R is a way to talk to computers.
It helps us look at lots of facts. It can make pretty pictures too. This helps people learn new things. It is free for everyone to use. Do you like looking at pictures?
R is a way to talk to computers.
It helps people study lots of facts. It can make pretty pictures from those facts.
Two teachers started R to teach math. It was named after them. One teacher was named Ross. The other was named Robert.
People can add new tools to R. These tools are called packages. They help R do even more work.
There are many packages to use. Some help make maps or charts.
Many people work together to help R. They meet at big parties to talk.
It is a very helpful tool.
R is a programming language. People use it to study data. This means they look at many facts to find patterns. It is used for things like math and science.
Two professors, Ross Ihaka and Robert Gentleman, started R. They wanted to teach statistics at a university. The name R comes from their first names. It also follows an older language called S.
Users can add new tools to R. These tools are called packages. A package is a set of code and data. They help R do more jobs. Some packages help with making charts.
Others help with biology or finance. You can find many packages on a site called CRAN. CRAN has over 22,000 packages.
Many people help keep R working. The R Core Team maintains the main code. The R Foundation provides money to help. There are also big meetings for R users. One is called UseR!
These meetings let people talk about new ideas. R is free for everyone to use.
R is a special programming language used for math and science. People use it for statistical computing and data visualization. This means they use it to study patterns in information. It is very popular in fields like data science and bioinformatics. Researchers also use it for data mining and complex data analysis. Because it is free and open-source, anyone can use it.
How R works is quite interesting. The core language is built using C, Fortran, and R itself. It is an interpreted language with a command line interface. Users can also use graphical interfaces like RStudio or Jupyter. One great way to use R is through software packages. A package is a collection of reusable code and data. You install a package once using a command like install.packages("tidyverse"). Then, you use the library() function to load it.
The history of R began at the University of Auckland. Two professors, Ross Ihaka and Robert Gentleman, created it. They wanted a way to teach introductory statistics to students. R was inspired by an older language called S. It also used ideas from a language called Scheme. The name R comes from the authors' first names. It also shows that R is a successor to S. In 1993, they shared the first files on a website.
There are many important facts about the R community. A huge network called CRAN hosts the code and packages. Kurt Hornik and Friedrich Leisch founded CRAN in 1997. It started with only three mirror sites and twelve packages. Today, it has 90 mirrors and 22,390 contributed packages. The R Core Team maintains the main source code. The R Foundation provides financial support for the project.
R connects to many things you might already know. If you like making charts, R can make violin plots. It can also create complex math drawings like a Mandelbrot set.
Many people gather at big meetings to talk about R. One famous meeting is called UseR!, which happens every year. You can even follow new R news on social media. People often use the hashtag #rstats to find each other online.
R is a specialized programming language designed for statistical computing and data visualization. It allows users to perform complex mathematical tasks and create detailed charts to represent information. Because of these capabilities, R is widely used in many scientific fields. These include data science, bioinformatics, data mining, and general data analysis. R is free and open-source software. This means its source code is available for anyone to see and modify under the GNU General Public License.
The technical foundation of R is quite robust. The language is implemented primarily using C, Fortran, and the R language itself. It is an interpreted language, which means code is executed directly by a command line interface. While many users work through this text-based interface, others prefer graphical user interfaces (GUIs). Popular third-party applications include RStudio, which is an integrated development environment. Other options include Jupyter, which provides a notebook interface. For those using mobile devices, tools like Termux and Google Colab are also available.
One of the most powerful features of R is its system of packages. A package is a collection of reusable code, documentation, and sample data. These packages expand what the core language can do. There are base packages that come installed immediately. These provide the fundamental syntax, arithmetic, and basic statistical functions. Beyond the base, users can install specialized collections. For example, the tidyverse is a popular collection of packages. It helps users with visualizing, transforming, and modeling data. The tidyverse is designed to work with "tidy data." This refers to data organized into a two-dimensional table with single rows for observations and single columns for variables.
To use these tools, a user must first install them. For instance, one might use the command `install.packages("tidyverse")`. This installation only needs to happen once. After installation, the user must load the package to use its functions. This is done using the `library()` function, such as `library(tidyverse)`. To manage these vast amounts of code, the Comprehensive R Archive Network (CRAN) was established. Founded in 1997 by Kurt Hornik and Friedrich Leisch, CRAN hosts source code, executables, and user-created packages. CRAN has grown immensely since its start. It began with only three mirror sites and twelve packages. Today, it maintains 90 mirrors and hosts 22,390 contributed packages.
The history of R is tied to academic teaching. Professors Ross Ihaka and Robert Gentleman started the project at the University of Auckland. They originally intended it as a tool to teach introductory statistics. The language was inspired by the S programming language. In fact, most S programs can run in R without any changes. R also took ideas from a language called Scheme, specifically its lexical scoping. This allows for the use of local variables. The name "R" is a clever tribute. It represents both the successor to S and the shared first letter of the authors, Ross and Robert. In August 1993, the first binary files were posted to the StatLib archive. R later became a GNU project in December 1997. The version 1.0 was officially released on February 29, 2000.
Advanced users often utilize R for complex modeling and object-oriented programming. R supports two native frameworks for object-oriented programming: S3 and S4. The S3 system is more informal and uses a "class" attribute. The S4 system is more formal and supports multiple inheritance. R also excels at mathematical modeling. Users can create linear regression models to study relationships between variables. For example, the `lm()` function can generate a model from data. Users can then create diagnostic plots to inspect their results.
Recent updates have also improved how people write code. Since version 4.1.0, R includes a native pipe operator, `|>`. This allows users to chain functions together in a sequence. This makes code much easier to read compared to nested function calls.
Because R is so central to science, a large community supports it. There are three main groups that help maintain the software. The R Core Team was founded in 1997 to maintain the source code. The R Foundation for Statistical Computing was founded in April 2003 to provide financial support. Finally, the R Consortium is a Linux Foundation project that develops R infrastructure.
The community also gathers at many events. These include the annual UseR! conference and the SatRdays conferences. There are even groups like R-Ladies, which works to promote gender diversity. You can even see the power of R through visual math, such as the creation of a Mandelbrot set.
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