The Benefits of R Data Analysis

Common among statisticians and data scientists, R data analysis refers to the process of performing various analysis techniques using the programming language R. There are several advantages of using R. For starters, it’s available under an open-source license, which means that anyone can download and modify the code. Over the years R has received significant contributions to its code that have made it stable and reliable as a language for R data analysis. Secondly, R is free to use, which has made it a particularly attractive tool to students and researchers. And finally, R connects with other languages, ensuring that users aren’t locked into a single language or system. In sum, performing R data analysis has proven to be a great solution for a number of thorny data problems.

Data Preparation: The Roadblock to Analysis

Before analyzing, data scientists must prepare their data to ensure that it has been properly cleansed and structured. No dataset is perfect; inevitably there will be missing fields or invalid information that needs correcting.

Data preparation, too, can be done using R, though there are several challenges in doing so. The primary challenge is that it’s time-consuming—data preparation using R requires specific scripts that must be carefully written. If an error occurs, that script must be written anew. Sometimes it is time-consuming to simply search for what needs to be corrected; since R is a scripting language, it doesn’t visually surface data variations or outliers.

In place of R for data preparation, many data scientists are using intelligent data preparation platforms such as Designer Cloud that accelerate the process of preparing data so that they can spend more time on analysis. Arushi Arora, a Data Science Master’s student at Columbia University, can testify to the power of Designer Cloud. She was working with a dataset on global flood records, which had several inconsistencies—“Heavy Rain,” for example, was spelled as Heavy rainheavy rainHEAVY Rain and even Monsoonal Rain. She writes, “Rather than spending two hours to complete this task using R, with Designer Cloud, I standardized my data in ten minutes. Finally, once my data was cleaned in Designer Cloud, I used R or D3.js for visualization.” Arora was able to accelerate time spent preparing data to focus on analysis.

Using Designer Cloud with R Data Analysis

The bottom line is that while data analysis in R is widely successful, using R for data preparation is not visual or intuitive, which slows down a process that is already recognized as the most time-consuming part of any analytics project. The Designer Cloud data preparation platform presents representations of data in the most compelling visual profile, and simply selecting certain elements of the profile immediately prompts intelligent transformation suggestions. We’d love to chat with you about how we can help integrate Designer Cloud into your workflow. Schedule a demo today.