# How to do R programming The R Programming language is a **popular statistical programming language** used primarily in science and mathematics for statistical computing. R is an interesting language with some distinctive features. The language is quite fun once you get used to it. ## What sets R apart from other programming languages? R is not a general-purpose programming language like Java or Python. The language is intended for **statistical computing.** R has remained in the top 20 most popular programming languages for years despite some strong competition. R is special because it comes with the whole package. R programming usually takes place in an **interactive environment**, complete with read-eval-print loop (REPL) and integrated help. The open-source language is supported by a widely developed ecosystem. The community maintains the package repository “The Comprehensive R Archive Network” (CRAN). Data sets and scientific white papers on new approaches and packages are also continually being submitted. These features make R the perfect programming environment **for statistics and data science**. The interactive environment promotes research and fosters playful learning of both the language and the underlying mathematics. ### R is a statistical programming language used for data analysis R is a statistical programming language and concepts such as **normal distribution, statistical tests, models and regression** are commonly used. In addition to R, there are a number of comparable scientific languages, such as the commercial product Matlab and the more recent language Julia. Python has become another strong competitor in recent years. Unlike Python, R has native support for statistical programming. The key difference is how the language operates on values. **In R, you usually compute with multiple values at once**. This is a special feature in R, as almost all other languages use a single number as the simplest value. Let’s have a look at **R’s approach to data processing** with a simple example. Mathematical operations can be performed in every programming language. This is also the case in R. Let’s add two numbers: ```R # returns 15 10 + 5 ``` Nothing unusual so far. However, the same **addition operation can be applied to a list of numbers** in R. We can combine two numbers into a list and add a constant value: ```R # returns 15, 25 c(10, 20) + 5 ``` This may be **a surprising result** for seasoned programmers. Even a modern, dynamic language like Python does not facilitate this: ```R # throws an error [10, 20] + 5 ``` Two lists can also be added in R. In this case, the list elements are not combined into one list, rather the **appropriate mathematical operation is performed for each element**: ```R # returns 42, 69 c(40, 60) + c(2, 9) ``` A loop is required to process multiple elements of a list in older languages like Java or C++. This is because these languages separate single values, or scalars, from composite data structures, known as vectors. **In R, the vector is the basic unit**. A scalar operating as a one-element vector is unique to R programming. What makes statistics so special is that it makes mathematical precision more flexible. In statistics, you have to **calculate with uncertainties and imperfect data derived from reality**. Something can, of course, always go wrong. But fortunately, R is equipped to deal with error to a certain extent. The language can handle missing values without crashing a running script. Let’s look at an example of the language’s robustness. In any programming language, a crash can occur when a number is divided by zero. However, R is not affected by this. **Division by zero results in the value *Inf***, which can be easily filtered out of the data during a cleanup later on: ```R # list of divisors, containing zero divisors = c(2, 4, 0, 10) # returns 'c(50, 25, Inf, 10) ' quotients = 100 / divisors # filter out Inf; returns 'c(50, 25, 10)' cleaned_quotients = quotients[quotients != Inf] ``` ### R supports OOP and functional programming R makes programming extremely flexible. The language doesn’t fit clearly into the hierarchy of [programming paradigms](https://www.ionos.com/digitalguide/websites/web-development/programming-paradigms/ "Programming paradigms"). It is supported by an OOP system, but you won’t find the usual class definitions. Its primarily functional and imperative approaches are used on a daily basis. The functional features are strongly pronounced, and they are ideal for data processing. Similar to JavaScript, the object system’s flexibility is its main advantage. **The generic functions are comparable to Python**, in the sense that they can be applied to different types of objects. For example, the *length()* function exists in R programming, which is similar to *len()* in Python. ## How does R programming work? R programming specializes in data and statistics. In R, you need a data set to develop a solution to a problem. Unfortunately, this may not always exist at the time of development. This means that an R programming project usually begins with simulated data. Users write the code, test the functionality, and replace the test data with real data at a later point. ### How is R code executed? R is a dynamic, interpreted scripting language, similar to Ruby and Python. Unlike the [programming language C](https://www.ionos.com/digitalguide/websites/web-development/c-programming-language/ "C") there is **no separation of source code and executable code in R**. Development usually takes place interactively, whereby the interpreter is fed line by line with [source code](https://www.ionos.com/digitalguide/websites/web-development/source-code-explained-definition-examples/ "Source code"), which is executed immediately. Variables are created automatically when needed and names are bound at runtime. This kind of interactive and dynamic programming is like being **inside the running program**. Objects can be examined and modified, and new ideas can be tested immediately. The *help command* grants access to the syntax and functions documentation: ```R # view help for 'for' syntax help('for') # view help for 'c()' function help(c) ``` **Script files can be loaded dynamically** from the interpreter. The *source command* works in the same way as the shell command. The R source code file is read and fed into the running session: ```R source('path/to/file.r') ``` ### What is the syntax of the R programming language? The scripting language uses curly braces to delimit the bodies of functions and control statements, like in C and Java. In contrast to Python, **indenting code does not affect the function**. Comments start with a hash, like in Ruby and Python, and no semicolon is needed at the end of a statement. The language has some peculiarities, making it easy to recognize R code once you become more familiar with it. The equal sign and **two arrow-like operators** are used in R programming **for assignments**. This allows the assignment’s direction to be reversed: ```R # equivalent assignments age <- 42 'Jack' -> name person = c(age, name) ``` Another typical feature of R code is the **pseudo-object notation** following the pattern *object.method()*: ```R # test if argument is a number is.numeric(42) ``` The *is.numeric* function looks like a *numeric()* method, which belongs to an object named *is*. However, this is not the case. **In R programming, the dot is a regular character**. The function could be called *is\_numeric* instead of is *.numeric*. The concatenation function *c()* is used to **create ubiquitous vectors** in R programming: ```R people.ages <- c(42, 51, 69) ``` Applying the function to vectors will **merge them into a coherent vector**: ```R # yields 'c(1, 2, 3, 4)' c(c(1, 2), c(3, 4)) ``` Unlike most programming languages, **indexing a vector’s elements starts at 1 in R**. This takes some time to get used to, but it helps to avoid the dreaded off-by-one errors. The highest vector index corresponds to the vector’s length: ```R # create a vector of names people <- c('Jack', 'Jim', 'John') # access the first name people[1] == 'Jack' # access the last name people[length(people)] == 'John' ``` Similar to Python, R programming also uses slicing. A slice can be used to **index a vector’s subrange**. This is based on sequences, which are natively supported in R. Let’s create a sequence of numbers and select a slice: ```R # create vector of numbers between 42 and 69 nums = seq(42, 69) # equivalent assignment using sequence notation nums = 42:69 # using a sequence, slice elements 3 through 7 sliced = nums[3:7] ``` ### How do control structures work in R programming? Basic operations are defined for vectors in R programming. This means that **loops are not required**. Instead, an operation is performed on the entire vector, which modifies the individual elements. We square the first ten positive numbers without a loop: ```R nums <- seq(10) squares <- nums ** 2 squares[3] == 9 ``` The [for loop in R](https://www.ionos.com/digitalguide/websites/web-development/for-loop-r/) does not work the same way as for loops in C, Java or JavaScript. **There is no detour via a loop variable**. Iteration is performed directly over the elements, like in Python: ```R people = c('Jim', 'Jack', 'John') for (person in people) { print(paste('Here comes', person, sep = ' ')) } ``` The [if-else branching in R](https://www.ionos.com/digitalguide/websites/web-development/r-if-else/) exists as a basic control structure. However, this can be **replaced by filter functions or the logical indexing of vectors**. Let’s create a vector of ages and filter the data using two variables: over 18 and under 18. This can be done without a loop or branching: ```R # create 20 ages between 1 and 99 ages = as.integer(runif(20, 1, 99)) # filter adults adults = ages[ages > 18] # filter children children = ages[ages < 18] # make sure everyone is accounted for length(adults) + length(children) == length(ages) ``` The **same approach can be taken with control structures**: ```R # create 20 ages between 1 and 99 ages = as.integer(runif(20, 1, 99)) # start with empty vectors adults = c() children = c() # populate vectors for (age in ages) { if (age > 18) { adults = c(adults, age) } else { children = c(children, age) } } ``` ### How to get started with R programming To get started with R programming, you **just need a local R installation**. There are installers available for all major operating systems. A standard R installation includes a GUI interpreter with REPL, integrated help and an editor. For efficient coding, we recommend using an established [code editor](https://www.ionos.com/digitalguide/websites/web-development/code-editors/ "Code editors"). [RStudio](https://www.rstudio.com "RStudio: Professional open-source software for data science") is a great alternative to the R environment. ## Which projects is R suitable for? R programming is used mainly in science and research, for example, in bioinformatics and machine learning. However, the language is suitable for all **projects that use mathematical models or statistical modeling**. R does not have an advantage when it comes to processing text. This is Python’s area of expertise. Common **calculations and visualizations in spreadsheets can be replaced with R code**. Data and code are not mixed in the same cells, allowing for code to be written once and applied to multiple data sets. Furthermore, there is no danger of overwriting a cell’s formula when making manual changes. R is considered the gold standard for scientific publications. The **separation of code and data is what makes scientific reproducibility possible**. The mature ecosystem of tools and packages allows efficient publication pipelines to be created. Evaluations and visualizations are automatically generated from code and data and then integrated into high-quality LaTeX or RMarkdown documents. Tip [Buy webspace](https://www.ionos.com/hosting/webspace "Buy webspace at an affordable price from IONOS") at an affordable price from IONOS. It’s the perfect foundation for your website. This is a markdown version of: [https://www.ionos.com/digitalguide/websites/web-development/r-programming/](https://www.ionos.com/digitalguide/websites/web-development/r-programming/) for AI/LLM consumption.