R Programming Prerequisites

R is a programming language and software environment that was created by Statisticians to be primarily used for statistical data analysis and visualization purposes. This widely used programming language has applications in various professions, from data science to business to academia, for analyzing experiment results and extracting insights from vast datasets. You might worry that R programming will be too hard to learn. This guide will help you understand the best methods for learning R and what you should study first to make the learning process easier. This way, you’ll be successful however you choose to apply your new skills. 

What is R Programming?

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.

Read more about what R programming is and why you should learn it.

What Can You Do with R Programming?

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. 

Is R Programming Easy to Learn?

Most individuals consider R to be a relatively tricky programming language to learn. Its syntax is quite different from other programming languages, such as Python, which makes it harder to read. In addition, many of R’s core operations, like selecting, naming, and renaming variables, are more difficult for R users than in other languages. Even some Data Scientists with a background in programming find R difficult to master because of its extensive set of commands, numerous GUIs, and inconsistently named functions. Like any other skill, the more time you invest in learning R’s syntax and rules, the easier it will be to master this language. 

Another factor that makes R particularly difficult to learn is that its help files aren’t considered to be especially beginner-friendly. Although they are thorough and provide working examples, some R users find the explanations of different functions confusing, making it more challenging to find help with common R questions. Enrolling in Noble Desktop’s in-person or live online Data Analytics with R Bootcamp is a great way to learn more about working with R. This hands-on class covers R and RStudio basics, data wrangling, R graphics, and the Shiny app, among other relevant R topics.

What to Know Before Learning R Programming

Before learning R programming, it’s helpful to be familiar with some core statistical concepts, as well as the fields of data science and data analytics:

Statistics

Statistics is a branch of applied math in which quantitative data is collected, described, and analyzed to draw conclusions. This field relies on mathematical concepts like linear algebra, integral and differential calculus, and probability theory. Statisticians are often involved with drawing accurate conclusions based on small samples that can then be applied to larger groups or more general events. Because R was designed by and for Statisticians to aid with various statistical analytic tasks, the more familiar you are with core statistical concepts, the easier it likely will be to understand how to apply R to the statistical analysis process.

Basic Data Science Concepts

Data science is a broad field that incorporates various scientific processes, methods, and algorithms to locate and extract valuable insights from both structured and unstructured datasets. For those interested in working with R, having a basic understanding of data science and the five stages of the data science lifecycle can be hugely beneficial. This cycle involves capturing, maintaining, processing, analyzing, and communicating data so an organization can use the insights retrieved to make better, more informed decisions.

A great way to get started learning about data science is to watch Noble Desktop’s Data Science Webinar. This video provides a general overview of the field of data science, the tools most commonly used by Data Scientists, and the various applications of data science in business. Noble also offers more intensive data science instruction through the in-person or live online Data Science Certificate. Participants receive expert instruction on core data science concepts like programming fundamentals, machine learning concepts, and Python.

Data Analytics

Those who understand the field of data analytics will likely find it easier to learn R programming. Data Analytics involves finding and extracting information from vast datasets to provide an organization with actionable insights. Data Analysts are tasked with finding the necessary data to make reliable predictions about what may transpire in the future and providing insights that improve a company’s problem-solving or decision-making processes. Data analytics also entails creating data visualizations of data findings, which are shared with other stakeholders to communicate their insights clearly. Because R relies heavily on data analytics and visualization, it’s a good idea to revisit core data analysis concepts before studying R.

Noble Desktop’s in-person and live online Data Analytics Certificate is an excellent choice if you want to learn more about data analytics before learning R.

Learn R Programming with Hands-on Training at Noble Desktop

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.

Key Insights

• R is an open-source programming language created by Statisticians to perform statistical analysis on large datasets and create visualizations based on the data findings.
• Although the difficulty involved with learning R depends on several factors, most individuals consider this language to be challenging to learn due to its hard-to-read syntax, extensive set of commands, and inconsistently named functions.
• If you are interested in learning R, you may consider reviewing basic statistical concepts and data analytics practices and tools before getting started. 
• Enrolling in one of Noble Desktop’s in-person and live online R courses is a great way to learn more about R programming.