Known as one of the most popular programming languages across the globe, R is a go-to for data and computer scientists alike. Primarily used for statistical analysis and academic research, R is considered to be a high-level programming language as well as one of the easiest programming languages to learn, making it a go-to for beginners and more experienced coders. When analyzing information and data, R and its accompanying packages, programs, and products have several unique features which make it an excellent choice for data science projects across industries. There are many reasons why learning R is essential for students and professionals interested in the field or industry of data science.

Background and Interpretation of R

Derived from the S Programming language, the R programming language is one of many productions which came from Bell Labs during the early to mid-1970s. As part of the GNU collection of software, R has become a programming language that is especially useful for statisticians, researchers, engineers, and practitioners that require a language that can handle advanced data analyses, such as regressions and statistical modeling. With a syntax that is familiar to those with a background in statistical learning, R is also well-known within the worlds of machine learning and artificial intelligence.

R has become one of the most popular programming languages for data scientists and is regularly cited (in conjunction with Python) as an essential skill for data science students and professionals. Statistically speaking (no pun intended), positions that ask for knowledge of R have grown considerably, and R is a general requirement for careers within any industry that rely on statistical modeling and analysis. Knowledge of R is commonly required for gaining employment opportunities within information technology, as well as data science and analysis. With that being said, knowledge of R does not just end with the programming language, and data science students and professionals also find use in learning the products, packages, and programming possibilities of this intuitive language.

Open-Source Programming and Products

As an open-source programming language, there are several products that are commonly used when working with R. R Studio is one of the most popular products for R users, and there are multiple data science tools that can be used with RStudio. Of these products, RStudio Workbench, RStudio Connect, and RStudio Package Manager are the most popular offerings which can be used within the suite. RStudio Workbench is especially useful for data science teams that want to collaborate and work on multiple projects, or in several programming languages, at the same time. Products like RStudio Connect then allow data science professionals to share their insights and findings with the audience(s) of their choice.

The R Studio products are also able to be combined within a portal, making these products an excellent add-on for a company or research team. In addition, R is compatible with a variety of products that can be used for data science projects and database management, such as Microsoft R Open and MRAN. While some of the R products work well for research teams and corporations, other products are more suited for a single user or data science project. It should also be noted that while R is a free and open-source programming language, not all R-related products can be downloaded for free or have a high level of accessibility when using a free version of the software. It is important to research the costs and benefits of using different R products for data science.

Data Science Specific Packages and Libraries

There are several packages and programs that can be used with R that offer resources for data science students and professionals. Within the realm of programming, packages and libraries make it easier to write programs and code by providing user-created files which streamline the process of collecting, organizing, and analyzing data. The amount of open-source packages and libraries that can be found online is also indicative of the power and productivity of the R community. If you are new to using R for data science there is always a variety of helpful resources and individuals that you can find online to assist you with your data analysis problems and projects.

There are several packages and libraries which are used with the R programming language, but the three most popular are the tidyverse, CRAN repository, and GitHub. The tidyverse is a curation of R packages that are used for data science, known for the inclusion of dplyr, which can be used for data manipulation. With multiple links to the community of R users, the tidyverse is also known for “Tidy Tuesdays” which engage users in the R for Data Science Learning Community around exercises and educational projects. The CRAN Repository is another library that includes code and documentation for data science projects and professionals which have been submitted by a community of users. In addition, GitHub is a repository or library which includes information and instructions for writing code in multiple programming languages, R included.

Statistical and Exploratory Data Analysis

One of the most important parts of data science is the understanding and analysis of the data. In order to analyze data, it is important to learn about the data through running an exploratory data analysis, in addition to other forms of statistical analysis. Exploratory data analysis is a method that is used by data science professionals in order to discover what might be going on within a dataset that is new or previously unexplored. Especially when working with big data, an exploratory analysis allows you to come up with some ideas about the type of analysis or relationships that you want to look at in the data set.

In addition to exploratory data analysis, R is known for its uses as a statistical processing tool due to the ease of use that the language has for writing statistical functions and running data analyses. Then, once you run the analyses, R is efficient at creating data visualizations and models which can be used to share important findings with others. Through dashboards and applications, R products and packages have made the process of completing any data science project more efficient and effective. So, whether you are running a simple analysis of a small dataset or a large-scale data science project, R makes statistical analysis as simple as writing a few lines of code.

Want to learn R for your next data science project or position?

As an incredibly popular programming language, learning R is especially useful for students and professionals that are interested in data science and data analysis projects. With several data science classes, Noble Desktop has several course offerings which not only focus on teaching you the basics of R but also how to use this programming language within data science. Whether you are interested in multi-week bootcamps and immersives or a two-day workshop, there is a diverse breadth of live online data science classes that teach R singularly and in conjunction with other programming languages. There are also in-person data science classes located in a city near you that focus on teaching the fundamentals of R for data science.