R has transformed how we approach data. From data visualization and analysis to machine learning and artificial intelligence, R has greatly impacted countless niches of data science. As such, plenty of data scientists, analysts, and academic professionals use the highly versatile R programming language.
A longtime member of the TIOBE index, which measures how popular different programming languages are, R is used by people of all professional backgrounds. This article will dive into R’s different uses and demonstrate what you can do with this programming language once you’ve learned it.
It’s next to impossible to understand your data without data visualization. Data visualization takes the data you’ve gathered and analyzed and turns it into easily digestible visual content such as images and graphs. These visuals allow data scientists and analysts to make sense of otherwise undetectable trends in their data.
Data visualization is essential if you intend to analyze large sets of data, and R’s ggplot2 package is one of the best data visualization tools available for use. The package allows users to map out nearly any plot of data they desire, and becoming proficient in ggplot2 is a great first step to take if you intend to harness R’s full potential.
You may also consider combining the syntax that the ggplot2 package offers with tools such as Plotly or Shiny. These will allow you to customize your data visualizations and dashboards and make them more interactive. If you have a front-end developer with whom you work, you can leverage their skills to make your visuals more impressive and easier to read. In the meantime, it may also be a good idea to explore courses that explore data visualization with ggplot2, Plotly, and Shiny.
Most projects in data science require that exploratory data analysis be conducted before anything else occurs. Exploratory data analysis covers the same steps you’d follow to characterize a dataset. Determining different types of data, locating pieces of information that are missing, and identifying distributions of values and correlations between data points all typically fall under exploratory data analysis.
When it comes to using and mastering R, understanding what your dataset consists of via exploratory data analysis is mandatory once you obtain new data. R offers a suite of packages known as “tidyverse,” which includes tidyr and dplyr, that allow users to explore, manipulate, and calculate on their datasets. The tidyverse suite of packages has a syntax that’s simple to use and makes it easy for users to obtain the answers they’re looking for quickly.
If you’re interested in exploring, manipulating, and calculating datasets with R’s tidyverse suite of packages, it’s a good idea to explore courses that dive into manipulating data with dplyr and tidyr.
If you’re aiming to learn and master the R programming language, you’ll need a way to make sure that your data analysis is scientifically rigorous. Statistics are just the thing you need to ensure that scientific rigor is present in your data analysis, and R is the language you can use to leverage multiple statistical tools.
Out of all the programming languages available for use, R has arguably the largest number of tools that focus specifically on statistics. R’s suite of tools allows you to perform various statistical functions. Whether you need to arrive at descriptive statistics such as standard deviations and means, create models that map logistic and linear regression, or experiment with A/B testing, R’s statistical capabilities are far-reaching indeed.
Interested in capitalizing on the power of statistics that R can give you? It’s recommended that you explore some courses that introduce you to statistics, linear and logistic regression, and A/B testing in R.
Machine learning is undoubtedly one of the most widely discussed topics among the data science community, and it’s easy to see why. Machine learning allows data scientists and analysts to automate how they arrive at crucial insights, recognize data patterns, and develop highly accurate predictive models. And the best part? You don’t need to use explicitly defined programming instructions to take advantage of the power of machine learning.
Within the context of using the R programming language, machine learning allows data scientists and analysts to make accurate and wide-reaching predictions. At its core, machine learning encapsulates the art of arriving at predictions with the power of data. And as with statistics, R offers its users a huge array of tools that allow them to run multiple machine learning models of their choosing.
The R Markdown package is highly popular among working professionals who frequently generate automated presentations and reports. Why is this the case? It’s mainly because R Markdown can run code written in R while a document is being rendered.
An extension of Markdown, R Markdown is a markup language that’s lightweight and easy to use. Whereas Markdown allows users to add things such as headers and lists to their documents, R Markdown goes a step further by handling the R programming language.
If you’re wondering why this is important, keep in mind that once you’ve conducted your data analysis, you’ll want to be able to report on your findings. R Markdown is exactly the package you need to structure your findings in comprehensible reports. It gives users an easy-to-use syntax that lets them create reports in multiple formats, be they written documents, presentations, or books.
Also worth noting is that with the help of R Markdown, users can fill their documents with information relevant to the complex calculations they make. You could, for instance, include in your R Markdown-generated reports the results of a questionnaire you conducted that take into account the responses of many respondents. With the help of R Markdown, you can generate a document that includes real data and is also clean and visually appealing.
The R programming language has a specialization in nearly every facet of data science, and it has completely revolutionized how data scientists and analysts approach their data. R offers many potential uses once you master it, and we hope the five surprising things you can do with R that we’ve covered incentivize you to begin your journey!
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed — among other intriguing things — to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.
