the same type. One major key point is that in R Programming Language the indexing of the vector will start from '1' and not from '0'. We can create numeric vectors and character vectors as well. R - Vec
4 min read
R-ListsA list in R programming is a generic object consisting of an ordered collection of objects. Lists are one-dimensional, heterogeneous data structures. The list can be a list of vectors, a list of matrices, a list of characters, a list of functions, and so on. A list in R is created with the use of th
6 min read
R - ArrayArrays are important data storage structures defined by a fixed number of dimensions. Arrays are used for the allocation of space at contiguous memory locations.In R Programming Language Uni-dimensional arrays are called vectors with the length being their only dimension. Two-dimensional arrays are
7 min read
R-MatricesR-matrix is a two-dimensional arrangement of data in rows and columns. In a matrix, rows are the ones that run horizontally and columns are the ones that run vertically. In R programming, matrices are two-dimensional, homogeneous data structures. These are some examples of matrices:R - MatricesCreat
10 min read
R-FactorsFactors in R Programming Language are used to represent categorical data, such as "male" or "female" for gender. While they might seem similar to character vectors, factors are actually stored as integers with corresponding labels. Factors are useful when dealing with data that has a fixed set of po
4 min read
R-Data FramesR Programming Language is an open-source programming language that is widely used as a statistical software and data analysis tool. Data Frames in R Language are generic data objects of R that are used to store tabular data. Data frames can also be interpreted as matrices where each column of a matr
6 min read
Object Oriented Programming
R-Object Oriented ProgrammingIn R, Object-Oriented Programming (OOP) uses classes and objects to manage program complexity. R is a functional language that applies OOP concepts. Class is like a car's blueprint, detailing its model, engine and other features. Based on this blueprint, we select a car, which is the object. Each ca
7 min read
Classes in R ProgrammingClasses and Objects are core concepts in Object-Oriented Programming (OOP), modeled after real-world entities. In R, everything is treated as an object. An object is a data structure with defined attributes and methods. A class is a blueprint that defines a set of properties and methods shared by al
3 min read
R-ObjectsIn R programming, objects are the fundamental data structures used to store and manipulate data. Objects in R can hold different types of data, such as numbers, characters, lists, or even more complex structures like data frames and matrices.An object in R is important an instance of a class and can
3 min read
Encapsulation in R ProgrammingEncapsulation is the practice of bundling data (attributes) and the methods that manipulate the data into a single unit (class). It also hides the internal state of an object from external interference and unauthorized access. Only specific methods are allowed to interact with the object's state, en
3 min read
Polymorphism in R ProgrammingR language implements parametric polymorphism, which means that methods in R refer to functions, not classes. Parametric polymorphism primarily lets us define a generic method or function for types of objects we haven’t yet defined and may never do. This means that one can use the same name for seve
6 min read
R - InheritanceInheritance is one of the concept in object oriented programming by which new classes can derived from existing or base classes helping in re-usability of code. Derived classes can be the same as a base class or can have extended features which creates a hierarchical structure of classes in the prog
7 min read
Abstraction in R ProgrammingAbstraction refers to the process of simplifying complex systems by concealing their internal workings and only exposing the relevant details to the user. It helps in reducing complexity and allows the programmer to work with high-level concepts without worrying about the implementation.In R, abstra
3 min read
Looping over Objects in R ProgrammingOne of the biggest issues with the “for†loop is its memory consumption and its slowness in executing a repetitive task. When it comes to dealing with a large data set and iterating over it, a for loop is not advised. In this article we will discuss How to loop over a list in R Programming Language
5 min read
S3 class in R ProgrammingAll things in the R language are considered objects. Objects have attributes and the most common attribute related to an object is class. The command class is used to define a class of an object or learn about the classes of an object. Class is a vector and this property allows two things:  Objects
8 min read
Explicit Coercion in R ProgrammingCoercing of an object from one type of class to another is known as explicit coercion. It is achieved through some functions which are similar to the base functions. But they differ from base functions as they are not generic and hence do not call S3 class methods for conversion. Difference between
3 min read
Error Handling