R Language Basics & Tables

---

1. Variables & Data Types

# Numeric
x <- 42
y <- 3.14

# Character (string)
name <- "Alice"

# Logical
is_true <- TRUE

# Check types
class(x)       # "numeric"

[1] "numeric"

class(name)    # "character"

[1] "character"

class(is_true) # "logical"

[1] "logical"

---

2. Vectors

# Create a vector
nums <- c(1, 2, 3, 4, 5)
fruits <- c("apple", "banana", "cherry")

# Access elements (1-indexed)
nums[1]      # 1

[1] 1

fruits[2]    # "banana"

[1] "banana"

# Vector operations
nums * 2     # 2 4 6 8 10

[1]  2  4  6  8 10

sum(nums)    # 15

[1] 15

mean(nums)   # 3

[1] 3

---

3. Data Frames

# Create a data frame
df <- data.frame(
  Name  = c("Alice", "Bob", "Carol"),
  Age   = c(25, 30, 28),
  Score = c(88, 92, 95)
)

# Inspect
print(df)

   Name Age Score
1 Alice  25    88
2   Bob  30    92
3 Carol  28    95

str(df)

'data.frame':   3 obs. of  3 variables:
 $ Name : chr  "Alice" "Bob" "Carol"
 $ Age  : num  25 30 28
 $ Score: num  88 92 95

summary(df)

        Name        Age            Score      
 Length   :3   Min.   :25.00   Min.   :88.00  
 N.unique :3   1st Qu.:26.50   1st Qu.:90.00  
 N.blank  :0   Median :28.00   Median :92.00  
 Min.nchar:3   Mean   :27.67   Mean   :91.67  
 Max.nchar:5   3rd Qu.:29.00   3rd Qu.:93.50  
               Max.   :30.00   Max.   :95.00  

# Access columns
df$Name

[1] "Alice" "Bob"   "Carol"

# Filter rows
df[df$Age > 25, ]

---

4. Control Flow

# If / else
x <- 10
if (x > 5) {
  print("Greater than 5")
} else {
  print("5 or less")
}

[1] "Greater than 5"

# For loop
for (i in 1:5) {
  print(i)
}

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

# While loop
count <- 1
while (count <= 3) {
  print(count)
  count <- count + 1
}

[1] 1
[1] 2
[1] 3

---

5. Functions

# Define a function
add <- function(a, b) {
  return(a + b)
}

add(3, 7)   # 10

[1] 10

# With default argument
greet <- function(name = "World") {
  paste("Hello,", name)
}

greet()           # "Hello, World"

[1] "Hello, World"

greet("Alice")    # "Hello, Alice"

[1] "Hello, Alice"

---

6. String Operations

s <- "Hello, R!"

nchar(s)               # 9

[1] 9

toupper(s)             # "HELLO, R!"

[1] "HELLO, R!"

tolower(s)             # "hello, r!"

[1] "hello, r!"

substring(s, 1, 5)     # "Hello"

[1] "Hello"

gsub("R", "World", s)  # "Hello, World!"

[1] "Hello, World!"

paste("Good", "morning")   # "Good morning"

[1] "Good morning"

paste0("value=", 42)       # "value=42"

[1] "value=42"

---

7. Basic Statistics

data <- c(4, 8, 15, 16, 23, 42)

mean(data)      # mean

[1] 18

median(data)    # median

[1] 15.5

sd(data)        # standard deviation

[1] 13.49074

var(data)       # variance

[1] 182

range(data)     # min and max

[1]  4 42

quantile(data)  # quartiles

   0%   25%   50%   75%  100% 
 4.00  9.75 15.50 21.25 42.00 

---

8. Basic Plotting

# Line plot
plot(1:10, type = "l", main = "Line Plot", xlab = "X", ylab = "Y", col = "steelblue", lwd = 2)

# Bar chart
barplot(c(3, 5, 2, 8),
        names.arg = c("A", "B", "C", "D"),
        main = "Bar Chart",
        col = c("steelblue", "tomato", "seagreen", "goldenrod"))

# Histogram
set.seed(42)
hist(rnorm(100), main = "Histogram", col = "steelblue", border = "white", xlab = "Value")

# Scatter plot
set.seed(42)
x <- rnorm(50)
y <- rnorm(50)
plot(x, y, main = "Scatter Plot", col = "tomato", pch = 16, cex = 1.2)

---

9. Importing & Exporting Data

# Read CSV
df <- read.csv("data.csv")

# Write CSV
write.csv(df, "output.csv", row.names = FALSE)

# Read with readr (tidyverse)
# install.packages("readr")
library(readr)
df <- read_csv("data.csv")

---

10. Useful Built-in Functions

length(c(1, 2, 3))      # 3

[1] 3

seq(1, 10, by = 2)      # 1 3 5 7 9

[1] 1 3 5 7 9

rep(0, times = 5)       # 0 0 0 0 0

[1] 0 0 0 0 0

sort(c(3, 1, 2))        # 1 2 3

[1] 1 2 3

rev(c(1, 2, 3))         # 3 2 1

[1] 3 2 1

unique(c(1, 1, 2, 3))   # 1 2 3

[1] 1 2 3

is.na(NA)               # TRUE

[1] TRUE

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11. Grids & Tables

11.1 Basic Matrix (Grid)

# Create a 3x3 matrix
m <- matrix(1:9, nrow = 3, ncol = 3)
print(m)

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

# Fill by row
m2 <- matrix(1:9, nrow = 3, byrow = TRUE)
print(m2)

     [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6
[3,]    7    8    9

11.2 Data Frame as Table

df <- data.frame(
  Name  = c("Alice", "Bob", "Carol"),
  Age   = c(25, 30, 28),
  Score = c(88, 92, 95)
)

print(df)

   Name Age Score
1 Alice  25    88
2   Bob  30    92
3 Carol  28    95

11.3 Pretty Table with knitr::kable()

library(knitr)

kable(df, caption = "Student Scores", align = c("l", "c", "c"))

Student Scores

Name	Age	Score
Alice	25	88
Bob	30	92
Carol	28	95

11.4 Interactive Table with DT

# install.packages("DT")
library(DT)

datatable(df,
          options = list(pageLength = 5),
          caption = "Interactive Student Table")

11.5 Contingency Table (Frequency Count)

gender <- c("M", "F", "M", "F", "M")
passed <- c("Yes", "No", "Yes", "Yes", "No")

table(gender, passed)

      passed
gender No Yes
     F  1   1
     M  1   2

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Summary: Table Methods in R

R Table Methods Overview

Method	Best For
matrix()	Numeric grids, math operations
data.frame()	Mixed data, analysis
kable()	Markdown reports
DT::datatable()	Interactive browsing
table()	Frequency counts