File Operations

📂 R File Operations: Your Digital Filing Cabinet

Imagine your computer is a huge library. R is your super-smart librarian who can read books, write new ones, organize shelves, and remember exactly where everything goes!

🎯 The Big Picture

Think of file operations like running a post office:

• 📤 Output = Sending letters out (showing messages)
• 📥 Input = Receiving letters (reading data)
• 📁 Files = The actual letters and packages
• 🗂️ Directories = The mailroom shelves

Let’s learn how R becomes your personal mail carrier!

📤 Console Output Functions

What Are They?

Output functions are like a megaphone for R. They let R “speak” to you by showing messages on the screen.

The Main Tools

print() - The Basic Announcer

print("Hello, World!")
# Output: [1] "Hello, World!"

x <- 42
print(x)
# Output: [1] 42

cat() - The Cleaner Speaker

cat("My name is R!\n")
# Output: My name is R!

cat("Score:", 100, "\n")
# Output: Score: 100

💡 Key Difference: print() shows quotes and [1]. cat() gives clean, simple output!

message() - For Important Notes

message("Loading data...")
# Shows in different color (diagnostic)

warning() - Yellow Flag Alert

warning("File might be old!")
# Shows a warning message

📥 Console Input Functions

What Are They?

Input functions are like asking a question and waiting for an answer. R pauses and listens!

readline() - Ask and Listen

name <- readline("What is your name? ")
# User types: Alice
# name now contains "Alice"

cat("Hello,", name, "!\n")
# Output: Hello, Alice !

scan() - Read Multiple Values

numbers <- scan(n = 3)
# User types: 10 20 30
# numbers = c(10, 20, 30)

graph TD
    A["R asks question"] --> B["User types answer"]
    B --> C["R stores the answer"]
    C --> D["R uses the answer"]

📊 Reading CSV and Table Files

The Magic of CSV Files

CSV = Comma Separated Values

Think of it like a spreadsheet saved as simple text:

name,age,score
Alice,10,95
Bob,12,88

read.csv() - Your CSV Reader

# Read a CSV file
students <- read.csv("students.csv")

# See what's inside
head(students)

read.table() - More Flexible Reader

# Read any table file
data <- read.table(
  "data.txt",
  header = TRUE,
  sep = "\t"  # Tab-separated
)

Common Options

💾 Writing CSV and Table Files

Saving Your Work

Just like saving a game, you need to save your data!

write.csv() - Save as CSV

# Create some data
students <- data.frame(
  name = c("Alice", "Bob"),
  score = c(95, 88)
)

# Save it!
write.csv(students, "my_data.csv")

write.table() - More Control

write.table(
  students,
  "my_data.txt",
  sep = "\t",
  row.names = FALSE
)

⚠️ Pro Tip: Use row.names = FALSE to avoid an extra mystery column!

graph TD
    A["Your Data in R"] --> B{Choose Format}
    B -->|CSV| C["write.csv"]
    B -->|Custom| D["write.table"]
    C --> E["file.csv"]
    D --> F["file.txt"]

📝 Reading and Writing Text Files

Simple Text - Like Notes!

Sometimes you just want to read or write plain text, like a diary.

readLines() - Read Text File

# Read all lines from a file
poem <- readLines("poem.txt")

# poem is a vector of lines
print(poem[1])  # First line

writeLines() - Write Text File

# Create some lines
my_lines <- c(
  "Line one",
  "Line two",
  "The end!"
)

# Save to file
writeLines(my_lines, "note.txt")

readr Package - Modern Way

library(readr)

# Fast reading
text <- read_file("story.txt")

# Fast writing
write_file("Hello!", "greeting.txt")

💼 R Object Persistence

What Is Persistence?

Persistence means saving R objects so they survive when you close R. Like freezing food to eat later!

save() - Freeze Objects

x <- 100
y <- c(1, 2, 3)

# Save these objects
save(x, y, file = "my_stuff.RData")

load() - Unfreeze Objects

# Later, bring them back!
load("my_stuff.RData")

print(x)  # 100 is back!

saveRDS() / readRDS() - Single Object

# Save ONE object
big_result <- lm(mpg ~ wt, data = mtcars)
saveRDS(big_result, "model.rds")

# Load it back with any name
my_model <- readRDS("model.rds")

graph TD
    A["R Objects in Memory"] --> B{How Many?}
    B -->|Multiple| C["save / load"]
    B -->|One| D["saveRDS / readRDS"]
    C --> E[".RData file"]
    D --> F[".rds file"]

💡 Best Practice: Use .rds for single objects, .RData for multiple!

🗂️ Working Directory Management

What Is Working Directory?

The working directory is R’s “home base” - the folder where R looks for files by default.

Think of it like: Where am I standing in the library?

getwd() - Where Am I?

getwd()
# "/Users/alice/projects"

setwd() - Move to New Folder

setwd("/Users/alice/data")

# Now R looks here first!

Best Practice: Use Projects!

Instead of setwd(), use RStudio Projects. They automatically set the right folder!

graph TD
    A["Open R"] --> B["getwd - Check location"]
    B --> C{Right folder?}
    C -->|No| D["setwd - Change location"]
    C -->|Yes| E["Start working!"]
    D --> E

📁 File System Operations

R as File Manager

R can do everything your file explorer does!

Check If File Exists

file.exists("data.csv")
# TRUE or FALSE

List Files in Folder

# All files
list.files()

# Only CSV files
list.files(pattern = "\\.csvquot;)

Create Folders

dir.create("new_folder")

# Create nested folders
dir.create("a/b/c", recursive = TRUE)

Copy, Move, Delete

# Copy a file
file.copy("old.txt", "new.txt")

# Rename/Move
file.rename("old.txt", "moved.txt")

# Delete (careful!)
file.remove("temp.txt")

Get File Info

file.info("data.csv")
# Shows size, modified date, etc.

Path Helpers

# Combine paths safely
file.path("folder", "file.csv")
# "folder/file.csv"

# Get just the filename
basename("/path/to/file.csv")
# "file.csv"

# Get just the folder
dirname("/path/to/file.csv")
# "/path/to"

🎁 Quick Summary

🚀 You Did It!

You now know how to:

• ✅ Talk to users with output functions
• ✅ Listen to users with input functions
• ✅ Read and write CSV files
• ✅ Handle text files like a pro
• ✅ Save R objects for later
• ✅ Navigate folders like a file explorer
• ✅ Manage files programmatically

R is now your personal file assistant! 📂✨