Discover the world of R programming, a critical tool for statisticians and data analysts. Learn about its applications, careers that utilize it, how to learn it, and the salaries associated with these careers.
R is a programming language used for graphics and statistical computing tasks. This open-source language was released in 1993 and has become a widely popular software environment and tool for Statisticians and others who regularly work with data. If you’ve always wanted to learn R programming but can’t figure out how to get started, this guide is for you. Here, you’ll learn more about the various ways to study R, free resources to take advantage of, and the types of careers that commonly use this programming language.
R is a programming language that statisticians created for statistical data analytics. This popular language has a range of applications for performing statistical computing and creating data visualizations. It is often used by Data Scientists, Business Analysts, Data Analysts, and those working in academia or science for tasks specifically involving statistical analysis. R is currently available for free and can run on Windows and Mac OS, as well as a variety of UNIX platforms and related systems.
R provides users with a range of graphical and statistical techniques, such as time-series analysis, clustering, classification, and linear and nonlinear modeling. One of the benefits of working with R is that it simplifies the process of creating publication-quality plots, especially those that incorporate formulas or mathematical symbols. This versatile language includes a fully integrated suite of software tools, such as a data storage and handling facility, operations for performing calculations on arrays, an extensive, integrated set of data analytics tools, and graphical tools designed to analyze and visualize data.
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R was created to primarily help with graphics and statistical computations. This language can accomplish various tasks, from data storage to data analysis to generating statistical models. Of all the available programming languages, R is considered to be the one with the most tools devoted exclusively to statistics. This language can aid with descriptive statistics tasks, like calculating standard deviations and designing models for mapping linear regression.
One of R’s most useful features is its ability to help users create customized data visualizations and dashboards. Some consider R’s ggplot2 package the best data visualization tool available. This package allows users to draw nearly any plot they can conceive. In addition, those who wish to take their data visualizations in R to the next level can combine ggplot2’s syntax with Plotly’s interactive features to make dashboards that are as engaging as they are interactive.
R draws from various machine learning tools so that users can make accurate, data-driven predictions. Users can select from an array of machine learning models, which have applications for creating predictive models, such as movie recommendation systems and churn models. In addition, R users can automate reporting by using R Markdown documents. R Markdown offers a straightforward, accessible syntax to generate various reports, such as presentations, books, or other written documents. This helps R users easily communicate data analysis results with others.
Because of the massive volume of data being created each day, those with the skills to analyze and visualize it are in demand in many professions and often receive high salaries for their skills. Since R is a highly versatile programming language, it is used across many industries and professions, such as in financial services, academia, retail, healthcare, manufacturing, and data journalism. It is also a widely used language across data science and analytics fields.
R Programmers are involved in designing the tools used to streamline the data analytics process. They work with data structures like data frames, vectors, and matrices to facilitate fast and efficient data analysis. Data Analysts work with R to locate, retrieve, and deliver data insights. Data Scientists commonly use R to create and implement the layout of a large dataset, as well as for tasks such as data mining, data modeling, research, and forecasting.
In addition to Data Scientists and Data Analysts, Data Architects also work with R when they perform data warehousing, data modeling, or system development. Geo Statisticians use R to examine both spatial and spatiotemporal datasets. Those working as Quantitative Analysts sometimes use R to ensure clients’ portfolios are risk-balanced, as well as to apply mathematical models to identify new opportunities and evaluate asset prices. Because of R’s data visualization capabilities, Data Visualization Analysts also use this language when they create and share interactive dashboards that are intended to help policymakers make more informed decisions.
Learning to program with R has many benefits and applications across data-related industries and professions. This language is free and open source, which makes it widely accessible. Because it’s available under the General Public License, it has no license restrictions; users can modify code as necessary. Since this language can run on various operating systems, it performs seamlessly, whether you’re using a Windows, Mac, or Linux-based system. In addition, R offers an array of built-in functions and more than 10,000 packages, which can help with data manipulation, statistical modeling, machine learning, and data visualization, among others.
Another perk of working with R is that it has a large community that can assist as necessary with questions and other R-related topics. This means that R users can seek advice from those who have completed projects like the one they are working on or collaborate with others. There are even data science contests available to test users’ R skills. For those working with data visualization, R offers packages like plotly, ggvis, and ggplot2, which are great resources for designing print-quality graphs. R’s package, Shiny, allows users to create their own dashboards and interactive web pages right from the R Console. Shiny web apps can then be hosted on any cloud service, like AWS.
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If you want to study R programming, the good news is that excellent study options are available online and in person. Learning R in the in-person environment is a great way for extroverted individuals to master core programming concepts. Classroom study provides access to a computer lab with the most up-to-date software and offers a way for students to connect with others on the same learning journey and receive guidance from an expert instructor. Live online classwork offers the same components as in-person programs, such as the ability to connect with an instructor in real-time and the added perk of allowing students to complete all coursework remotely from the comfort of their home or office. You can browse nearby in-person R classes to find one close to home or search specifically for live online R classes that you can complete from anywhere.
Another way to study R is to enroll in self-paced R courses. Top educational providers like Codeacademy, Coursera, and edX offer on-demand R lessons and tutorials, which can help students study this programming language at their own pace and from their preferred location. Unlike in-person and live online study, asynchronous R classwork does not entail meeting at a regularly scheduled time to complete lessons. Instead, students work with pre-recorded videos, tutorials, and other learning materials, to gain an overview of core R concepts. Self-paced learning doesn’t allow students to get support when they have questions, so this method is best for beginners interested in studying the basics of programming with R. To master more advanced programming concepts, it may be necessary to enroll in a course that provides real-time access to an instructor.
Noble Desktop also offers an Intro to Data Science seminar for those interested in an overview of the field of data science. This video briefly introduces data science and discusses the different tools Data Scientists use. It also contains information on how professionals can use data science in the business sector. Those interested in only studying R programming can browse nearby R classes to find a learning option most suited to their learning needs.
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Are you looking instead for an overview of R programming before diving into an entire course? If this sounds like you, you can begin learning about R programming and other core data science concepts for free online. In this free Intro to Data Science seminar offered by Noble Desktop, you’ll receive an 80-minute overview of how Python is used in data science. This webinar covers the advantages of working with Python in data science and an introduction to the field of data science. This seminar is a great way to learn about data science before enrolling in a class or certificate program.
Other top educational providers also provide free beginner-friendly R materials. Coursera’s R Programming is a fully online, intermediate-level R class that spans 57 hours. Participants learn how R is used for data analysis and different practical issues that can occur in statistical computing, such as using R loop functions and debugging tools. Udemy’s R Basics: R Programming Language Introduction is a free, beginner-friendly class in which participants study how to navigate the RStudio interface, create basic graphs, and execute common commands using R. Udemy also offers a business-specific R course: learn R for Business Analytics. Those enrolled receive instruction on how to leverage R for business analytics. Topics covered include leading packages into R, importing data into R, and running various macros with this programming language.
Read more about free R programming videos and online tutorials.
Because R is offered as a free software environment for graphical and statistical computing tasks, downloading and using this language does not require any additional costs. R can run on various UNIX platforms, as well as macOS and Windows. If you want to download R, you can do so directly from The R Project for Statistical Computing’s website. You will need to select your preferred CRAN mirror before downloading.
If you’re interested in learning R, there are a few prerequisites to consider studying first that can help you acquire this programming language easier and faster. Because R is often used for statistical analysis, it’s essential to have a strong background in mathematics and statistics. In addition, since R also has applications for data visualization, it’s helpful to be familiar with basic visualization options, such as working with plots and graphs. Some people who learn R also find it helpful to understand fundamental analytics skills and practices so that it will be easier to spot and use the patterns that emerge in data. In addition, you may consider learning basic programming concepts before studying R.
R is known to be challenging to learn for most people. Because its syntax is so different from most other programming languages like Python, it can be hard to read R. In addition, core operations, such as naming, selecting, and renaming variables, tend to be more challenging for most R users than in other languages. For those who have a background in other programming languages or have worked previously in the data sciences, it may be easier to learn R than those who are novices to coding or this field. However, some Data Scientists struggle using R due to its numerous GUIs, extensive commands, and inconsistent function names. Like any skill, the more time you spend becoming familiar with R’s rules, the easier it will be to work with this language.
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Learning R is considered one of the more challenging programming languages to master. This is because its syntax is quite different from other coding languages. In addition to R’s syntax being challenging for some to read, standard operations like naming or selecting variables are more demanding in R than in other languages. R users sometimes struggle to learn this language thoroughly due to the number of commands available, misleading parameter or function names, inconsistent syntax, and the lack of beginner-friendly help files.
One of the programming languages that’s often compared to R is Python. Just like R, Python can be downloaded and used for free. Both also have many applications in data science, machine learning, and data analytics. While these languages can create similar end results, they have several significant differences. Whereas R focuses mainly on the statistical element of a given project, Python is a general-purpose language with applications for bringing a project into a production environment and tends to be more flexible when performing tasks like data analysis.
Because Python was created to be used for software development, those with a background in C++ or Java likely will be able to learn Python faster than they would pick up R. On the other hand, it may be easier for individuals who have studied statistics to learn R. Since Python has so many libraries, it can be challenging to become familiar with them. R, however, has fewer libraries, making this language easier to work with. Generally speaking, Python’s syntax is easier to read, and some users find it a more straightforward language to learn than R.
Noble Desktop’s How to Learn Data Science seminar provides a comprehensive overview of this field for those interested in learning more about data science. Information is available about the benefits of learning data science, the career options available for those with a background in this field, and in-person and live online training options for individuals interested in learning more. Interested learners can also learn about coding by reading Noble’s Learn Coding page. This article provides a general overview of what coding is, what can be done with it, the careers that require a background in coding, and how to learn more about it.
Deciding the best learning format for your R studies depends on several variables. Ultimately, the most important consideration is what you will use this skill for. For those new to R programming or programming in general, free self-paced introductory classes, webinars, or other online materials are a good starting point. Not only are these a cost-effective method for learning R, but they also allow the flexibility of balancing family and work commitments with learning since they’re pre-recorded. Noble Desktop’s Intro to Data Science webinar is an excellent resource for learners who want to explore the field of data science. This webinar also provides content on open-source software, the core tools Data Scientists use, and why Python has become the most popular language for working with data.
If you’re interested in a more thorough and detailed understanding of R programming to grow in your current career or plug a skill gap, you may consider Noble’s in-person or live online Data Analytics with R Bootcamp. In the small class setting, participants are taught by an expert instructor how to use R to transform data into meaningful, visible measures. Students explore R Studio, looping, conditionals, data wrangling, and the Shiny app. All participants can choose whether they prefer to study in person in Manhattan or complete the bootcamp remotely in the live online environment. Both options provide real-time support so students can ask questions and receive guidance along their learning path.
A more intensive course of study might be a better match for those interested in mastering R programming to begin a new career or pivot to a new industry. Noble currently has an in-person or live online Data Science Certificate that provides hands-on training to prepare you to become a Data Scientist. Participants in this program work with industry experts to learn how to use Python for data analysis, construct machine learning models, and use SQL to query databases. All students have the option of a free retake to revisit course material and have access to one-on-one mentoring to help them learn more complicated data science skills.
Noble Desktop has several excellent learning options for those new to R and courses for more advanced programmers interested in mastering complex R skills. Noble’s Data Analytics with R Bootcamp is an immersive class designed to take participants from the basics of coding to a portfolio showcasing your experience working with R. Those enrolled receive expert instruction and can retake the class for up to one year to brush up on course materials.
Noble also offers a variety of in-person and live online coding classes that prepare participants to become Data Scientists or Web Developers. Courses are available in popular programming languages such as JavaScript and Python, as well as front end and full stack web development. Certificates are also offered in software engineering and data science. For students seeking instruction about using programming for finance technology, Noble currently has a FinTech bootcamp available as well, which includes one-on-one mentoring for all participants.
In addition to the variety of programming courses Noble teaches, this top educational provider also has several in-person and live online Data Analytics classes. Noble’s Data Analytics Technologies Bootcamp is a beginner-friendly course that prepares students to work with core data analytics tools like SQL, Excel, and Tableau. In addition, a certificate in data analytics is also available for those interested in becoming a Business Analyst or Data Analyst. This rigorous learning opportunity prepares students to perform data analysis, statistical analysis, and data visualization, as well as how to use relational databases. All students receive one-on-one mentoring to help with their learning process.
Learn to visualize and analyze data using the R programming language in this comprehensive data analytics bootcamp. Explore R basics, data wrangling, and graphics, and create interactive Shiny apps for various projects.
Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.