Data Analytics with R

Data Analytics with R – R is a powerful statistical programming language widely used in data analysis, machine learning, and visualization. This guide will walk you through the basics, from setting up R to executing scripts and creating visualizations.

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Data Analytics with R

1. Exploring Basic Features of R – Data Analytics with R

What is R?

R is an open-source language designed for statistical computing and graphics. It is widely used in academia, research, and industry for analyzing and visualizing data.

Key Features of R:

• Open-source and available for free.
• Supports statistical analysis, data manipulation, and visualization.
• A vast collection of libraries for various applications (e.g., ggplot2 for visualization, dplyr for data manipulation).
• Integration with other programming languages like Python, C, and SQL.
• Strong community support with thousands of packages on CRAN (Comprehensive R Archive Network).

Installing R and RStudio

1. Download R from the official CRAN website: https://cran.r-project.org/.
2. Install RStudio, an IDE for R, from https://posit.co/download/rstudio-desktop/.
3. Open RStudio and start coding in R.

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2. Exploring RGUI (R Graphical User Interface)

R comes with a built-in GUI called RGUI. It allows you to write and execute R commands in an interactive environment.

Features of RGUI:

• Console: Executes commands.
• Script Editor: Write and save scripts (.R files).
• Workspace: Stores variables and objects created during the session.
• Help System: Provides built-in documentation.

Starting RGUI on Windows:

• Open R from the Start menu.
• The R Console will appear, where you can enter commands directly.

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3. Exploring RStudio – Data Analytics with R

RStudio is a powerful IDE that makes R programming more user-friendly. It provides a well-organized interface for coding and visualization.

Key Components of RStudio:

1. Console – Executes R commands.
2. Script Editor – Write, edit, and save scripts.
3. Environment Pane – Displays variables and objects.
4. Files, Plots, Packages, and Help Pane – Manages working directories, visualizations, and documentation.

Running a Script in RStudio:

1. Click on File > New File > R Script.
2. Write your R code.
3. Press Ctrl + Enter to run a line of code.
4. Save the script as a .R file.

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4. Handling Basic Expressions in R , Data Analytics with R

R can handle basic mathematical operations and expressions.

# Basic Arithmetic Operations
5 + 3  # Addition
10 - 2 # Subtraction
4 * 2  # Multiplication
8 / 2  # Division
2^3    # Exponentiation

R follows the PEMDAS rule (Parentheses, Exponents, Multiplication, Division, Addition, Subtraction).

# Order of operations
(3 + 2) * 4  # Output: 20

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5. Variables in R

A variable stores a value and can be used for calculations. In R, we use <- to assign values to variables.

x <- 10
y <- 5
z <- x + y
print(z)  # Output: 15

Rules for Naming Variables:

✔️ Variable names are case-sensitive (X and x are different).
✔️ They must start with a letter (not a number or special character).
✔️ Avoid using reserved words like if, for, while.

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6. Working with Vectors

A vector is a sequence of data elements of the same type. It is the most basic data structure in R.

Creating a Vector:

numbers <- c(1, 2, 3, 4, 5)
print(numbers)

Vector Operations:

sum(numbers)    # Sum of elements
length(numbers) # Number of elements
mean(numbers)   # Average value
max(numbers)    # Maximum value
min(numbers)    # Minimum value

Vectorized Operations:

numbers * 2  # Multiplies each element by 2

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7. Storing and Calculating Values in R

R allows calculations using stored values.

a <- 10
b <- 20
result <- a + b
print(result) # Output: 30

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8. Creating and Using Objects

Objects in R store values, functions, or datasets.

num_var <- 42      # Numeric
char_var <- "R Language"  # Character
bool_var <- TRUE   # Logical

To check an object’s type:

class(num_var)  # Output: "numeric"

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9. Interacting with Users

Use readline() to take user input.

name <- readline(prompt = "Enter your name: ")
print(paste("Hello,", name))

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10. Handling Data in R Workspace

Viewing Objects:

ls()  # List all objects

Removing Objects:

rm(x) # Remove a single object
rm(list = ls()) # Remove all objects

Saving and Loading Workspace:

save.image("my_workspace.RData")  # Save
load("my_workspace.RData")        # Load

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11. Executing Scripts , Data Analytics with R

To execute an R script:

1. Write the script in RStudio.
2. Save the file as script.R.
3. Run the script using: source("script.R")

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12. Creating Plots – Data Analytics with R

Basic Scatter Plot:

x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 6, 8, 10)

plot(x, y, type="o", col="blue", main="Basic Plot")

Histogram:

hist(x, main="Histogram")

Boxplot:

boxplot(y, main="Boxplot")

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13. Accessing Help and Documentation in R

Use built-in help functions:

?mean  # Help on mean function
help("plot")  # Help on plot function
help.start()  # Opens R help in the browser

Explore package documentation:

help(package="ggplot2")

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For AR-VR Notes	Click Here
For Big Data Analytics (BDA) Notes	Click Here

Conclusion

This guide covered:
✔️ Basic operations and expressions in R.
✔️ Working with variables and vectors.
✔️ Writing and executing R scripts.
✔️ Creating plots for data visualization.
✔️ Accessing R’s built-in help system.

Data Analytics with R

FAQ

RProgramming #DataScience #Statistics #RStudio #DataAnalysis #MachineLearning #DataVisualization #Coding #Programming #Analytics #BigData