R is a powerful programming language mainly used for data analysis, statistics, data visualization, and machine learning. It is widely used by data scientists, statisticians, researchers, and analysts.
In this blog, each R topic is explained in one simple line followed by a small example, written in easy English, making it perfect for beginners, students, and interview preparation.
1. What is R?
R is a programming language used for statistical computing and data analysis.
print("Hello R")
2. History of R
R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland.
# R was created in 1993
3. Features of R
R provides powerful tools for data analysis, visualization, and statistical modeling.
summary(c(10,20,30))
4. Applications of R
R is used in data science, finance, healthcare, research, and machine learning.
mean(c(5,10,15))
5. R Program Structure
An R program is written as simple statements executed line by line.
x <- 10
print(x)
6. Variables in R
Variables store data values using the assignment operator <-.
age <- 25
7. Data Types in R
R supports numeric, character, logical, and complex data types.
| Structure | Description | Example |
|---|---|---|
| Vector | Same data type elements | v <- c(1, 2, 3) |
| List | Different data types | l <- list(1, "R", TRUE) |
| Matrix | 2D data (same type) | m <- matrix(1:6, nrow=2) |
| Array | Multi-dimensional data | a <- array(1:8, dim=c(2,2,2)) |
| Data Frame | Table-like structure | df <- data.frame(id=1:3, name=c("A","B","C")) |
| Factor | Categorical data | f <- factor(c("Yes","No","Yes")) |
name <- "Krishna"
isPassed <- TRUE
8. Operators in R
Operators perform arithmetic and logical operations.
10 + 58.1 Arithmetic Operators
x <- 10
y <- 5
x + y
x - y
x * y
x / y
x %% y
8.2 Relational Operators
x > y
x < y
x == y
x != y
8.3 Logical Operators
x > 5 & y < 10
x > 5 | y > 10
!TRUE
8.4 Assignment Operators
a <- 5
b = 10
9. Conditional Statements
Conditional statements control decision-making in programs.
if (age >= 18) print("Adult")
10. Loops in R
Loops repeat code execution multiple times.
for (i in 1:3) print(i)
11. Functions
Functions are reusable blocks of code.
add <- function(a,b){ a+b }
12. Vectors
Vectors store multiple values of the same type.
v <- c(1,2,3)
13. Lists
Lists store different types of data together.
l <- list(name="Raj", age=21)
14. Matrices
Matrices store data in row and column format.
m <- matrix(1:4, nrow=2)
15. Data Frames
Data frames store tabular data like Excel tables.
df <- data.frame(id=1:3, marks=c(80,90,100))
16. Factors
Factors are used to represent categorical data.
gender <- factor(c("Male","Female"))
17. File Handling
R can read and write data files easily.
write.csv(df,"data.csv")
18. Data Visualization
R provides powerful plotting libraries.
plot(v)
19. Packages in R
Packages add extra functionality to R.
install.packages("ggplot2")
20. Statistical Functions
R is rich in built-in statistical functions.
sd(c(10,20,30))
21. NA and Missing Values
R uses NA to represent missing data.
is.na(NA)
22. Data Manipulation
R can filter and modify data easily.
df$marks[df$marks > 80]
23. R for Machine Learning
R supports machine learning through libraries.
library(caret)
24. Advantages of R
R is free, open-source, and excellent for data analysis.
# Large community support
25. Disadvantages of R
R is slower for large-scale production systems.
# Memory intensive
26. Interview Important Points
R is mainly used for data analysis and visualization.
27. Learning Path for Beginners
Start with basics, learn data structures, then visualization and ML.
28. Conclusion
R is a powerful and beginner-friendly language for data analysis and statistics. Learning R helps you work with real-world data and build strong analytical skills.
