R: R Basic Syntax

Last updated: 2026-08-26

In the previous lesson, we installed R and RStudio and ran our first line of code cat("Hello"). In this lesson, we’ll thoroughly master R’s “four basic syntax elements”—comments, variables, assignments, and output. This will serve as the foundation for the next 28 lessons.

R’s syntax differs somewhat from that of other languages: comments use # instead of //, assignments typically use <- instead of =, and string concatenation uses paste() instead of +. Understanding these four differences is half the battle when writing R code.

1. What You'll Learn

• How to Write Single-Line and Multi-Line Comments in R
• Naming Conventions and Best Practices for Variables
• The Differences Between the 4 Assignment Operators (<- = -> <<-)
• Differences between the print() and cat() output functions
• The Concept of a Working Directory and Using RStudio Projects
• Rscript Running R scripts from the command line

2. Comments: Adding "Comments" to Code

Comments are text intended for human readers; the R interpreter completely ignores them. However, comments are not merely "decorations"—they are a programmer’s most important communication tool.

(1) Why are comments important?

Imagine looking back at the code you wrote six months from now:

# Code without comments:
x <- df %>% filter(age > 18) %>% mutate(group = ifelse(score >= 60, "pass", "fail"))

# Code with Comments:
# Filter 18 Adults aged 18 and older,and by score 60 Classified as "Approved"/Failed both groups
x <- df %>% filter(age > 18) %>% mutate(group = ifelse(score >= 60, "pass", "fail"))

The latter is immediately understandable, while the former takes 5 minutes to figure out. That’s the value of comments.

(2) R Comment Syntax

R Only single-line comments (using #), no /* */ block comments:

(3) How do I handle multi-line comments?

R does not have native multi-line comments, but there are two workarounds:

# Plan 1: Use this on every line # (Recommendations, Standard Practice)
# Line 1 Comments
# Line 2 Comments
# Line 3 Comments
x <- 10

# Plan 2:RStudio Keyboard Shortcuts(Most Commonly Used)
# Select multiple lines of code → Ctrl+Shift+C (macOS: Cmd+Shift+C)
# RStudio Automatically add to each line #,Click again to remove the comment

3. Variables and Naming Conventions

Variables are "containers" used to store data. R is a dynamically typed language, so you don't need to declare types in advance; you can simply assign values to variables.

(1) Naming Conventions (Must be followed; otherwise, an error will occur)

(2) Naming Conventions (Recommended)

There are two mainstream naming conventions in the R community; choose one and stick with it:

This tutorial consistently uses snake_case (in line with the style of modern R packages such as dplyr and ggplot2).

(3) View Variables

# Assignment
x <- 10
y <- 20

# View a Single Variable
x
# [1] 10

# View All Variables(Equivalent to Environment Pane)
ls()
# [1] "x" "y"

4. Assignment Operator: <- vs =

R has two top-level assignment operators; they are almost equivalent in function, but <- is recommended.

(1) Comparison of the Two Writing Styles

# Writing Style 1:<-  (Recommendations)
x <- 10

# Writing Style 2:=   (Available but not recommended)
x = 10

(2) Reverse Assignment -> and Global Assignment <<-

R also has two "advanced" assignment operators:

(3) The Importance of Spaces: <- vs < -

# Correct: Assignment
x <- 10

# Error: This is "less-than sign" Plus "negative 10" (Comparison x and -10)
x < -10

5. Output Functions: print() vs. cat()

R has two commonly used output functions, which serve different purposes.

(1) A Table That Shows the Differences at a Glance

(2) Live Demonstration

name <- "Alice"
age <- 25

# print() -- General Output (with [1] and quotation marks)
print(name)
# [1] "Alice"

# cat() —— Concatenated Output(Recommendations,More flexible)
cat("Name:", name, ",Age:", age, "\n")
# Name: Alice ,Age: 25

(3) Other Output Functions

6. Working Directory

When R starts up, it enters a working directory by default, and all file reading and writing takes place there.

(1) The Importance of the Working Directory

graph LR
    A["R Process"] --> B["Working Directory<br/>(work dir)"]
    B --> C["data/sales.csv<br/>(Relative Path = ./data/sales.csv)"]
    B --> D["scripts/clean.R<br/>(Relative Path)"]
    B --> E["output/report.html<br/>(Relative Path)"]
    
    style A fill:#fff3cd
    style B fill:#d4edda

graph LR
    A["R Process"] --> B["Working Directory<br/>(work dir)"]
    B --> C["data/sales.csv<br/>(Relative Path = ./data/sales.csv)"]
    B --> D["scripts/clean.R<br/>(Relative Path)"]
    B --> E["output/report.html<br/>(Relative Path)"]
    
    style A fill:#fff3cd
    style B fill:#d4edda

If the working directory is incorrect, read_csv("data/sales.csv") will not be able to find the file.

(2) View and set the working directory

# View the current working directory
getwd()
# [1] "C:/Users/Alice/Documents"

# Set the working directory (Use / or \\, Do not use \)
setwd("C:/Users/Alice/Projects/my_r_project")

(3) Recommended Approach: RStudio Project

Doing this manually setwd() is prone to errors (problems may occur if the path contains Chinese characters or spaces). We recommend using an RStudio Project:

graph LR
    A["File → New Project"] --> B["Select New Directory → New Project"]
    B --> C["Enter the project name r-tutorial"]
    C --> D["RStudio Create .Rproj Documents"]
    D --> E["Open later .Rproj<br/>Automatic Working Directory Setup"]

graph LR
    A["File → New Project"] --> B["Select New Directory → New Project"]
    B --> C["Enter the project name r-tutorial"]
    C --> D["RStudio Create .Rproj Documents"]
    D --> E["Open later .Rproj<br/>Automatic Working Directory Setup"]

7. Command-Line Execution: Rscript

If you want to run R scripts (for automation, scheduled tasks, or web backends) without opening RStudio, use Rscript.

(1) Create a script

Create a new file named hello.R in any location:

# hello.R
cat("Hello, World!\n")

(2) Running from the Command Line

Open the terminal (cmd / PowerShell on Windows, Terminal on macOS / Linux), and run the following command:

Rscript hello.R

Expected Output:

Hello, World!

8. Complete Example: Comprehensive Practice of Basic Syntax

Below is an example of a complete workflow that ties together all the concepts covered in this lesson.

▶ Example: Student Information Management Script

# ============================================
# Student Information Management Script
# File Name:student.R
# ============================================

# 1. Notes:Script Description
# Author:Alice
# Features:Define student information and output it
# Date:2026-07-02

# 2. Variable Assignment (Use <- No need =)
student_name <- "Alice"
student_age <- 20
student_score <- 95.5
is_passed <- TRUE

# 3. Use cat() Output (Recommendations, Flexible Assembly)
cat("=== Student Information ===\n")
cat("Name:", student_name, "\n")
cat("Age:", student_age, "\n")
cat("Score:", student_score, "\n")
cat("Did I pass?:", is_passed, "\n")

# 4. Use print() Debugging (with [1] Index)
print(student_name)
print(student_score)

# 5. Simple Calculations
final_score <- student_score * 0.6 + 100 * 0.4  # 60% End of Term + 40% Usually
cat("\nFinal Results(60% End of Term + 40% Usually):", round(final_score, 2), "\n")

# 6. Delete Variable(Use with caution)
rm(student_name, student_age, student_score, is_passed, final_score)

Expected Output:

=== Student Information ===
Name: Alice
Age: 20
Grades: 95.5
Did I pass?: TRUE
[1] "Alice"
[1] 95.5

Final Results(60% End of Term + 40% Usually): 97.3

(1) Procedure

❓ FAQ

📖 Summary

• In R, use # to write single-line comments (there is no /* */ syntax for block comments); use the RStudio shortcut Ctrl+Shift+C to comment out multiple lines at once
• Variable naming conventions: alphanumeric characters, underscores, and periods; cannot start with a number; case-sensitive; the snake_case style is recommended
• The preferred assignment operator is <- (R community convention); = is used when passing arguments to functions; <- must be surrounded by spaces (to avoid confusion with < -)
• Output functions: print() General (with [1] index), cat() Concatenation (recommended, flexible), message() Normal message, warning() Warning, stop() Error
• To view the working directory settings, use getwd() / setwd(); We strongly recommend using an RStudio project to avoid path issues.
• Use Rscript hello.R to run R scripts from the command line; suitable for automation, CI/CD, and Web API scenarios

📝 Exercises

1. Basic Exercise: Create a new script in RStudio and define five variables to store a piece of text (name), your age (age), your weight (weight, floating-point number), whether you are a student (is_student, boolean), and your city (city). Use cat() to concatenate them into the output: "I am [name], [age] years old, weigh [weight] kg, live in [city], and [is_student] am still in school." Take a screenshot after running the script and save it.

2. Basic Exercise: Create a new RStudio project named r-basics. Within the project, create a script named variables.R and define five variables of different types (numeric, character, logical, complex, and null NA). Use typeof() to check the type of each variable, and take a screenshot of the output for each of the five typeof() commands and save it.

3. Advanced Problem: Write a script that: ① uses paste() to concatenate "Hello" and "World" into "Hello-World" (sep = "-"); ② uses cat() to print the result; ③ uses nchar() to check the length of the concatenated string. After running the script, take a screenshot and save the console output.

4. Challenge: Write an R script that does the following: ① Use setwd() to change the working directory to tempdir() (the R temporary directory); ② Use getwd() to print the current working directory; ③ Use save.image() to save the workspace to my_workspace.RData; ④ Use rm(list = ls()) to clear all variables; ⑤ Use load("my_workspace.RData") to reload. Verify that all variables are still present after reloading. After running the script, take a screenshot and save the complete output for all 5 steps.

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