R is a language designed primarily for statistical computing and data visualization. Its syntax is relatively simple and expressive, with a focus on vectorized operations. Below is an overview of the key aspects of R syntax:

Syntax

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1. Variables and Assignment

You can assign values to variables using <- (preferred) or =.

x <- 10  # Preferred
y = 20   # Also works

To print a variable:

print(x)
x  # Implicit printing in interactive mode

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2. Data Types

R supports various data types:

• Numeric: x <- 10.5
• Integer: y <- 10L (denoted with L)
• Character (String): z <- "Hello"
• Logical (Boolean): flag <- TRUE
• Complex: c <- 3 + 2i

To check the type:

class(x)  # Returns "numeric"

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3. Vectors (Key Data Structure)

Vectors are one-dimensional arrays and the most fundamental data type in R.

v <- c(1, 2, 3, 4, 5)  # Create a vector
v[1]  # Access first element
length(v)  # Get length

Vectorized operations:

v * 2  # Multiplies each element by 2
v + c(5, 6, 7, 8, 9)  # Element-wise addition

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4. Lists

Lists can hold multiple types of data.

my_list <- list(name="John", age=25, scores=c(90, 85, 88))
my_list$name  # Access list elements

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5. Matrices

Matrices are 2D arrays.

m <- matrix(1:9, nrow=3, ncol=3)
m[1, 2]  # Access row 1, column 2

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6. Data Frames (Like Tables)

Data frames are like spreadsheets.

df <- data.frame(name=c("Alice", "Bob"), age=c(25, 30))
df$name  # Access column
df[1, ]  # Access row

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7. Control Structures

If-Else:

x <- 10
if (x > 5) {
  print("Big")
} else {
  print("Small")
}

Loops:

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

while (x > 0) {
  print(x)
  x <- x - 1
}

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8. Functions

Define custom functions:

add_numbers <- function(a, b) {
  return(a + b)
}

result <- add_numbers(3, 5)
print(result)

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9. Apply Functions (Vectorized Operations)

Instead of loops, R uses apply functions:

nums <- c(1, 2, 3, 4, 5)
squared <- sapply(nums, function(x) x^2)
print(squared)

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10. Reading and Writing Data

Read CSV:

df <- read.csv("data.csv")

Write CSV:

write.csv(df, "output.csv")

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11. Plotting

Basic plot:

plot(1:10, (1:10)^2, type="b", col="red")

Histogram:

hist(rnorm(1000), col="blue")

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12. Packages and Libraries

Install and load packages:

install.packages("ggplot2")  # Install package
library(ggplot2)  # Load package

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13. Working with ggplot2 (Data Visualization)

Example:

library(ggplot2)
ggplot(mtcars, aes(x=mpg, y=hp)) + geom_point()

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R is powerful for data analysis and visualization. Would you like more details on a specific topic? 🚀

Import

Importing files into R depends on the file type. Below are the most common ways to import data:

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1. Import a CSV File

CSV (Comma-Separated Values) is one of the most common formats.

Using read.csv() (Built-in Function)

data <- read.csv("data.csv")  # Default assumes a header row
head(data)  # Show first few rows

• If the file has no headers:

  data <- read.csv("data.csv", header=FALSE)
  

• If the file uses a different delimiter (e.g., ; instead of ,):

  data <- read.csv("data.csv", sep=";")
  

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2. Import an Excel File

For Excel files (.xlsx), you need the readxl package.

Using readxl Package

install.packages("readxl")  # Install package (only needed once)
library(readxl)  # Load package

data <- read_excel("data.xlsx", sheet = 1)  # Read first sheet
head(data)

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3. Import a TXT File

For space- or tab-separated text files, use read.table().

data <- read.table("data.txt", header=TRUE, sep="\t")
head(data)

For space-separated files:

data <- read.table("data.txt", header=TRUE, sep=" ")

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4. Import an RDS File (.rds)

R's native format for saving objects.

data <- readRDS("data.rds")

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5. Import JSON Data

For JSON files, use the jsonlite package.

install.packages("jsonlite")
library(jsonlite)

data <- fromJSON("data.json")

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6. Import Data from a URL

You can read data directly from an online source.

data <- read.csv("https://example.com/data.csv")
head(data)

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7. Import a Database Table

For databases (MySQL, PostgreSQL, SQLite), use the DBI package.

install.packages("DBI")
library(DBI)

# Example for SQLite
conn <- dbConnect(RSQLite::SQLite(), "database.sqlite")
data <- dbReadTable(conn, "table_name")
dbDisconnect(conn)

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