If you're a business analyst, data scientist, or statistician, learning the R programming language is a great use of your time. R has become the lingua franca of data analysis, and its flexibility and power make it the tool of choice for researchers and professionals in many industries. Whether you're just starting out or are already familiar with R, this guide will lay out the benefits of learning this versatile language.
What Is R Language?
R is a programming language that serves as an excellent tool for programmers analyzing large data sets and coming to conclusions based on that data. It shouldn't be your first choice if you’re looking for programming languages for application or web development, but it has some specific and important uses.
What Is R Programming Language Used For?
R is used for statistical computing, data analysis, and making a graphical representation of the analyzed data. It can help data experts make sense of statistical data and mine information.
R was inspired by S (Scheme), a programming language that already implemented lexical scoping semantics. It was created by a group working at Bell Labs in the 1970s, including John Chambers, Judy Schilling, Jean McRae, Rick Becker, and Doug Dunn. It is an interpreted programming language that can be run without a compiler to transfer it into machine code and was created as another Lisp dialect.
Ross Ihaka and Robert Gentleman developed R in 1995 while working at the University of Auckland, New Zealand. The R project is named after their initials and is an homage to the name of S, which inspired the endeavor.
R is a free software environment available for use by anyone. Its packages fall under GNU General Public Licenses.
It complies with and runs on a wide variety of platforms. Windows binary versions are also available from the CRAN (Comprehensive R Archive Network) website, where the R development core team consistently updates it. Binary versions for Mac OS X are available from CRAN, or using Homebrew or MacPorts package managers. You can also download R for Linux, as well as systems like Ubuntu, Debian, and Fedora/Redhat.
While it was originally developed by academics at universities, many commercial statistical packages now interface with R. These include SAS, Stata, IBM SPSS Statistics, Matlab, Microsoft Excel, and many others.
How To Get Started With R Coding Language
If you're looking to get started with R, the first question you might be asking is: what do I need to learn first? The answer, fortunately, is not much. Unlike some other programming languages, R is designed to be easy to learn for beginners. That means that you won't need to spend hours memorizing syntax before you can start writing code.
However, there are still a few basic concepts you'll need to understand before you can start coding in R. In particular, you'll need to be familiar with the following:
- variables and data types
- functions
- loops
- conditional statements
Once you've got a handle on these concepts, you'll be ready to start writing your own R programming code. And if you're looking for a little extra help, there are plenty of resources available online, including tutorials, cheatsheets, and forums where you can ask questions and get help from other R users.
Other Languages Useful for R Programming
There are also a number of other programming languages that can be helpful when learning R.
For example, many people find it useful to learn the basics of SQL before diving into the R programming language. This is because SQL can be used to easily manipulate data frames (the data structures used in R).
Although it’s not strictly necessary, knowing a bit of HTML/CSS and JavaScript can also be helpful when creating dynamic web applications with R (using the Shiny package).
Ultimately, the best way to learn R (or any other programming language) is simply by doing it. There are a number of resources available online (such as Codecademy) that can help you get started.
Additionally, there are many books and tutorials specifically for learning the R programming language. However, the most important thing is simply to begin writing code and working with data. The more you do it, the easier it will become.
The Basics of the R Statistical Software Language
The basics of the R programming language include working with vectors, matrices, and data frames, as well as plotting data. R also has a rich set of packages for machine learning, text mining, and statistical analysis. With R, you can easily manipulate and visualize data to gain insights into complex problems.
R’s environment is perfect for statistical computing and graphics; the R language is widely used for statistics and data mining. R developers can code statistical software and efficiently analyze data.
The basics of R include object-oriented programming, lexical scoping, vectorization, assignment using bind operators, garbage collection, and the S3/S4 object system. The R programming language has support for procedural programming with functions and recursion, as well as support for object-oriented programming with generic functions. This gives users flexibility when choosing a programming style.
With R, variables in a function can be assigned values from the surrounding environment, and these values will persist after the function exits.
R Packages
Using R is even more convenient when you get one of the packages that exist for the language. These contain organized sets of code, data, and documentation, while a CRAN serves as a centralized download repository.
The tidyverse is a must-use collection of R packages designed for data science that implement a shared design, grammar, design approach, and data structure. It consists of eight packages: dplyr, ggplot2, tidyr, readr, purrr, tibble, forcats, and stringr. Commonalities between these packages make them easy to learn together and allow them to work seamlessly with one another.
In addition to the tidyverse packages, there are many others that are perfect for R data science, such as htmltab (designed for reading HTML tables into data frames), broom (tidying models created with many different modeling functions), and modelr (for creating tidy pipelines for model building). Other commonly used packages are reshape2 (for working with matrix-like data structures), plyr (to split up, apply functions to, combine, or manipulate data), and ggvis (an interactive grammar of graphics).
What Can You Do With R?
The R programming language enables you to perform statistical analysis, general data analysis, data visualization, and predictive modeling. As data visualization developers are in high demand, it can be an exciting proposition to learn R and start a career in the IT industry.
Using R, you can create graphs, perform statistical analysis and modeling, and organize and manipulate data. R is also versatile, as it can be easily integrated with other languages and tools, such as Python and SQL. You can import data from a variety of sources, including Excel spreadsheets, text files, and databases. As a result, R can be used for a wide variety of data science and statistical computing tasks.
Once your data is in R, you can manipulate it to extract meaning and generate insights. For example, you can explore relationships between variables, identify patterns, and build models to make predictions.
In short, R computer language is an essential tool for data analysts who want to make sense of data. So whether you're a statistician, data analyst, business intelligence analyst, or any other data science professional, learning R is a worthwhile investment of your time.
Benefits of Programming in R
R has seen explosive growth in recent years; it has quickly become the language of choice for anyone wanting to make sense of large amounts of data. While R is not as widely known as languages like Java or Python, it offers a number of advantages that make it worth learning if you’re working in data analysis or data science.
R software has a large and active community of users, which means that there are always new packages and features being developed. For anyone looking to get started in data science, learning R is a great choice.
Where To Learn Programming with R
R’s large community of data analysts is particularly helpful, as users gladly share code and contribute to open-source packages. This makes it easy to find help when needed and keeps the language up-to-date with the latest data science methods.
Additionally, R is cross-platform, meaning it can be installed on Windows, Mac, and Linux machines. For these reasons, the R language is an excellent choice for those looking to learn the basics of programming or further specialize in R analytics.
There are a few different places you can go to get started with the basics of R. One option is to sign up for an online course taught by experienced data scientists. These courses typically offer video lessons and interactive exercises that will help you learn the basics of coding in R. Another option is to purchase a textbook that will provide you with step-by-step instructions for completing various tasks - such as data visualization - in R.
Finally, there are plenty of free online resources compiled by some of the best data analysts, such as blog posts, tutorials, and forums dedicated to R programming. By taking advantage of all of these different learning opportunities, you'll be well on your way to becoming an expert R programmer in no time!
Final Thoughts
Programming languages such as R are not as widely known as some others that are used for web or application development. Still, R has a growing community of users and developers. If you're interested in learning more about R, there are a number of resources available online.
The official R website provides a wealth of information on the language, including tutorials, documentation, and user forums. There are also a number of books available on R, such as "An Introduction to R" by Alex Douglas, Francesca Mancini, Deon Roos, David Lusseau, and Ana Couto.
Whatever your level of expertise, there are plenty of resources and courses offering an introduction to R programming, and these can help you learn more about this unique language.
