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2. R Basics – Syntax & Core Concepts

This section covers the foundational elements of R programming. Once you master these, you'll be able to read, write, and understand most R code used in data analysis, statistics, and visualization.

2.1 Variables, Data Types & Basic Operations

In R, you create variables using <- (traditional) or = (also accepted).

Basic data types in R

R

# Numeric (integer or double) x <- 42 # integer y <- 3.14 # double (floating point) z <- 1L # explicit integer (L suffix) # Character (strings) name <- "Anshuman" city <- 'Ranchi' # Logical (boolean) is_student <- TRUE has_degree <- FALSE # Check type class(x) # "numeric" typeof(y) # "double" is.numeric(z) # TRUE

Basic operations

R

a <- 10 b <- 3 print(a + b) # 13 print(a - b) # 7 print(a * b) # 30 print(a / b) # 3.333333 print(a %/% b) # integer division → 3 print(a %% b) # modulo → 1 print(a ^ b) # power → 1000

Logical operations

R

print(a > b) # TRUE print(a == 10) # TRUE print(a != 5) # TRUE print(!TRUE) # FALSE print(TRUE & FALSE) # FALSE (AND) print(TRUE | FALSE) # TRUE (OR)

Tip: Use <- for assignment (community standard). Avoid = except in function arguments.

2.2 Vectors, Lists, Matrices & Arrays

Vectors – The most basic and most used data structure in R (1D, atomic)

R

# Create vector v1 <- c(1, 2, 3, 4, 5) v2 <- 10:20 # sequence 10 to 20 v3 <- seq(0, 10, by = 2) # 0, 2, 4, ..., 10 # Operations are vectorized (no loops needed!) print(v1 * 2) # 2 4 6 8 10 print(v1 + 100) # 101 102 103 104 105 # Indexing (starts from 1!) print(v1[3]) # 3 print(v1[c(1, 3, 5)]) # 1 3 5 print(v1[-2]) # exclude 2nd element → 1 3 4 5

Lists – Can hold mixed types (most flexible)

R

my_list <- list( name = "Anshuman", age = 25, scores = c(85, 92, 78), passed = TRUE, details = list(city = "Ranchi", state = "Jharkhand") ) print(my_list$name) # "Anshuman" print(my_list[[3]]) # scores vector print(my_list$details$city) # "Ranchi"

Matrices – 2D, homogeneous (same type)

R

m <- matrix(1:12, nrow = 3, ncol = 4, byrow = TRUE) print(m) # [,1] [,2] [,3] [,4] # [1,] 1 2 3 4 # [2,] 5 6 7 8 # [3,] 9 10 11 12 print(m[2, 3]) # 7 print(m[, 2]) # column 2 → 2 6 10

Arrays – Multi-dimensional (rarely used directly)

R

arr <- array(1:24, dim = c(2, 3, 4)) # 2×3×4 array print(arr[1, 2, 3]) # access element

2.3 Factors & Data Frames – The Heart of R

Factors – Used for categorical data (levels)

R

gender <- factor(c("Male", "Female", "Male", "Other", "Female")) print(gender) # [1] Male Female Male Other Female # Levels: Female Male Other levels(gender) <- c("F", "M", "O") # rename levels print(gender)

Data Frames – Rectangular table (like Excel or SQL table) – most important structure

R

# Create data frame students <- data.frame( name = c("Anshuman", "Priya", "Rahul", "Sneha"), age = c(25, 23, 24, 22), marks = c(92, 88, 85, 90), passed = c(TRUE, TRUE, TRUE, TRUE) ) print(students) # name age marks passed # 1 Anshuman 25 92 TRUE # 2 Priya 23 88 TRUE # 3 Rahul 24 85 TRUE # 4 Sneha 22 90 TRUE # Access students$marks students[1, ] # first row students[, "age"] # age column students[students$age > 23, ] # filter

Important: Data frames are lists of vectors (columns) — each column must be same length.

2.4 Control Structures (if-else, for, while, apply family)

if-else

R

score <- 85 if (score >= 90) { print("A+") } else if (score >= 80) { print("A") } else { print("B or below") }

for loop

R

for (i in 1:5) { print(i^2) }

while loop

R

count <- 1 while (count <= 5) { print(count) count <- count + 1 }

apply family – Vectorized alternatives to loops (very important in R)

R

# apply (for matrices/arrays) m <- matrix(1:12, nrow=3) apply(m, 1, sum) # sum each row # lapply (returns list) lapply(students$marks, function(x) x + 5) # sapply (simplifies to vector/matrix) sapply(students$marks, function(x) x > 85) # tapply (grouped apply) tapply(students$marks, students$age > 23, mean)

Best practice: Avoid explicit for loops when possible — use apply, lapply, sapply, tapply, or tidyverse map_* functions.

2.5 Writing Your First R Script

Create a new R script in RStudio File → New File → R Script

Example script – student_analysis.R

R

# student_analysis.R # Load packages library(tidyverse) # Sample data students <- data.frame( name = c("Anshuman", "Priya", "Rahul", "Sneha"), age = c(25, 23, 24, 22), marks = c(92, 88, 85, 90) ) # Summary summary(students) # Visualization ggplot(students, aes(x = age, y = marks)) + geom_point(size = 4, color = "blue") + geom_smooth(method = "lm", color = "red") + labs(title = "Marks vs Age", x = "Age", y = "Marks") + theme_minimal() # Save plot ggsave("marks_vs_age.png", width = 8, height = 6, dpi = 300) # Save results write.csv(students, "students_data.csv", row.names = FALSE) print("Analysis complete!")

Run script

• Press Ctrl + Enter (line by line)

• Or Source entire script (Ctrl + Shift + S)

This completes the full R Basics – Syntax & Core Concepts section — now you have the strong foundation to write real R code! of Numerical Computing section — the true backbone of all data science in Python!

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