Introduction to R: What Is the R Statistical Programming Language?

R is the world’s most popular language for statistical computing and graphics—it makes data analysis and visualization simple and professional.

This tutorial is designed for beginners with no prior experience, starting with "What is R?" and progressing all the way to the point where you can complete a full data analysis project using R. R is the de facto standard in academic research, data science, and statistical modeling, and is used by over 2.5 million data scientists worldwide.

1. What You'll Learn

• What is R, and what can it do?
• Differences Between R and Python and Their Appropriate Use Cases
• The History and Design Philosophy of R
• Installing R on Windows, macOS, and Linux
• Installing and Using the RStudio IDE
• Write and run your first R script
• The R package ecosystem (CRAN and tidyverse)

2. A Data Analyst’s True Story

(1) The Agony: Excel Can't Handle It Anymore

Alice is a market analyst who recently received an urgent assignment:

"Compile the @,000 rows of sales data from the past 12 months into a 6-page trend report covering different dimensions; the manager needs to see it tomorrow morning."

He opened Excel to get started, only to find that:

• @,000 rows of data It takes 5 minutes just to open the Excel file.
• I want to create 6 charts with different dimensions, but repeatedly copying and pasting + using pivot tables would take 3 hours.
• To calculate a summary based on the three dimensions of "region, month, and product," you need to use a 3-level nested VLOOKUP.
• Excel crashed twice in the middle of the task, and he almost smashed his computer.

How did he manage to do it in the end? He switched to R.

(2) Solution using R

For the same task, Alice writes the following in R:

# Load the entire data analysis toolkit with a single line of code
library(tidyverse)

# Read in a single line of code @,000 rows ofData(5 Done in a second)
sales <- read_csv("sales_2024.csv")

# Draw with one line of code 6 Post a picture
sales |>
  group_by(region, month) |>
  summarise(total = sum(amount)) |>
  ggplot(aes(x = month, y = total, color = region)) +
  geom_line() +
  facet_wrap(~ product) +
  labs(title = "2024 Annual Sales Trends(By Region/Products)")

Total time: 5 minutes—5 seconds to read the data, 1 second to aggregate it, 3 seconds to create 6 charts, and the rest of the time spent polishing the titles.

That’s the beauty of R: performing the most complex data analysis with the least amount of code. Its design philosophy is “to let statisticians focus on thinking, rather than struggling with tools.”

3. What is R?

R is a programming language for statistical computing and graphics that was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand.

(1) The Three Main Roles of R

(2) The Core Advantages of R

• The most comprehensive collection of statistical methods: From basic t-tests to cutting-edge Bayesian inference, R has ready-to-use packages for everything
• Leading in Visualization: ggplot2 is the gold standard in data visualization and has been widely adopted by Python and JavaScript.
• Academic Standards: 90% of journal articles in statistics, medicine, and economics use R
• Rich package ecosystem: Over 20,000 peer-reviewed open-source packages on CRAN
• Reproducibility: R Markdown brings together "code, data, and paper" into a unified whole

4. Differences Between R and Python

Many beginners ask, "Should I learn R or Python?" The answer is to learn both—but for different purposes.

(1) A Single Chart to Understand the Differences in Positioning

graph TB
    subgraph Division of Labor Among Programming Languages
        A[R<br/>Statistical Analysis<br/>Academic Research<br/>Data Visualization] 
        B[Python<br/>Web Development<br/>Machine Learning<br/>Automation Engineering]
        C[SQL<br/>Database Query]
        D[JavaScript<br/>Front-End Interaction]
    end
    
    style A fill:#e1f5ff
    style B fill:#fff4e1
    style C fill:#f0e1ff
    style D fill:#e1ffe1

graph TB
    subgraph Division of Labor Among Programming Languages
        A[R<br/>Statistical Analysis<br/>Academic Research<br/>Data Visualization] 
        B[Python<br/>Web Development<br/>Machine Learning<br/>Automation Engineering]
        C[SQL<br/>Database Query]
        D[JavaScript<br/>Front-End Interaction]
    end
    
    style A fill:#e1f5ff
    style B fill:#fff4e1
    style C fill:#f0e1ff
    style D fill:#e1ffe1

(2) Six-Dimensional Comparison Table

(3) Brief Conclusion

• Doing statistics, creating graphs, and writing papers → Use R
• Building products, developing websites, and working with AI → Use Python
• Data processing → Either one works, but R is more "authentic" in the field of data science

5. The Birth of R

(1) A Language with a Story

R wasn't designed out of thin air; it has a clear evolutionary path:

• 1976: Bell Labs developed the S language (designed specifically for statistical analysis)
• 1993: Ross Ihaka and Robert Gentleman designed the R language based on S
• Origin of the Name: Derived from the first letters of the two authors' names (Ross + Robert), and also pays homage to the S language
• 2000: R 1.0 was released, marking R's entry into its mature phase
• 2015: R 3.0 was released, bringing significant performance improvements
• 2024: R 4.4.x (current mainstream version)

(2) Why has R lasted 30 years?

R isn't a "perfect" language (its syntax has its quirks, and the IDE is slow to start up), but the reason it has survived for 30 years is:

• What statisticians actually use (not "used just for the sake of it")
• Package authors are the users (When they encounter a problem, they write a package; CRAN has become a "practical knowledge base")
• Barriers have been erected in the academic community (journals require R code that can be reproduced)

6. Install R (R 4.x main program)

R is open-source and free; you can download the installation package from the CRAN website.

(1) Installation Instructions for Each Platform

(2) Linux Installation Commands

# Ubuntu/Debian User:Execute the following commands one by one in the terminal
sudo apt update -qq
sudo apt install --no-install-recommends software-properties-common dirmngr
wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc
sudo add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/"
sudo apt install -y r-base

(3) Verify the installation

Once the installation is complete, open the system terminal and enter:

R --version

Expected Output:

R version 4.4.1 (2024-06-14) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

If you see the R version number, it means the main program has been installed successfully.

7. Install RStudio (Recommended IDE)

R comes with a basic graphical user interface, but 99% of R users use RStudio—an R-specific IDE developed by Posit, Inc., and the standard tool for R users worldwide.

(1) Download RStudio

Visit the Posit official website to download the free version of RStudio Desktop (available for Windows, macOS, and Linux; approximately 200 MB).

(2) RStudio Four-Pane View

When you open RStudio for the first time, you'll see four panes:

graph TB
    A["Source Editor<br/>Source Editor<br/>Write R Scripts and Documentation"] 
    B["Console<br/>Console<br/>Execute Code,Display Results"]
    C["Environment/History<br/>Environment/History<br/>View Variables,Historical Commands"]
    D["Documents/Charts/Packages/Help<br/>Files/Plots/Packages/Help"]
    
    A -- "Ctrl+Enter<br/>Run the selected row" --> B
    B -- "Assignment Generation" --> C
    B -- "plot Drawing" --> D
    C -- "Click on the variable name to view" --> D
    
    style A fill:#fff3cd
    style B fill:#d4edda
    style C fill:#cce5ff
    style D fill:#f8d7da

graph TB
    A["Source Editor<br/>Source Editor<br/>Write R Scripts and Documentation"] 
    B["Console<br/>Console<br/>Execute Code,Display Results"]
    C["Environment/History<br/>Environment/History<br/>View Variables,Historical Commands"]
    D["Documents/Charts/Packages/Help<br/>Files/Plots/Packages/Help"]
    
    A -- "Ctrl+Enter<br/>Run the selected row" --> B
    B -- "Assignment Generation" --> C
    B -- "plot Drawing" --> D
    C -- "Click on the variable name to view" --> D
    
    style A fill:#fff3cd
    style B fill:#d4edda
    style C fill:#cce5ff
    style D fill:#f8d7da

(3) Four-Pane Menu

8. The First R Script

Now let’s write our first R script. The complete workflow is as follows:

▶ Example: Hello World + Simple Calculations

# ============================================
# My First R Screenplay
# File Name:hello.R
# ============================================

# 1. Comments are marked with # Introduction,Will not be executed
# 2. Used for variable assignment <-
name <- "World"

# 3. Use cat() Output (cat = concatenate, join)
cat("Hello,", name, "!\n")

# 4. R It's a calculator.(Enter the expression directly)
1 + 1
2 * 3
sqrt(144)  # Square Root = 12
log(100)   # Natural Logarithm

# 5. Performing Calculations Using Variables
pi_value <- 3.14159
radius <- 5
area <- pi_value * radius ^ 2
cat("The radius is", radius, "The area of the circle is approximately equal to", round(area, 2), "\n")

Expected Output:

Hello, World !
[1] 2
[1] 6
[1] 12
[1] 4.60517
The radius is 5 The area of the circle is approximately equal to 78.54

(1) Procedure

9. The R Package Ecosystem

R’s greatest strength is its package ecosystem—more than 20,000 open-source packages contributed by statisticians worldwide, covering virtually all statistical methods and data science scenarios.

(1) The Three Essentials of Package Management

# 1. Installation Package (Download from CRAN Mirror, Just do it once)
install.packages("dplyr")

# 2. Load Package(It must be loaded once for each new session.)
library(dplyr)

# 3. View Installed Packages
installed.packages()[, "Package"]

(2) tidyverse: The "Five-Piece Set" for Data Science in R

tidyverse is a collection of "tidy" packages led by Hadley Wickham (the author of ggplot2) and is the de facto standard for modern R data science:

# One-time installation 8 A core package(dplyr/ggplot2/readr/tidyr/stringr/purrr/tibble/readxl)
install.packages("tidyverse")

❓ FAQ

📖 Summary

• R is a statistical computing and graphics language created in 1993, positioned as "the statistician's workbench + the data scientist's paintbrush + the standard for reproducible research."
• Key differences between R and Python: R is geared toward statistics, academia, and visualization, while Python is geared toward engineering, AI, and general-purpose development
• Installation order: First install the R main program (downloaded from CRAN), then install the RStudio IDE (from the Posit website)
• RStudio four-pane layout: Source Editor (top left) → Console (bottom left) → Environment/History (top right) → Files/Plots/Packages/Help (bottom right)
• First script: <- Assignment + cat() Output + Comment starting with #
• The tidyverse 5-piece suite (dplyr/ggplot2/readr/tidyr/stringr) is at the core of R for data science

📝 Exercises

1. Basic Exercise: Visit the CRAN website, find the latest R installation package for your operating system, and note down the current version number. Install the R program and RStudio, then take a screenshot of RStudio’s four-pane interface and save it.

2. Advanced Exercise: Create a new script in RStudio and write a "self-introduction" program: ① Define 3 variables (name name, age age, city city); ② Use cat() to construct a sentence and output "My name is [Name], I am [Age] years old, and I am from [City]"; ③ Use # to write 2–3 lines of comments explaining the code. After running the script, take a screenshot and save the console output.

3. Challenge: Research one R package that interests you the most (recommended: ggplot2 / dplyr / shiny / rmarkdown / forecast), and write a paragraph (about 200 characters) describing its purpose, typical use cases, and a real-world example (which you can find on the RStudio website or CRAN Task Views). Save your research findings in a document.

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