R — Data Analysis and Statistics

A data analysis environment that handles statistical computation and graph creation in a single language.

Basic Information

What is this program?

R is a scripting language and execution environment created for statistical computing and graph creation. It is designed to execute commands line by line so you can inspect data and immediately plot the results.

Because CRAN has a very large number of published packages, most data work—from statistical tests and time series analysis to machine learning and report generation—can be handled by combining packages.

What problems does it solve?

• Summarizes experimental and measurement data and performs statistical tests.
• Loads CSV/Excel data, cleans it, and visualizes it as graphs.
• Creates aggregations by multiple conditions and cross-tabulations for reports.
• Keeps repeated analysis procedures as scripts so the same results can be reproduced.

Key features

Data structures and data types

Provides built-in structures for handling data, including vectors, matrices, lists, and data frames.

• Use case: Used when loading a single CSV and viewing it split by condition.
• Result: Tabular data can be handled as-is, which shortens preprocessing code.
• Limitations: Large data is loaded into memory, so server memory limits must be considered.

Statistical functions and tests

Provides basic functions for everything from mean and variance to t-tests and regression analysis.

• Use case: Used when checking whether the difference between two groups is significant.
• Result: Immediately outputs the test statistic and p-value.
• Limitations: If the assumptions of the test method are not checked, the interpretation of the results can be incorrect.

Graphs and visualization

Creates static graphs with base graphics and the ggplot2 package.

• Use case: Used when creating trend charts and distribution plots for reports.
• Result: Can be saved as output devices (PNG, PDF, SVG) and inserted into documents.
• Limitations: The grammar of graphics (ggplot2) requires separate learning.

Package ecosystem (CRAN)

Packages are installed with install.packages() and loaded with library().

• Use case: Used when a particular analysis technique is needed and you search for and install a package.
• Result: Reuses proven implementations, greatly reducing analysis code.
• Limitations: Results may change when package versions change, so version recording is necessary.

Installation and execution

Ubuntu / Debian

Install the distribution repository version, or add the CRAN repository to install the latest version.

sudo apt update
sudo apt install r-base
# If you need the latest version, add the CRAN repository and then install

Windows / macOS

Download the installer from a CRAN mirror and install it.

Windows: base/R-4.6.1-win.exe install
macOS: R-4.6.1-arm64.pkg (Apple Silicon) install

Package installation

Install the analysis packages you need after starting R. Packages that require compilation may need development tools.

install.packages("ggplot2")
library(ggplot2)

Real-world usage examples

Read a CSV and check summary statistics

Loads a data file and checks its structure and summary values.

df <- read.csv("measurements.csv")
str(df)
summary(df)

Aggregation by condition and graphs

Calculates the mean by group and saves it as a graph.

agg <- aggregate(value ~ group, data = df, FUN = mean)
print(agg)
png("by_group.png", width = 800, height = 600)
boxplot(value ~ group, data = df)
dev.off()

Advantages and limitations

Advantages

• Statistical analysis and visualization are completed in one environment, reducing the need to switch between separate tools.
• The vast collection of CRAN packages lets you immediately use most analysis techniques.
• Because analysis can be saved as scripts, it is easy to reproduce and verify the process.
• It is free and the same code runs on both servers and personal computers.

Points to consider before use

• Because data is loaded into memory, a separate strategy is needed for large datasets.
• Without statistical knowledge, it is easy to misinterpret results.
• Execution speed is relatively slow compared to general-purpose programming languages.
• Results can change due to package version differences, so environment recording is necessary.

Comparison with similar programs

Official resources and further learning

• Official website — https://www.r-project.org/
• Official documentation / knowledge base — https://cran.r-project.org/manuals.html
• Official download — https://cran.r-project.org/mirrors.html
• Release notes — https://cran.r-project.org/bin/windows/base/NEWS.R-4.6.1.html
• Source / development resources — https://cran.r-project.org/sources.html

Version and supported environment information is based on the official site as of 2026-10-18. Installation commands may vary depending on your distribution and version, so please also check the official installation documentation.

Actual execution screen

The screen below is a synthetic example that assumes a situation where you check the version and calculation results on an operating server (not an actual execution result). The screen is a synthetic example and not an actual execution result.

!R version and mean calculation example

A server in actual operation

Source (HTML): https://games.aidebatehub.com/programs/apps/data/r.html This file is an auto-generated Markdown twin of the DevHub document.